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Sample records for determine optimal models

  1. Optimal moment determination in POME-copula based hydrometeorological dependence modelling

    Science.gov (United States)

    Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

    2017-07-01

    Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

  2. An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty

    Directory of Open Access Journals (Sweden)

    Feng Zhou

    2015-11-01

    Full Text Available An indirect simulation-optimization model framework with enhanced computational efficiency and risk-based decision-making capability was developed to determine optimal total maximum daily load (TMDL allocation under uncertainty. To convert the traditional direct simulation-optimization model into our indirect equivalent model framework, we proposed a two-step strategy: (1 application of interval regression equations derived by a Bayesian recursive regression tree (BRRT v2 algorithm, which approximates the original hydrodynamic and water-quality simulation models and accurately quantifies the inherent nonlinear relationship between nutrient load reductions and the credible interval of algal biomass with a given confidence interval; and (2 incorporation of the calibrated interval regression equations into an uncertain optimization framework, which is further converted to our indirect equivalent framework by the enhanced-interval linear programming (EILP method and provides approximate-optimal solutions at various risk levels. The proposed strategy was applied to the Swift Creek Reservoir’s nutrient TMDL allocation (Chesterfield County, VA to identify the minimum nutrient load allocations required from eight sub-watersheds to ensure compliance with user-specified chlorophyll criteria. Our results indicated that the BRRT-EILP model could identify critical sub-watersheds faster than the traditional one and requires lower reduction of nutrient loadings compared to traditional stochastic simulation and trial-and-error (TAE approaches. This suggests that our proposed framework performs better in optimal TMDL development compared to the traditional simulation-optimization models and provides extreme and non-extreme tradeoff analysis under uncertainty for risk-based decision making.

  3. Method to determine the optimal constitutive model from spherical indentation tests

    Directory of Open Access Journals (Sweden)

    Tairui Zhang

    2018-03-01

    Full Text Available The limitation of current indentation theories was investigated and a method to determine the optimal constitutive model through spherical indentation tests was proposed. Two constitutive models, the Power-law and the Linear-law, were used in Finite Element (FE calculations, and then a set of indentation governing equations was established for each model. The load-depth data from the normal indentation depth was used to fit the best parameters in each constitutive model while the data from the further loading part was compared with those from FE calculations, and the model that better predicted the further deformation was considered the optimal one. Moreover, a Yang’s modulus calculation model which took the previous plastic deformation and the phenomenon of pile-up (or sink-in into consideration was also proposed to revise the original Sneddon-Pharr-Oliver model. The indentation results on six materials, 304, 321, SA508, SA533, 15CrMoR, and Fv520B, were compared with tensile ones, which validated the reliability of the revised E calculation model and the optimal constitutive model determination method in this study. Keywords: Optimal constitutive model, Spherical indentation test, Finite Element calculations, Yang’s modulus

  4. HIV Treatment and Prevention: A Simple Model to Determine Optimal Investment.

    Science.gov (United States)

    Juusola, Jessie L; Brandeau, Margaret L

    2016-04-01

    To create a simple model to help public health decision makers determine how to best invest limited resources in HIV treatment scale-up and prevention. A linear model was developed for determining the optimal mix of investment in HIV treatment and prevention, given a fixed budget. The model incorporates estimates of secondary health benefits accruing from HIV treatment and prevention and allows for diseconomies of scale in program costs and subadditive benefits from concurrent program implementation. Data sources were published literature. The target population was individuals infected with HIV or at risk of acquiring it. Illustrative examples of interventions include preexposure prophylaxis (PrEP), community-based education (CBE), and antiretroviral therapy (ART) for men who have sex with men (MSM) in the US. Outcome measures were incremental cost, quality-adjusted life-years gained, and HIV infections averted. Base case analysis indicated that it is optimal to invest in ART before PrEP and to invest in CBE before scaling up ART. Diseconomies of scale reduced the optimal investment level. Subadditivity of benefits did not affect the optimal allocation for relatively low implementation levels. The sensitivity analysis indicated that investment in ART before PrEP was optimal in all scenarios tested. Investment in ART before CBE became optimal when CBE reduced risky behavior by 4% or less. Limitations of the study are that dynamic effects are approximated with a static model. Our model provides a simple yet accurate means of determining optimal investment in HIV prevention and treatment. For MSM in the US, HIV control funds should be prioritized on inexpensive, effective programs like CBE, then on ART scale-up, with only minimal investment in PrEP. © The Author(s) 2015.

  5. Method to determine the optimal constitutive model from spherical indentation tests

    Science.gov (United States)

    Zhang, Tairui; Wang, Shang; Wang, Weiqiang

    2018-03-01

    The limitation of current indentation theories was investigated and a method to determine the optimal constitutive model through spherical indentation tests was proposed. Two constitutive models, the Power-law and the Linear-law, were used in Finite Element (FE) calculations, and then a set of indentation governing equations was established for each model. The load-depth data from the normal indentation depth was used to fit the best parameters in each constitutive model while the data from the further loading part was compared with those from FE calculations, and the model that better predicted the further deformation was considered the optimal one. Moreover, a Yang's modulus calculation model which took the previous plastic deformation and the phenomenon of pile-up (or sink-in) into consideration was also proposed to revise the original Sneddon-Pharr-Oliver model. The indentation results on six materials, 304, 321, SA508, SA533, 15CrMoR, and Fv520B, were compared with tensile ones, which validated the reliability of the revised E calculation model and the optimal constitutive model determination method in this study.

  6. Use of multilevel modeling for determining optimal parameters of heat supply systems

    Science.gov (United States)

    Stennikov, V. A.; Barakhtenko, E. A.; Sokolov, D. V.

    2017-07-01

    The problem of finding optimal parameters of a heat-supply system (HSS) is in ensuring the required throughput capacity of a heat network by determining pipeline diameters and characteristics and location of pumping stations. Effective methods for solving this problem, i.e., the method of stepwise optimization based on the concept of dynamic programming and the method of multicircuit optimization, were proposed in the context of the hydraulic circuit theory developed at Melentiev Energy Systems Institute (Siberian Branch, Russian Academy of Sciences). These methods enable us to determine optimal parameters of various types of piping systems due to flexible adaptability of the calculation procedure to intricate nonlinear mathematical models describing features of used equipment items and methods of their construction and operation. The new and most significant results achieved in developing methodological support and software for finding optimal parameters of complex heat supply systems are presented: a new procedure for solving the problem based on multilevel decomposition of a heat network model that makes it possible to proceed from the initial problem to a set of interrelated, less cumbersome subproblems with reduced dimensionality; a new algorithm implementing the method of multicircuit optimization and focused on the calculation of a hierarchical model of a heat supply system; the SOSNA software system for determining optimum parameters of intricate heat-supply systems and implementing the developed methodological foundation. The proposed procedure and algorithm enable us to solve engineering problems of finding the optimal parameters of multicircuit heat supply systems having large (real) dimensionality, and are applied in solving urgent problems related to the optimal development and reconstruction of these systems. The developed methodological foundation and software can be used for designing heat supply systems in the Central and the Admiralty regions in

  7. Using an optimal CC-PLSR-RBFNN model and NIR spectroscopy for the starch content determination in corn

    Science.gov (United States)

    Jiang, Hao; Lu, Jiangang

    2018-05-01

    Corn starch is an important material which has been traditionally used in the fields of food and chemical industry. In order to enhance the rapidness and reliability of the determination for starch content in corn, a methodology is proposed in this work, using an optimal CC-PLSR-RBFNN calibration model and near-infrared (NIR) spectroscopy. The proposed model was developed based on the optimal selection of crucial parameters and the combination of correlation coefficient method (CC), partial least squares regression (PLSR) and radial basis function neural network (RBFNN). To test the performance of the model, a standard NIR spectroscopy data set was introduced, containing spectral information and chemical reference measurements of 80 corn samples. For comparison, several other models based on the identical data set were also briefly discussed. In this process, the root mean square error of prediction (RMSEP) and coefficient of determination (Rp2) in the prediction set were used to make evaluations. As a result, the proposed model presented the best predictive performance with the smallest RMSEP (0.0497%) and the highest Rp2 (0.9968). Therefore, the proposed method combining NIR spectroscopy with the optimal CC-PLSR-RBFNN model can be helpful to determine starch content in corn.

  8. Parameters Determination of Yoshida Uemori Model Through Optimization Process of Cyclic Tension-Compression Test and V-Bending Springback

    Directory of Open Access Journals (Sweden)

    Serkan Toros

    Full Text Available Abstract In recent years, the studies on the enhancement of the prediction capability of the sheet metal forming simulations have increased remarkably. Among the used models in the finite element simulations, the yield criteria and hardening models have a great importance for the prediction of the formability and springback. The required model parameters are determined by using the several test results, i.e. tensile, compression, biaxial stretching tests (bulge test and cyclic tests (tension-compression. In this study, the Yoshida-Uemori (combined isotropic and kinematic hardening model is used to determine the performance of the springback prediction. The model parameters are determined by the optimization processes of the cyclic test by finite element simulations. However, in the study besides the cyclic tests, the model parameters are also evaluated by the optimization process of both cyclic and V-die bending simulations. The springback angle predictions with the model parameters obtained by the optimization of both cyclic and V-die bending simulations are found to mimic the experimental results in a better way than those obtained from only cyclic tests. However, the cyclic simulation results are found to be close enough to the experimental results.

  9. A Novel Scheme for Optimal Control of a Nonlinear Delay Differential Equations Model to Determine Effective and Optimal Administrating Chemotherapy Agents in Breast Cancer.

    Science.gov (United States)

    Ramezanpour, H R; Setayeshi, S; Akbari, M E

    2011-01-01

    Determining the optimal and effective scheme for administrating the chemotherapy agents in breast cancer is the main goal of this scientific research. The most important issue here is the amount of drug or radiation administrated in chemotherapy and radiotherapy for increasing patient's survival. This is because in these cases, the therapy not only kills the tumor cells, but also kills some of the healthy tissues and causes serious damages. In this paper we investigate optimal drug scheduling effect for breast cancer model which consist of nonlinear ordinary differential time-delay equations. In this paper, a mathematical model of breast cancer tumors is discussed and then optimal control theory is applied to find out the optimal drug adjustment as an input control of system. Finally we use Sensitivity Approach (SA) to solve the optimal control problem. The goal of this paper is to determine optimal and effective scheme for administering the chemotherapy agent, so that the tumor is eradicated, while the immune systems remains above a suitable level. Simulation results confirm the effectiveness of our proposed procedure. In this paper a new scheme is proposed to design a therapy protocol for chemotherapy in Breast Cancer. In contrast to traditional pulse drug delivery, a continuous process is offered and optimized, according to the optimal control theory for time-delay systems.

  10. Determining of the Optimal Device Lifetime using Mathematical Renewal Models

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    Knežo Dušan

    2016-05-01

    Full Text Available Paper deals with the operations and equipment of the machine in the process of organizing production. During operation machines require maintenance and repairs, while in case of failure or machine wears it is necessary to replace them with new ones. For the process of replacement of old machines with new ones the term renewal is used. Qualitative aspects of the renewal process observe renewal theory, which is mainly based on the theory of probability and mathematical statistics. Devices lifetimes are closely related to the renewal of the devices. Presented article is focused on mathematical deduction of mathematical renewal models and determining optimal lifetime of the devices from the aspect of expenditures on renewal process.

  11. Energy group structure determination using particle swarm optimization

    International Nuclear Information System (INIS)

    Yi, Ce; Sjoden, Glenn

    2013-01-01

    Highlights: ► Particle swarm optimization is applied to determine broad group structure. ► A graph representation of the broad group structure problem is introduced. ► The approach is tested on a fuel-pin model. - Abstract: Multi-group theory is widely applied for the energy domain discretization when solving the Linear Boltzmann Equation. To reduce the computational cost, fine group cross libraries are often down-sampled into broad group cross section libraries. Cross section data collapsing generally involves two steps: Firstly, the broad group structure has to be determined; secondly, a weighting scheme is used to evaluate the broad cross section library based on the fine group cross section data and the broad group structure. A common scheme is to average the fine group cross section weighted by the fine group flux. Cross section collapsing techniques have been intensively researched. However, most studies use a pre-determined group structure, open based on experience, to divide the neutron energy spectrum into thermal, epi-thermal, fast, etc. energy range. In this paper, a swarm intelligence algorithm, particle swarm optimization (PSO), is applied to optimize the broad group structure. A graph representation of the broad group structure determination problem is introduced. And the swarm intelligence algorithm is used to solve the graph model. The effectiveness of the approach is demonstrated using a fuel-pin model

  12. DETERMINATION ALGORITHM OF OPTIMAL GEOMETRICAL PARAMETERS FOR COMPONENTS OF FREIGHT CARS ON THE BASIS OF GENERALIZED MATHEMATICAL MODELS

    Directory of Open Access Journals (Sweden)

    O. V. Fomin

    2013-10-01

    Full Text Available Purpose. Presentation of features and example of the use of the offered determination algorithm of optimum geometrical parameters for the components of freight cars on the basis of the generalized mathematical models, which is realized using computer. Methodology. The developed approach to search for optimal geometrical parameters can be described as the determination of optimal decision of the selected set of possible variants. Findings. The presented application example of the offered algorithm proved its operation capacity and efficiency of use. Originality. The determination procedure of optimal geometrical parameters for freight car components on the basis of the generalized mathematical models was formalized in the paper. Practical value. Practical introduction of the research results for universal open cars allows one to reduce container of their design and accordingly to increase the carrying capacity almost by100 kg with the improvement of strength characteristics. Taking into account the mass of their park this will provide a considerable economic effect when producing and operating. The offered approach is oriented to the distribution of the software packages (for example Microsoft Excel, which are used by technical services of the most enterprises, and does not require additional capital investments (acquisitions of the specialized programs and proper technical staff training. This proves the correctness of the research direction. The offered algorithm can be used for the solution of other optimization tasks on the basis of the generalized mathematical models.

  13. Models and Methods for Structural Topology Optimization with Discrete Design Variables

    DEFF Research Database (Denmark)

    Stolpe, Mathias

    in the conceptual design phase to find innovative designs. The strength of topology optimization is the capability of determining both the optimal shape and the topology of the structure. In some cases also the optimal material properties can be determined. Optimal structural design problems are modeled...... such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used in the conceptual design phase to find innovative designs. The strength of topology optimization is the capability of determining both the optimal......Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used...

  14. Determining Optimal Decision Version

    Directory of Open Access Journals (Sweden)

    Olga Ioana Amariei

    2014-06-01

    Full Text Available In this paper we start from the calculation of the product cost, applying the method of calculating the cost of hour- machine (THM, on each of the three cutting machines, namely: the cutting machine with plasma, the combined cutting machine (plasma and water jet and the cutting machine with a water jet. Following the calculation of cost and taking into account the precision of manufacturing of each machine, as well as the quality of the processed surface, the optimal decisional version needs to be determined regarding the product manufacturing. To determine the optimal decisional version, we resort firstly to calculating the optimal version on each criterion, and then overall using multiattribute decision methods.

  15. Optimal Path Determination for Flying Vehicle to Search an Object

    Science.gov (United States)

    Heru Tjahjana, R.; Heri Soelistyo U, R.; Ratnasari, L.; Irawanto, B.

    2018-01-01

    In this paper, a method to determine optimal path for flying vehicle to search an object is proposed. Background of the paper is controlling air vehicle to search an object. Optimal path determination is one of the most popular problem in optimization. This paper describe model of control design for a flying vehicle to search an object, and focus on the optimal path that used to search an object. In this paper, optimal control model is used to control flying vehicle to make the vehicle move in optimal path. If the vehicle move in optimal path, then the path to reach the searched object also optimal. The cost Functional is one of the most important things in optimal control design, in this paper the cost functional make the air vehicle can move as soon as possible to reach the object. The axis reference of flying vehicle uses N-E-D (North-East-Down) coordinate system. The result of this paper are the theorems which say that the cost functional make the control optimal and make the vehicle move in optimal path are proved analytically. The other result of this paper also shows the cost functional which used is convex. The convexity of the cost functional is use for guarantee the existence of optimal control. This paper also expose some simulations to show an optimal path for flying vehicle to search an object. The optimization method which used to find the optimal control and optimal path vehicle in this paper is Pontryagin Minimum Principle.

  16. An optimization model for metabolic pathways.

    Science.gov (United States)

    Planes, F J; Beasley, J E

    2009-10-15

    Different mathematical methods have emerged in the post-genomic era to determine metabolic pathways. These methods can be divided into stoichiometric methods and path finding methods. In this paper we detail a novel optimization model, based upon integer linear programming, to determine metabolic pathways. Our model links reaction stoichiometry with path finding in a single approach. We test the ability of our model to determine 40 annotated Escherichia coli metabolic pathways. We show that our model is able to determine 36 of these 40 pathways in a computationally effective manner.

  17. Multipurpose optimization models for high level waste vitrification

    International Nuclear Information System (INIS)

    Hoza, M.

    1994-08-01

    Optimal Waste Loading (OWL) models have been developed as multipurpose tools for high-level waste studies for the Tank Waste Remediation Program at Hanford. Using nonlinear programming techniques, these models maximize the waste loading of the vitrified waste and optimize the glass formers composition such that the glass produced has the appropriate properties within the melter, and the resultant vitrified waste form meets the requirements for disposal. The OWL model can be used for a single waste stream or for blended streams. The models can determine optimal continuous blends or optimal discrete blends of a number of different wastes. The OWL models have been used to identify the most restrictive constraints, to evaluate prospective waste pretreatment methods, to formulate and evaluate blending strategies, and to determine the impacts of variability in the wastes. The OWL models will be used to aid in the design of frits and the maximize the waste in the glass for High-Level Waste (HLW) vitrification

  18. Modeling investor optimism with fuzzy connectives

    NARCIS (Netherlands)

    Lovric, M.; Almeida, R.J.; Kaymak, U.; Spronk, J.; Carvalho, J.P.; Dubois, D.; Kaymak, U.; Sousa, J.M.C.

    2009-01-01

    Optimism or pessimism of investors is one of the important characteristics that determine the investment behavior in financial markets. In this paper, we propose a model of investor optimism based on a fuzzy connective. The advantage of the proposed approach is that the influence of different levels

  19. Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

    Directory of Open Access Journals (Sweden)

    Adel Taieb

    2017-01-01

    Full Text Available This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO Fuzzy Optimal Model Predictive Control (FOMPC using the Adaptive Particle Swarm Optimization (APSO algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR and Tank system, where the proposed approach provides better performances compared with other methods.

  20. Assessing the accuracy of subject-specific, muscle-model parameters determined by optimizing to match isometric strength.

    Science.gov (United States)

    DeSmitt, Holly J; Domire, Zachary J

    2016-12-01

    Biomechanical models are sensitive to the choice of model parameters. Therefore, determination of accurate subject specific model parameters is important. One approach to generate these parameters is to optimize the values such that the model output will match experimentally measured strength curves. This approach is attractive as it is inexpensive and should provide an excellent match to experimentally measured strength. However, given the problem of muscle redundancy, it is not clear that this approach generates accurate individual muscle forces. The purpose of this investigation is to evaluate this approach using simulated data to enable a direct comparison. It is hypothesized that the optimization approach will be able to recreate accurate muscle model parameters when information from measurable parameters is given. A model of isometric knee extension was developed to simulate a strength curve across a range of knee angles. In order to realistically recreate experimentally measured strength, random noise was added to the modeled strength. Parameters were solved for using a genetic search algorithm. When noise was added to the measurements the strength curve was reasonably recreated. However, the individual muscle model parameters and force curves were far less accurate. Based upon this examination, it is clear that very different sets of model parameters can recreate similar strength curves. Therefore, experimental variation in strength measurements has a significant influence on the results. Given the difficulty in accurately recreating individual muscle parameters, it may be more appropriate to perform simulations with lumped actuators representing similar muscles.

  1. Optimization modeling with spreadsheets

    CERN Document Server

    Baker, Kenneth R

    2015-01-01

    An accessible introduction to optimization analysis using spreadsheets Updated and revised, Optimization Modeling with Spreadsheets, Third Edition emphasizes model building skills in optimization analysis. By emphasizing both spreadsheet modeling and optimization tools in the freely available Microsoft® Office Excel® Solver, the book illustrates how to find solutions to real-world optimization problems without needing additional specialized software. The Third Edition includes many practical applications of optimization models as well as a systematic framework that il

  2. Determination of the optimal area of waste incineration in a rotary kiln using a simulation model.

    Science.gov (United States)

    Bujak, J

    2015-08-01

    The article presents a mathematical model to determine the flux of incinerated waste in terms of its calorific values. The model is applicable in waste incineration systems equipped with rotary kilns. It is based on the known and proven energy flux balances and equations that describe the specific losses of energy flux while considering the specificity of waste incineration systems. The model is universal as it can be used both for the analysis and testing of systems burning different types of waste (municipal, medical, animal, etc.) and for allowing the use of any kind of additional fuel. Types of waste incinerated and additional fuel are identified by a determination of their elemental composition. The computational model has been verified in three existing industrial-scale plants. Each system incinerated a different type of waste. Each waste type was selected in terms of a different calorific value. This allowed the full verification of the model. Therefore the model can be used to optimize the operation of waste incineration system both at the design stage and during its lifetime. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS

    International Nuclear Information System (INIS)

    Rogers, Adam; Fiege, Jason D.

    2011-01-01

    Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image χ 2 and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest χ 2 is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.

  4. Review: Optimization methods for groundwater modeling and management

    Science.gov (United States)

    Yeh, William W.-G.

    2015-09-01

    Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

  5. Computer modeling for optimal placement of gloveboxes

    International Nuclear Information System (INIS)

    Hench, K.W.; Olivas, J.D.; Finch, P.R.

    1997-08-01

    Reduction of the nuclear weapons stockpile and the general downsizing of the nuclear weapons complex has presented challenges for Los Alamos. One is to design an optimized fabrication facility to manufacture nuclear weapon primary components (pits) in an environment of intense regulation and shrinking budgets. Historically, the location of gloveboxes in a processing area has been determined without benefit of industrial engineering studies to ascertain the optimal arrangement. The opportunity exists for substantial cost savings and increased process efficiency through careful study and optimization of the proposed layout by constructing a computer model of the fabrication process. This paper presents an integrative two- stage approach to modeling the casting operation for pit fabrication. The first stage uses a mathematical technique for the formulation of the facility layout problem; the solution procedure uses an evolutionary heuristic technique. The best solutions to the layout problem are used as input to the second stage - a computer simulation model that assesses the impact of competing layouts on operational performance. The focus of the simulation model is to determine the layout that minimizes personnel radiation exposures and nuclear material movement, and maximizes the utilization of capacity for finished units

  6. Computer modeling for optimal placement of gloveboxes

    Energy Technology Data Exchange (ETDEWEB)

    Hench, K.W.; Olivas, J.D. [Los Alamos National Lab., NM (United States); Finch, P.R. [New Mexico State Univ., Las Cruces, NM (United States)

    1997-08-01

    Reduction of the nuclear weapons stockpile and the general downsizing of the nuclear weapons complex has presented challenges for Los Alamos. One is to design an optimized fabrication facility to manufacture nuclear weapon primary components (pits) in an environment of intense regulation and shrinking budgets. Historically, the location of gloveboxes in a processing area has been determined without benefit of industrial engineering studies to ascertain the optimal arrangement. The opportunity exists for substantial cost savings and increased process efficiency through careful study and optimization of the proposed layout by constructing a computer model of the fabrication process. This paper presents an integrative two- stage approach to modeling the casting operation for pit fabrication. The first stage uses a mathematical technique for the formulation of the facility layout problem; the solution procedure uses an evolutionary heuristic technique. The best solutions to the layout problem are used as input to the second stage - a computer simulation model that assesses the impact of competing layouts on operational performance. The focus of the simulation model is to determine the layout that minimizes personnel radiation exposures and nuclear material movement, and maximizes the utilization of capacity for finished units.

  7. Enhanced index tracking modelling in portfolio optimization

    Science.gov (United States)

    Lam, W. S.; Hj. Jaaman, Saiful Hafizah; Ismail, Hamizun bin

    2013-09-01

    Enhanced index tracking is a popular form of passive fund management in stock market. It is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the risk. Enhanced index tracking aims to generate excess return over the return achieved by the index without purchasing all of the stocks that make up the index by establishing an optimal portfolio. The objective of this study is to determine the optimal portfolio composition and performance by using weighted model in enhanced index tracking. Weighted model focuses on the trade-off between the excess return and the risk. The results of this study show that the optimal portfolio for the weighted model is able to outperform the Malaysia market index which is Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.

  8. Portfolio optimization by using linear programing models based on genetic algorithm

    Science.gov (United States)

    Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

    2018-01-01

    In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

  9. Determining Optimal Replacement Policy with an Availability Constraint via Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Shengliang Zong

    2017-01-01

    Full Text Available We develop a model and a genetic algorithm for determining an optimal replacement policy for power equipment subject to Poisson shocks. If the time interval of two consecutive shocks is less than a threshold value, the failed equipment can be repaired. We assume that the operating time after repair is stochastically nonincreasing and the repair time is exponentially distributed with a geometric increasing mean. Our objective is to minimize the expected average cost under an availability requirement. Based on this average cost function, we propose the genetic algorithm to locate the optimal replacement policy N to minimize the average cost rate. The results show that the GA is effective and efficient in finding the optimal solutions. The availability of equipment has significance effect on the optimal replacement policy. Many practical systems fit the model developed in this paper.

  10. On the Determinants of Optimal Border Taxes for a Small Open Economy

    DEFF Research Database (Denmark)

    Munk, Knud Jørgen; Rasmussen, Bo Sandemann

    of the primary factor and domestic consumption of the export good cannot be taxed is nevertheless a constraint; this insight provides the key to understanding what determines the optimal tariff structure. The optimal border tax structure is derived for both exogenous and endogenous labour supply, and the results...... are interpreted in the spirit of the Corlett-Hague results for the optimal tax structure in a closed economy and compared with results from CGE models....

  11. A PROCEDURE FOR DETERMINING OPTIMAL FACILITY LOCATION AND SUB-OPTIMAL POSITIONS

    Directory of Open Access Journals (Sweden)

    P.K. Dan

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: This research presents a methodology for determining the optimal location of a new facility, having physical flow interaction of various degrees with other existing facilities in the presence of barriers impeding the shortest flow-path as well as the sub-optimal iso-cost positions. It also determines sub-optimal iso-cost positions with additional cost or penalty for not being able to site it at the computed optimal point. The proposed methodology considers all types of quadrilateral barrier or forbidden region configurations to generalize and by-pass such impenetrable obstacles, and adopts a scheme of searching through the vertices of the quadrilaterals to determine the alternative shortest flow-path. This procedure of obstacle avoidance is novel. Software has been developed to facilitate computations for the search algorithm to determine the optimal and iso-cost co-ordinates. The test results are presented.

    AFRIKAANSE OPSOMMING: Die navorsing behandel ‘n procedure vir die bepaling van optimum stigtingsposisie vir ‘n onderneming met vloei vanaf ander bestaande fasiliteite in die teenwoordigheid van ‘n verskeidenheid van randvoorwaardes. Die prodedure lewer as resultaat suboptimale isokoste-stigtingsplekke met bekendmaking van die koste wat onstaan a.g.v. afwyking van die randvoorwaardlose optimum oplossingskoste, die prosedure maak gebruik van ‘n vindingryke soekmetode wat toegepas word op niersydige meerkundige voorstellings vir die bepaling van korste roetes wat versperring omseil. Die prosedure word onderskei deur programmatuur. Toetsresultate word voorgehou.

  12. Human error considerations and annunciator effects in determining optimal test intervals for periodically inspected standby systems

    International Nuclear Information System (INIS)

    McWilliams, T.P.; Martz, H.F.

    1981-01-01

    This paper incorporates the effects of four types of human error in a model for determining the optimal time between periodic inspections which maximizes the steady state availability for standby safety systems. Such safety systems are characteristic of nuclear power plant operations. The system is modeled by means of an infinite state-space Markov chain. Purpose of the paper is to demonstrate techniques for computing steady-state availability A and the optimal periodic inspection interval tau* for the system. The model can be used to investigate the effects of human error probabilities on optimal availability, study the benefits of annunciating the standby-system, and to determine optimal inspection intervals. Several examples which are representative of nuclear power plant applications are presented

  13. Parameter optimization for surface flux transport models

    Science.gov (United States)

    Whitbread, T.; Yeates, A. R.; Muñoz-Jaramillo, A.; Petrie, G. J. D.

    2017-11-01

    Accurate prediction of solar activity calls for precise calibration of solar cycle models. Consequently we aim to find optimal parameters for models which describe the physical processes on the solar surface, which in turn act as proxies for what occurs in the interior and provide source terms for coronal models. We use a genetic algorithm to optimize surface flux transport models using National Solar Observatory (NSO) magnetogram data for Solar Cycle 23. This is applied to both a 1D model that inserts new magnetic flux in the form of idealized bipolar magnetic regions, and also to a 2D model that assimilates specific shapes of real active regions. The genetic algorithm searches for parameter sets (meridional flow speed and profile, supergranular diffusivity, initial magnetic field, and radial decay time) that produce the best fit between observed and simulated butterfly diagrams, weighted by a latitude-dependent error structure which reflects uncertainty in observations. Due to the easily adaptable nature of the 2D model, the optimization process is repeated for Cycles 21, 22, and 24 in order to analyse cycle-to-cycle variation of the optimal solution. We find that the ranges and optimal solutions for the various regimes are in reasonable agreement with results from the literature, both theoretical and observational. The optimal meridional flow profiles for each regime are almost entirely within observational bounds determined by magnetic feature tracking, with the 2D model being able to accommodate the mean observed profile more successfully. Differences between models appear to be important in deciding values for the diffusive and decay terms. In like fashion, differences in the behaviours of different solar cycles lead to contrasts in parameters defining the meridional flow and initial field strength.

  14. A multidimensional model of optimal participation of children with physical disabilities.

    Science.gov (United States)

    Kang, Lin-Ju; Palisano, Robert J; King, Gillian A; Chiarello, Lisa A

    2014-01-01

    To present a conceptual model of optimal participation in recreational and leisure activities for children with physical disabilities. The conceptualization of the model was based on review of contemporary theories and frameworks, empirical research and the authors' practice knowledge. A case scenario is used to illustrate application to practice. The model proposes that optimal participation in recreational and leisure activities involves the dynamic interaction of multiple dimensions and determinants of participation. The three dimensions of participation are physical, social and self-engagement. Determinants of participation encompass attributes of the child, family and environment. Experiences of optimal participation are hypothesized to result in long-term benefits including better quality of life, a healthier lifestyle and emotional and psychosocial well-being. Consideration of relevant child, family and environment determinants of dimensions of optimal participation should assist children, families and health care professionals to identify meaningful goals and outcomes and guide the selection and implementation of innovative therapy approaches and methods of service delivery. Implications for Rehabilitation Optimal participation is proposed to involve the dynamic interaction of physical, social and self-engagement and attributes of the child, family and environment. The model emphasizes the importance of self-perceptions and participation experiences of children with physical disabilities. Optimal participation may have a positive influence on quality of life, a healthy lifestyle and emotional and psychosocial well-being. Knowledge of child, family, and environment determinants of physical, social and self-engagement should assist children, families and professionals in identifying meaningful goals and guiding innovative therapy approaches.

  15. Model averaging, optimal inference and habit formation

    Directory of Open Access Journals (Sweden)

    Thomas H B FitzGerald

    2014-06-01

    Full Text Available Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour.

  16. Model for determining and optimizing delivery performance in industrial systems

    Directory of Open Access Journals (Sweden)

    Fechete Flavia

    2017-01-01

    Full Text Available Performance means achieving organizational objectives regardless of their nature and variety, and even overcoming them. Improving performance is one of the major goals of any company. Achieving the global performance means not only obtaining the economic performance, it is a must to take into account other functions like: function of quality, delivery, costs and even the employees satisfaction. This paper aims to improve the delivery performance of an industrial system due to their very low results. The delivery performance took into account all categories of performance indicators, such as on time delivery, backlog efficiency or transport efficiency. The research was focused on optimizing the delivery performance of the industrial system, using linear programming. Modeling the delivery function using linear programming led to obtaining precise quantities to be produced and delivered each month by the industrial system in order to minimize their transport cost, satisfying their customers orders and to control their stock. The optimization led to a substantial improvement in all four performance indicators that concern deliveries.

  17. Optimization of determination of 126Sn by ion exchange chromatography method (presentation)

    International Nuclear Information System (INIS)

    Pasteka, L.; Dulanska, S.

    2013-01-01

    The aim of the work is to optimize the uptake of tin on anion exchange resins and application of this knowledge for the analysis of samples of radioactive waste from the device of Jaslovske Bohunice and Mochovce in determining of 126 Sn. First to be optimized a method for the separation of tin on ion exchange sorbent Anion Exchange Resin (1-X8, Chloride Form) from Eichrom Technologies. Model sample was prepared in 7 mol dm -3 HCl, because in that environment a sorbent effectively captures the tin, which is bounded complexly with chloride anions as SnCl 6 2- . The radiochemical separation yield was monitored by gamma spectrometric measurements on high purity germanium detector HPGe (E = 391 keV) by adding isotope 113 Sn to each model solution. The method of tin separation was optimized on model samples.

  18. Modeling and optimization of potable water network

    Energy Technology Data Exchange (ETDEWEB)

    Djebedjian, B.; Rayan, M.A. [Mansoura Univ., El-Mansoura (Egypt); Herrick, A. [Suez Canal Authority, Ismailia (Egypt)

    2000-07-01

    Software was developed in order to optimize the design of water distribution systems and pipe networks. While satisfying all the constraints imposed such as pipe diameter and nodal pressure, it was based on a mathematical model treating looped networks. The optimum network configuration and cost are determined considering parameters like pipe diameter, flow rate, corresponding pressure and hydraulic losses. It must be understood that minimum cost is relative to the different objective functions selected. The determination of the proper objective function often depends on the operating policies of a particular company. The solution for the optimization technique was obtained by using a non-linear technique. To solve the optimal design of network, the model was derived using the sequential unconstrained minimization technique (SUMT) of Fiacco and McCormick, which decreased the number of iterations required. The pipe diameters initially assumed were successively adjusted to correspond to the existing commercial pipe diameters. The technique was then applied to a two-loop network without pumps or valves. Fed by gravity, it comprised eight pipes, 1000 m long each. The first evaluation of the method proved satisfactory. As with other methods, it failed to find the global optimum. In the future, research efforts will be directed to the optimization of networks with pumps and reservoirs. 24 refs., 3 tabs., 1 fig.

  19. Determining an optimal supply chain strategy

    Directory of Open Access Journals (Sweden)

    Intaher M. Ambe

    2012-11-01

    Full Text Available In today’s business environment, many companies want to become efficient and flexible, but have struggled, in part, because they have not been able to formulate optimal supply chain strategies. Often this is as a result of insufficient knowledge about the costs involved in maintaining supply chains and the impact of the supply chain on their operations. Hence, these companies find it difficult to manufacture at a competitive cost and respond quickly and reliably to market demand. Mismatched strategies are the root cause of the problems that plague supply chains, and supply-chain strategies based on a one-size-fits-all strategy often fail. The purpose of this article is to suggest instruments to determine an optimal supply chain strategy. This article, which is conceptual in nature, provides a review of current supply chain strategies and suggests a framework for determining an optimal strategy.

  20. Model for determining the completion and production policy in oil wells

    Energy Technology Data Exchange (ETDEWEB)

    Acurero S, L A

    1983-12-01

    An optimization scheme for reservoir development was examined considering the value of the resource, choice of completion and production techniques, and boundary conditions for the reservoir. A 3-phase semi-analytic single-well model was formulated to determine the reservoir response for any completion and production policy. Second, an optimization scheme based on the discrete version of the maximum principle of Pontryagin and the Fibonacci search method was formulated to determine the optimal production and completion policy. Both models are combined in a general algorithm of solution proposed to solve the optimization problem, and a computer code was developed and tested.

  1. Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

    KAUST Repository

    Reyes, Juan Carlos De los

    2013-11-01

    We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

  2. Image denoising: Learning the noise model via nonsmooth PDE-constrained optimization

    KAUST Repository

    Reyes, Juan Carlos De los; Schö nlieb, Carola-Bibiane

    2013-01-01

    We propose a nonsmooth PDE-constrained optimization approach for the determination of the correct noise model in total variation (TV) image denoising. An optimization problem for the determination of the weights corresponding to different types of noise distributions is stated and existence of an optimal solution is proved. A tailored regularization approach for the approximation of the optimal parameter values is proposed thereafter and its consistency studied. Additionally, the differentiability of the solution operator is proved and an optimality system characterizing the optimal solutions of each regularized problem is derived. The optimal parameter values are numerically computed by using a quasi-Newton method, together with semismooth Newton type algorithms for the solution of the TV-subproblems. © 2013 American Institute of Mathematical Sciences.

  3. Determination of optimal self-drive tourism route using the orienteering problem method

    Science.gov (United States)

    Hashim, Zakiah; Ismail, Wan Rosmanira; Ahmad, Norfaieqah

    2013-04-01

    This paper was conducted to determine the optimal travel routes for self-drive tourism based on the allocation of time and expense by maximizing the amount of attraction scores assigned to each city involved. Self-drive tourism represents a type of tourism where tourists hire or travel by their own vehicle. It only involves a tourist destination which can be linked with a network of roads. Normally, the traveling salesman problem (TSP) and multiple traveling salesman problems (MTSP) method were used in the minimization problem such as determination the shortest time or distance traveled. This paper involved an alternative approach for maximization method which is maximize the attraction scores and tested on tourism data for ten cities in Kedah. A set of priority scores are used to set the attraction score at each city. The classical approach of the orienteering problem was used to determine the optimal travel route. This approach is extended to the team orienteering problem and the two methods were compared. These two models have been solved by using LINGO12.0 software. The results indicate that the model involving the team orienteering problem provides a more appropriate solution compared to the orienteering problem model.

  4. Model-Based Optimization of Velocity Strategy for Lightweight Electric Racing Cars

    Directory of Open Access Journals (Sweden)

    Mirosław Targosz

    2018-01-01

    Full Text Available The article presents a method for optimizing driving strategies aimed at minimizing energy consumption while driving. The method was developed for the needs of an electric powered racing vehicle built for the purposes of the Shell Eco-marathon (SEM, the most famous and largest race of energy efficient vehicles. Model-based optimization was used to determine the driving strategy. The numerical model was elaborated in Simulink environment, which includes both the electric vehicle model and the environment, i.e., the race track as well as the vehicle environment and the atmospheric conditions. The vehicle model itself includes vehicle dynamic model, numerical model describing issues concerning resistance of rolling tire, resistance of the propulsion system, aerodynamic phenomena, model of the electric motor, and control system. For the purpose of identifying design and functional features of individual subassemblies and components, numerical and stand tests were carried out. The model itself was tested on the research tracks to tune the model and determine the calculation parameters. The evolutionary algorithms, which are available in the MATLAB Global Optimization Toolbox, were used for optimization. In the race conditions, the model was verified during SEM races in Rotterdam where the race vehicle scored the result consistent with the results of simulation calculations. In the following years, the experience gathered by the team gave us the vice Championship in the SEM 2016 in London.

  5. Simplified ejector model for control and optimization

    International Nuclear Information System (INIS)

    Zhu Yinhai; Cai Wenjian; Wen Changyun; Li Yanzhong

    2008-01-01

    In this paper, a simple yet effective ejector model for a real time control and optimization of an ejector system is proposed. Firstly, a fundamental model for calculation of ejector entrainment ratio at critical working conditions is derived by one-dimensional analysis and the shock circle model. Then, based on thermodynamic principles and the lumped parameter method, the fundamental ejector model is simplified to result in a hybrid ejector model. The model is very simple, which only requires two or three parameters and measurement of two variables to determine the ejector performance. Furthermore, the procedures for on line identification of the model parameters using linear and non-linear least squares methods are also presented. Compared with existing ejector models, the solution of the proposed model is much easier without coupled equations and iterative computations. Finally, the effectiveness of the proposed model is validated by published experimental data. Results show that the model is accurate and robust and gives a better match to the real performances of ejectors over the entire operating range than the existing models. This model is expected to have wide applications in real time control and optimization of ejector systems

  6. Comparison of adsorption equilibrium and kinetic models for a case study of pharmaceutical active ingredient adsorption from fermentation broths: parameter determination, simulation, sensitivity analysis and optimization

    Directory of Open Access Journals (Sweden)

    B. Likozar

    2012-09-01

    Full Text Available Mathematical models for a batch process were developed to predict concentration distributions for an active ingredient (vancomycin adsorption on a representative hydrophobic-molecule adsorbent, using differently diluted crude fermentation broth with cells as the feedstock. The kinetic parameters were estimated using the maximization of the coefficient of determination by a heuristic algorithm. The parameters were estimated for each fermentation broth concentration using four concentration distributions at initial vancomycin concentrations of 4.96, 1.17, 2.78, and 5.54 g l−¹. In sequence, the models and their parameters were validated for fermentation broth concentrations of 0, 20, 50, and 100% (v/v by calculating the coefficient of determination for each concentration distribution at the corresponding initial concentration. The applicability of the validated models for process optimization was investigated by using the models as process simulators to optimize the two process efficiencies.

  7. Modelling, simulating and optimizing boiler heating surfaces and evaporator circuits

    DEFF Research Database (Denmark)

    Sørensen, K.; Condra, T.; Houbak, Niels

    2003-01-01

    A model for optimizing the dynamic performance of boiler have been developed. Design variables related to the size of the boiler and its dynamic performance have been defined. The object function to be optimized takes the weight of the boiler and its dynamic capability into account. As constraints...... for the optimization a dynamic model for the boiler is applied. Furthermore a function for the value of the dynamic performance is included in the model. The dynamic models for simulating boiler performance consists of a model for the flue gas side, a model for the evaporator circuit and a model for the drum....... The dynamic model has been developed for the purpose of determining boiler material temperatures and heat transfer from the flue gas side to the water-/steam side in order to simulate the circulation in the evaporator circuit and hereby the water level fluctuations in the drum. The dynamic model has been...

  8. Risk modelling in portfolio optimization

    Science.gov (United States)

    Lam, W. H.; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi

    2013-09-01

    Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio.

  9. A Method for Determining Optimal Residential Energy Efficiency Packages

    Energy Technology Data Exchange (ETDEWEB)

    Polly, B. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gestwick, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Bianchi, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Anderson, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Horowitz, S. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Christensen, C. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Judkoff, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2011-04-01

    This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location.

  10. A model based on stochastic dynamic programming for determining China's optimal strategic petroleum reserve policy

    International Nuclear Information System (INIS)

    Zhang Xiaobing; Fan Ying; Wei Yiming

    2009-01-01

    China's Strategic Petroleum Reserve (SPR) is currently being prepared. But how large the optimal stockpile size for China should be, what the best acquisition strategies are, how to release the reserve if a disruption occurs, and other related issues still need to be studied in detail. In this paper, we develop a stochastic dynamic programming model based on a total potential cost function of establishing SPRs to evaluate the optimal SPR policy for China. Using this model, empirical results are presented for the optimal size of China's SPR and the best acquisition and drawdown strategies for a few specific cases. The results show that with comprehensive consideration, the optimal SPR size for China is around 320 million barrels. This size is equivalent to about 90 days of net oil import amount in 2006 and should be reached in the year 2017, three years earlier than the national goal, which implies that the need for China to fill the SPR is probably more pressing; the best stockpile release action in a disruption is related to the disruption levels and expected continuation probabilities. The information provided by the results will be useful for decision makers.

  11. Simplified model-based optimal control of VAV air-conditioning system

    Energy Technology Data Exchange (ETDEWEB)

    Nassif, N.; Kajl, S.; Sabourin, R. [Ecole de Technologie Superieure, Montreal, PQ (Canada). Dept. of Construction Engineering

    2005-07-01

    The improvement of Variable Air Volume (VAV) system performance is one of several attempts being made to minimize the high energy use associated with the operation of heating, ventilation and air conditioning (HVAC) systems. A Simplified Optimization Process (SOP) comprised of controller set point strategies and a simplified VAV model was presented in this paper. The aim of the SOP was to determine supply set points. The advantage of the SOP over previous methods was that it did not require a detailed VAV model and optimization program. In addition, the monitored data for representative local-loop control can be checked on-line, after which controller set points can be updated in order to ensure proper operation by opting for real situations with minimum energy use. The SOP was validated using existing monitoring data and a model of an existing VAV system. Energy use simulations were compared to that of the existing VAV system. At each simulation step, 3 controller set point values were proposed and studied using the VAV model in order to select a value for each point which corresponded to the best performance of the VAV system. Simplified VAV component models were presented. Strategies for controller set points were described, including zone air temperature, duct static pressure set points; chilled water supply set points and supply air temperature set points. Simplified optimization process calculations were presented. Results indicated that the SOP provided significant energy savings when applied to specific AHU systems. In a comparison with a Detailed Optimization Process (DOP), the SOP was capable of determining set points close to those obtained by the DOP. However, it was noted that the controller set points determined by the SOP need a certain amount of time to reach optimal values when outdoor conditions or thermal loads are significantly changed. It was suggested that this disadvantage could be overcome by the use of a dynamic incremental value, which

  12. Determination of the Prosumer's Optimal Bids

    Science.gov (United States)

    Ferruzzi, Gabriella; Rossi, Federico; Russo, Angela

    2015-12-01

    This paper considers a microgrid connected with a medium-voltage (MV) distribution network. It is assumed that the microgrid, which is managed by a prosumer, operates in a competitive environment and participates in the day-ahead market. Then, as the first step of the short-term management problem, the prosumer must determine the bids to be submitted to the market. The offer strategy is based on the application of an optimization model, which is solved for different hourly price profiles of energy exchanged with the main grid. The proposed procedure is applied to a microgrid and four different its configurations were analyzed. The configurations consider the presence of thermoelectric units that only produce electricity, a boiler or/and cogeneration power plants for the thermal loads, and an electric storage system. The numerical results confirmed the numerous theoretical considerations that have been made.

  13. Determination of optimal reformer temperature in a reformed methanol fuel cell system using ANFIS models and numerical optimization methods

    DEFF Research Database (Denmark)

    Justesen, Kristian Kjær; Andreasen, Søren Juhl

    2015-01-01

    In this work a method for choosing the optimal reformer temperature for a reformed methanol fuel cell system is presented based on a case study of a H3 350 module produced by Serenergy A/S. The method is based on ANFIS models of the dependence of the reformer output gas composition on the reformer...... temperature and fuel flow, and the dependence of the fuel cell voltage on the fuel cell temperature, current and anode supply gas CO content. These models are combined to give a matrix of system efficiencies at different fuel cell currents and reformer temperatures. This matrix is then used to find...... the reformer temperature which gives the highest efficiency for each fuel cell current. The average of this optimal efficiency curve is 32.11% and the average efficiency achieved using the standard constant temperature is 30.64% an increase of 1.47 percentage points. The gain in efficiency is 4 percentage...

  14. Optimal foraging in marine ecosystem models: selectivity, profitability and switching

    DEFF Research Database (Denmark)

    Visser, Andre W.; Fiksen, Ø.

    2013-01-01

    ecological mechanics and evolutionary logic as a solution to diet selection in ecosystem models. When a predator can consume a range of prey items it has to choose which foraging mode to use, which prey to ignore and which ones to pursue, and animals are known to be particularly skilled in adapting...... to the preference functions commonly used in models today. Indeed, depending on prey class resolution, optimal foraging can yield feeding rates that are considerably different from the ‘switching functions’ often applied in marine ecosystem models. Dietary inclusion is dictated by two optimality choices: 1...... by letting predators maximize energy intake or more properly, some measure of fitness where predation risk and cost are also included. An optimal foraging or fitness maximizing approach will give marine ecosystem models a sound principle to determine trophic interactions...

  15. Han's model parameters for microalgae grown under intermittent illumination: Determined using particle swarm optimization.

    Science.gov (United States)

    Pozzobon, Victor; Perre, Patrick

    2018-01-21

    This work provides a model and the associated set of parameters allowing for microalgae population growth computation under intermittent lightning. Han's model is coupled with a simple microalgae growth model to yield a relationship between illumination and population growth. The model parameters were obtained by fitting a dataset available in literature using Particle Swarm Optimization method. In their work, authors grew microalgae in excess of nutrients under flashing conditions. Light/dark cycles used for these experimentations are quite close to those found in photobioreactor, i.e. ranging from several seconds to one minute. In this work, in addition to producing the set of parameters, Particle Swarm Optimization robustness was assessed. To do so, two different swarm initialization techniques were used, i.e. uniform and random distribution throughout the search-space. Both yielded the same results. In addition, swarm distribution analysis reveals that the swarm converges to a unique minimum. Thus, the produced set of parameters can be trustfully used to link light intensity to population growth rate. Furthermore, the set is capable to describe photodamages effects on population growth. Hence, accounting for light overexposure effect on algal growth. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. A system-level cost-of-energy wind farm layout optimization with landowner modeling

    International Nuclear Information System (INIS)

    Chen, Le; MacDonald, Erin

    2014-01-01

    Highlights: • We model the role of landowners in determining the success of wind projects. • A cost-of-energy (COE) model with realistic landowner remittances is developed. • These models are included in a system-level wind farm layout optimization. • Basic verification indicates the optimal COE is in-line with real-world data. • Land plots crucial to a project’s success can be identified with the approach. - Abstract: This work applies an enhanced levelized wind farm cost model, including landowner remittance fees, to determine optimal turbine placements under three landowner participation scenarios and two land-plot shapes. Instead of assuming a continuous piece of land is available for the wind farm construction, as in most layout optimizations, the problem formulation represents landowner participation scenarios as a binary string variable, along with the number of turbines. The cost parameters and model are a combination of models from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory, and Windustry. The system-level cost-of-energy (COE) optimization model is also tested under two land-plot shapes: equally-sized square land plots and unequal rectangle land plots. The optimal COEs results are compared to actual COE data and found to be realistic. The results show that landowner remittances account for approximately 10% of farm operating costs across all cases. Irregular land-plot shapes are easily handled by the model. We find that larger land plots do not necessarily receive higher remittance fees. The model can help site developers identify the most crucial land plots for project success and the optimal positions of turbines, with realistic estimates of costs and profitability

  17. Subthreshold SPICE Model Optimization

    Science.gov (United States)

    Lum, Gregory; Au, Henry; Neff, Joseph; Bozeman, Eric; Kamin, Nick; Shimabukuro, Randy

    2011-04-01

    The first step in integrated circuit design is the simulation of said design in software to verify proper functionally and design requirements. Properties of the process are provided by fabrication foundries in the form of SPICE models. These SPICE models contain the electrical data and physical properties of the basic circuit elements. A limitation of these models is that the data collected by the foundry only accurately model the saturation region. This is fine for most users, but when operating devices in the subthreshold region they are inadequate for accurate simulation results. This is why optimizing the current SPICE models to characterize the subthreshold region is so important. In order to accurately simulate this region of operation, MOSFETs of varying widths and lengths are fabricated and the electrical test data is collected. From the data collected the parameters of the model files are optimized through parameter extraction rather than curve fitting. With the completed optimized models the circuit designer is able to simulate circuit designs for the sub threshold region accurately.

  18. Optimized computational method for determining the beta dose distribution using a multiple-element thermoluminescent dosimeter system

    International Nuclear Information System (INIS)

    Shen, L.; Levine, S.H.; Catchen, G.L.

    1987-01-01

    This paper describes an optimization method for determining the beta dose distribution in tissue, and it describes the associated testing and verification. The method uses electron transport theory and optimization techniques to analyze the responses of a three-element thermoluminescent dosimeter (TLD) system. Specifically, the method determines the effective beta energy distribution incident on the dosimeter system, and thus the system performs as a beta spectrometer. Electron transport theory provides the mathematical model for performing the optimization calculation. In this calculation, parameters are determined that produce calculated doses for each of the chip/absorber components in the three-element TLD system. The resulting optimized parameters describe an effective incident beta distribution. This method can be used to determine the beta dose specifically at 7 mg X cm-2 or at any depth of interest. The doses at 7 mg X cm-2 in tissue determined by this method are compared to those experimentally determined using an extrapolation chamber. For a great variety of pure beta sources having different incident beta energy distributions, good agreement is found. The results are also compared to those produced by a commonly used empirical algorithm. Although the optimization method produces somewhat better results, the advantage of the optimization method is that its performance is not sensitive to the specific method of calibration

  19. Optimal Tax Reduction by Depreciation : A Stochastic Model

    NARCIS (Netherlands)

    Berg, M.; De Waegenaere, A.M.B.; Wielhouwer, J.L.

    1996-01-01

    This paper focuses on the choice of a depreciation method, when trying to minimize the expected value of the present value of future tax payments.In a quite general model that allows for stochastic future cash- ows and a tax structure with tax brackets, we determine the optimal choice between the

  20. Optimal control in a model of malaria with differential susceptibility

    Science.gov (United States)

    Hincapié, Doracelly; Ospina, Juan

    2014-06-01

    A malaria model with differential susceptibility is analyzed using the optimal control technique. In the model the human population is classified as susceptible, infected and recovered. Susceptibility is assumed dependent on genetic, physiological, or social characteristics that vary between individuals. The model is described by a system of differential equations that relate the human and vector populations, so that the infection is transmitted to humans by vectors, and the infection is transmitted to vectors by humans. The model considered is analyzed using the optimal control method when the control consists in using of insecticide-treated nets and educational campaigns; and the optimality criterion is to minimize the number of infected humans, while keeping the cost as low as is possible. One first goal is to determine the effects of differential susceptibility in the proposed control mechanism; and the second goal is to determine the algebraic form of the basic reproductive number of the model. All computations are performed using computer algebra, specifically Maple. It is claimed that the analytical results obtained are important for the design and implementation of control measures for malaria. It is suggested some future investigations such as the application of the method to other vector-borne diseases such as dengue or yellow fever; and also it is suggested the possible application of free software of computer algebra like Maxima.

  1. Determination of optimal electrode positions for transcranial direct current stimulation (tDCS)

    International Nuclear Information System (INIS)

    Im, Chang-Hwan; Jung, Hui-Hun; Choi, Jung-Do; Lee, Soo Yeol; Jung, Ki-Young

    2008-01-01

    The present study introduces a new approach to determining optimal electrode positions in transcranial direct current stimulation (tDCS). Electric field and 3D conduction current density were analyzed using 3D finite element method (FEM) formulated for a dc conduction problem. The electrode positions for minimal current injection were optimized by changing the Cartesian coordinate system into the spherical coordinate system and applying the (2+6) evolution strategy (ES) algorithm. Preliminary simulation studies applied to a standard three-layer head model demonstrated that the proposed approach is promising in enhancing the performance of tDCS. (note)

  2. Determination of optimal electrode positions for transcranial direct current stimulation (tDCS)

    Energy Technology Data Exchange (ETDEWEB)

    Im, Chang-Hwan; Jung, Hui-Hun; Choi, Jung-Do [Department of Biomedical Engineering, Yonsei University, Wonju, 220-710 (Korea, Republic of); Lee, Soo Yeol [Department of Biomedical Engineering, Kyung Hee University, Suwon (Korea, Republic of); Jung, Ki-Young [Korea University Medical Center, Korea University College of Medicine, Seoul (Korea, Republic of)], E-mail: ich@yonsei.ac.kr

    2008-06-07

    The present study introduces a new approach to determining optimal electrode positions in transcranial direct current stimulation (tDCS). Electric field and 3D conduction current density were analyzed using 3D finite element method (FEM) formulated for a dc conduction problem. The electrode positions for minimal current injection were optimized by changing the Cartesian coordinate system into the spherical coordinate system and applying the (2+6) evolution strategy (ES) algorithm. Preliminary simulation studies applied to a standard three-layer head model demonstrated that the proposed approach is promising in enhancing the performance of tDCS. (note)

  3. An Optimization Waste Load Allocation Model in River Systems

    Science.gov (United States)

    Amirpoor Daylami, A.; jarihani, A. A.; Aminisola, K.

    2012-04-01

    In many river systems, increasing of the waste discharge leads to increasing pollution of these water bodies. While the capacity of the river flow for pollution acceptance is limited and the ability of river to clean itself is restricted, the dischargers have to release their waste into the river after a primary pollution treatment process. Waste Load Allocation as a well-known water quality control strategy is used to determine the optimal pollutant removal at a number of point sources along the river. This paper aim at developing a new approach for treatment and management of wastewater inputs into the river systems, such that water quality standards in these receiving waters are met. In this study, inspired by the fact that cooperation among some single point source waste dischargers can lead to a more waste acceptance capacity and/or more optimum quality control in a river, an efficient approach was implemented to determine both primary waste water treatment levels and/or the best releasing points of the waste into the river. In this methodology, a genetic algorithm is used as an optimization tool to calculate optimal fraction removal levels of each one of single or shared discharger. Besides, a sub-model embedded to optimization model was used to simulate water quality of the river in each one of discharging scenarios based on the modified Streeter and Phelps quality equations. The practical application of the model is illustrated with a case study of the Gharesoo river system in west of Iran.

  4. Optimization in engineering models and algorithms

    CERN Document Server

    Sioshansi, Ramteen

    2017-01-01

    This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...

  5. Algorithm for selection of optimized EPR distance restraints for de novo protein structure determination

    Science.gov (United States)

    Kazmier, Kelli; Alexander, Nathan S.; Meiler, Jens; Mchaourab, Hassane S.

    2010-01-01

    A hybrid protein structure determination approach combining sparse Electron Paramagnetic Resonance (EPR) distance restraints and Rosetta de novo protein folding has been previously demonstrated to yield high quality models (Alexander et al., 2008). However, widespread application of this methodology to proteins of unknown structures is hindered by the lack of a general strategy to place spin label pairs in the primary sequence. In this work, we report the development of an algorithm that optimally selects spin labeling positions for the purpose of distance measurements by EPR. For the α-helical subdomain of T4 lysozyme (T4L), simulated restraints that maximize sequence separation between the two spin labels while simultaneously ensuring pairwise connectivity of secondary structure elements yielded vastly improved models by Rosetta folding. 50% of all these models have the correct fold compared to only 21% and 8% correctly folded models when randomly placed restraints or no restraints are used, respectively. Moreover, the improvements in model quality require a limited number of optimized restraints, the number of which is determined by the pairwise connectivities of T4L α-helices. The predicted improvement in Rosetta model quality was verified by experimental determination of distances between spin labels pairs selected by the algorithm. Overall, our results reinforce the rationale for the combined use of sparse EPR distance restraints and de novo folding. By alleviating the experimental bottleneck associated with restraint selection, this algorithm sets the stage for extending computational structure determination to larger, traditionally elusive protein topologies of critical structural and biochemical importance. PMID:21074624

  6. Optimized numerical annular flow dryout model using the drift-flux model in tube geometry

    International Nuclear Information System (INIS)

    Chun, Ji Han; Lee, Un Chul

    2008-01-01

    Many experimental analyses for annular film dryouts, which is one of the Critical Heat Flux (CHF) mechanisms, have been performed because of their importance. Numerical approaches must also be developed in order to assess the results from experiments and to perform pre-tests before experiments. Various thermal-hydraulic codes, such as RELAP, COBRATF, MARS, etc., have been used in the assessment of the results of dryout experiments and in experimental pre-tests. These thermal-hydraulic codes are general tools intended for the analysis of various phenomena that could appear in nuclear power plants, and many models applying these codes are unnecessarily complex for the focused analysis of dryout phenomena alone. In this study, a numerical model was developed for annular film dryout using the drift-flux model from uniform heated tube geometry. Several candidates of models that strongly affect dryout, such as the entrainment model, deposition model, and the criterion for the dryout point model, were tested as candidates for inclusion in an optimized annular film dryout model. The optimized model was developed by adopting the best combination of these candidate models, as determined through comparison with experimental data. This optimized model showed reasonable results, which were better than those of MARS code

  7. Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization.

    Directory of Open Access Journals (Sweden)

    Devaraj Jayachandran

    Full Text Available 6-Mercaptopurine (6-MP is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN through enzymatic reaction involving thiopurine methyltransferase (TPMT. Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.

  8. Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

    Science.gov (United States)

    Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami

    2015-01-01

    6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP’s widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient’s ability to metabolize the drug instead of the traditional standard-dose-for-all approach. PMID:26226448

  9. Portfolio optimization with mean-variance model

    Science.gov (United States)

    Hoe, Lam Weng; Siew, Lam Weng

    2016-06-01

    Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.

  10. Determination of radial profile of ICF hot spot's state by multi-objective parameters optimization

    International Nuclear Information System (INIS)

    Dong Jianjun; Deng Bo; Cao Zhurong; Ding Yongkun; Jiang Shaoen

    2014-01-01

    A method using multi-objective parameters optimization is presented to determine the radial profile of hot spot temperature and density. And a parameter space which contain five variables: the temperatures at center and the interface of fuel and remain ablator, the maximum model density of remain ablator, the mass ratio of remain ablator to initial ablator and the position of interface between fuel and the remain ablator, is used to described the hot spot radial temperature and density. Two objective functions are set as the variances of normalized intensity profile from experiment X-ray images and the theory calculation. Another objective function is set as the variance of experiment average temperature of hot spot and the average temperature calculated by theoretical model. The optimized parameters are obtained by multi-objective genetic algorithm searching for the five dimension parameter space, thereby the optimized radial temperature and density profiles can be determined. The radial temperature and density profiles of hot spot by experiment data measured by KB microscope cooperating with X-ray film are presented. It is observed that the temperature profile is strongly correlated to the objective functions. (authors)

  11. Optimal Patent Life in a Variety-Expansion Growth Model

    OpenAIRE

    Lin, Hwan C.

    2013-01-01

    This paper presents more channels through which the optimal patent life is determined in a R&D-based endogenous growth model that permits growth of new varieties of consumer goods over time. Its modeling features include an endogenous hazard rate facing incumbent monopolists, the prevalence of research congestion, and the aggregate welfare importance of product differentiation. As a result, a patent’s effective life is endogenized and less than its legal life. The model is calibrated to a glo...

  12. A model for an economically optimal replacement of a breeder flock

    NARCIS (Netherlands)

    Yassin, H.; Velthuis, A.G.J.; Giesen, G.W.J.; Oude Lansink, A.G.J.M.

    2012-01-01

    A deterministic model is developed to support the tactical and operational replacement decisions at broiler breeder farms. The marginal net revenue approach is applied to determine the optimal replacement age of a flock. The objective function of the model maximizes the annual gross margin over the

  13. Venturi scrubber modelling and optimization

    Energy Technology Data Exchange (ETDEWEB)

    Viswanathan, S [National Univ., La Jolla, CA (United States). School of Engineering and Technology; Ananthanarayanan, N.V. [National Univ. of Singapore (Singapore). Dept. of Chemical and Environmental Engineering; Azzopardi, B.J. [Nottingham Univ., Nottingham (United Kingdom). Dept. of Chemical Engineering

    2005-04-01

    This study presented a method to maintain the efficiency of venturi scrubbers in removing fine particulates during gas clean operations while minimizing pressure drop. Venturi scrubbers meet stringent emission standards. In order to choose the optimal method for predicting pressure drop, 4 established models were compared for their accuracy of prediction and simplicity in application. The enhanced algorithm optimizes Pease-Anthony type venturi scrubber performance by predicting the minimum pressure drop required to achieve the desired collection efficiency. This was accomplished by optimizing the key operating and design parameters such as liquid-to-gas ratio, throat gas velocity, number of nozzles, nozzle diameter and throat aspect ratio. Two of the 4 established models were expanded by providing an empirical algorithm to better predict pressure drop in the venturi throat. Model results were validated with experimental data. The optimization algorithm considers the non-uniformity in liquid distribution. It can be applied to cylindrical and rectangular Pease-Anthony type scrubbers. It offers an effective, systematic and accurate method to optimize the performance of new and existing scrubbers. 54 refs., 5 figs.

  14. A hydroeconomic modeling framework for optimal integrated management of forest and water

    Science.gov (United States)

    Garcia-Prats, Alberto; del Campo, Antonio D.; Pulido-Velazquez, Manuel

    2016-10-01

    Forests play a determinant role in the hydrologic cycle, with water being the most important ecosystem service they provide in semiarid regions. However, this contribution is usually neither quantified nor explicitly valued. The aim of this study is to develop a novel hydroeconomic modeling framework for assessing and designing the optimal integrated forest and water management for forested catchments. The optimization model explicitly integrates changes in water yield in the stands (increase in groundwater recharge) induced by forest management and the value of the additional water provided to the system. The model determines the optimal schedule of silvicultural interventions in the stands of the catchment in order to maximize the total net benefit in the system. Canopy cover and biomass evolution over time were simulated using growth and yield allometric equations specific for the species in Mediterranean conditions. Silvicultural operation costs according to stand density and canopy cover were modeled using local cost databases. Groundwater recharge was simulated using HYDRUS, calibrated and validated with data from the experimental plots. In order to illustrate the presented modeling framework, a case study was carried out in a planted pine forest (Pinus halepensis Mill.) located in south-western Valencia province (Spain). The optimized scenario increased groundwater recharge. This novel modeling framework can be used in the design of a "payment for environmental services" scheme in which water beneficiaries could contribute to fund and promote efficient forest management operations.

  15. Handbook on modelling for discrete optimization

    CERN Document Server

    Pitsoulis, Leonidas; Williams, H

    2006-01-01

    The primary objective underlying the Handbook on Modelling for Discrete Optimization is to demonstrate and detail the pervasive nature of Discrete Optimization. While its applications cut across an incredibly wide range of activities, many of the applications are only known to specialists. It is the aim of this handbook to correct this. It has long been recognized that "modelling" is a critically important mathematical activity in designing algorithms for solving these discrete optimization problems. Nevertheless solving the resultant models is also often far from straightforward. In recent years it has become possible to solve many large-scale discrete optimization problems. However, some problems remain a challenge, even though advances in mathematical methods, hardware, and software technology have pushed the frontiers forward. This handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It will be done in an academic handbook treatment...

  16. Method for Determining Optimal Residential Energy Efficiency Retrofit Packages

    Energy Technology Data Exchange (ETDEWEB)

    Polly, B.; Gestwick, M.; Bianchi, M.; Anderson, R.; Horowitz, S.; Christensen, C.; Judkoff, R.

    2011-04-01

    Businesses, government agencies, consumers, policy makers, and utilities currently have limited access to occupant-, building-, and location-specific recommendations for optimal energy retrofit packages, as defined by estimated costs and energy savings. This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location. Energy savings and incremental costs are calculated relative to a minimum upgrade reference scenario, which accounts for efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out and replacement with current minimum standards.

  17. Multi-objective analytical model for optimal sizing of stand-alone photovoltaic water pumping systems

    International Nuclear Information System (INIS)

    Olcan, Ceyda

    2015-01-01

    Highlights: • An analytical optimal sizing model is proposed for PV water pumping systems. • The objectives are chosen as deficiency of power supply and life-cycle costs. • The crop water requirements are estimated for a citrus tree yard in Antalya. • The optimal tilt angles are calculated for fixed, seasonal and monthly changes. • The sizing results showed the validity of the proposed analytical model. - Abstract: Stand-alone photovoltaic (PV) water pumping systems effectively use solar energy for irrigation purposes in remote areas. However the random variability and unpredictability of solar energy makes difficult the penetration of PV implementations and complicate the system design. An optimal sizing of these systems proves to be essential. This paper recommends a techno-economic optimization model to determine optimally the capacity of the components of PV water pumping system using a water storage tank. The proposed model is developed regarding the reliability and cost indicators, which are the deficiency of power supply probability and life-cycle costs, respectively. The novelty is that the proposed optimization model is analytically defined for two-objectives and it is able to find a compromise solution. The sizing of a stand-alone PV water pumping system comprises a detailed analysis of crop water requirements and optimal tilt angles. Besides the necessity of long solar radiation and temperature time series, the accurate forecasts of water supply needs have to be determined. The calculation of the optimal tilt angle for yearly, seasonally and monthly frequencies results in higher system efficiency. It is, therefore, suggested to change regularly the tilt angle in order to maximize solar energy output. The proposed optimal sizing model incorporates all these improvements and can accomplish a comprehensive optimization of PV water pumping systems. A case study is conducted considering the irrigation of citrus trees yard located in Antalya, Turkey

  18. Optimization Models for Petroleum Field Exploitation

    Energy Technology Data Exchange (ETDEWEB)

    Jonsbraaten, Tore Wiig

    1998-12-31

    This thesis presents and discusses various models for optimal development of a petroleum field. The objective of these optimization models is to maximize, under many uncertain parameters, the project`s expected net present value. First, an overview of petroleum field optimization is given from the point of view of operations research. Reservoir equations for a simple reservoir system are derived and discretized and included in optimization models. Linear programming models for optimizing production decisions are discussed and extended to mixed integer programming models where decisions concerning platform, wells and production strategy are optimized. Then, optimal development decisions under uncertain oil prices are discussed. The uncertain oil price is estimated by a finite set of price scenarios with associated probabilities. The problem is one of stochastic mixed integer programming, and the solution approach is to use a scenario and policy aggregation technique developed by Rockafellar and Wets although this technique was developed for continuous variables. Stochastic optimization problems with focus on problems with decision dependent information discoveries are also discussed. A class of ``manageable`` problems is identified and an implicit enumeration algorithm for finding optimal decision policy is proposed. Problems involving uncertain reservoir properties but with a known initial probability distribution over possible reservoir realizations are discussed. Finally, a section on Nash-equilibrium and bargaining in an oil reservoir management game discusses the pool problem arising when two lease owners have access to the same underlying oil reservoir. Because the oil tends to migrate, both lease owners have incentive to drain oil from the competitors part of the reservoir. The discussion is based on a numerical example. 107 refs., 31 figs., 14 tabs.

  19. Model Risk in Portfolio Optimization

    Directory of Open Access Journals (Sweden)

    David Stefanovits

    2014-08-01

    Full Text Available We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture model. This model allows for heavy tails, tail dependence and leptokurtosis of marginals. The results show that mean-variance optimization is seriously compromised by model uncertainty, in particular, for non-Gaussian data and small sample sizes. To mitigate these shortcomings, we propose a method to adjust the sample covariance matrix in order to reduce model risk.

  20. An optimal generic model for multi-parameters and big data optimizing: a laboratory experimental study

    Science.gov (United States)

    Utama, D. N.; Ani, N.; Iqbal, M. M.

    2018-03-01

    Optimization is a process for finding parameter (parameters) that is (are) able to deliver an optimal value for an objective function. Seeking an optimal generic model for optimizing is a computer science study that has been being practically conducted by numerous researchers. Generic model is a model that can be technically operated to solve any varieties of optimization problem. By using an object-oriented method, the generic model for optimizing was constructed. Moreover, two types of optimization method, simulated-annealing and hill-climbing, were functioned in constructing the model and compared to find the most optimal one then. The result said that both methods gave the same result for a value of objective function and the hill-climbing based model consumed the shortest running time.

  1. Determining Optimal Replacement Policy with an Availability Constraint via Genetic Algorithms

    OpenAIRE

    Zong, Shengliang; Chai, Guorong; Su, Yana

    2017-01-01

    We develop a model and a genetic algorithm for determining an optimal replacement policy for power equipment subject to Poisson shocks. If the time interval of two consecutive shocks is less than a threshold value, the failed equipment can be repaired. We assume that the operating time after repair is stochastically nonincreasing and the repair time is exponentially distributed with a geometric increasing mean. Our objective is to minimize the expected average cost under an availability requi...

  2. Optimization Modeling with Spreadsheets

    CERN Document Server

    Baker, Kenneth R

    2011-01-01

    This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming and heuristic programming; as well as an emphasis on model building using Excel and Solver.  The emphasis on model building (rather than algorithms) is one of the features that makes this book distinctive. Most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the sp

  3. Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes

    Directory of Open Access Journals (Sweden)

    Trine Krogh-Madsen

    2017-12-01

    Full Text Available In silico cardiac myocyte models present powerful tools for drug safety testing and for predicting phenotypical consequences of ion channel mutations, but their accuracy is sometimes limited. For example, several models describing human ventricular electrophysiology perform poorly when simulating effects of long QT mutations. Model optimization represents one way of obtaining models with stronger predictive power. Using a recent human ventricular myocyte model, we demonstrate that model optimization to clinical long QT data, in conjunction with physiologically-based bounds on intracellular calcium and sodium concentrations, better constrains model parameters. To determine if the model optimized to congenital long QT data better predicts risk of drug-induced long QT arrhythmogenesis, in particular Torsades de Pointes risk, we tested the optimized model against a database of known arrhythmogenic and non-arrhythmogenic ion channel blockers. When doing so, the optimized model provided an improved risk assessment. In particular, we demonstrate an elimination of false-positive outcomes generated by the baseline model, in which simulations of non-torsadogenic drugs, in particular verapamil, predict action potential prolongation. Our results underscore the importance of currents beyond those directly impacted by a drug block in determining torsadogenic risk. Our study also highlights the need for rich data in cardiac myocyte model optimization and substantiates such optimization as a method to generate models with higher accuracy of predictions of drug-induced cardiotoxicity.

  4. Modeling and optimization of laser cutting operations

    Directory of Open Access Journals (Sweden)

    Gadallah Mohamed Hassan

    2015-01-01

    Full Text Available Laser beam cutting is one important nontraditional machining process. This paper optimizes the parameters of laser beam cutting parameters of stainless steel (316L considering the effect of input parameters such as power, oxygen pressure, frequency and cutting speed. Statistical design of experiments is carried in three different levels and process responses such as average kerf taper (Ta, surface roughness (Ra and heat affected zones are measured accordingly. A response surface model is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27OA are employed to search for an optimal combination to achieve desired process yield. Response Surface Models (RSMs are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective optimization problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA and optimized using Matlab developed environment. Optimum solutions are compared with Taguchi Methodology results. As such, practicing engineers have means to model, analyze and optimize nontraditional machining processes. Validation experiments are carried to verify the developed models with success.

  5. A MATHEMATICAL MODEL OF OPTIMIZATION OF THE VOLUME OF MATERIAL FLOWS IN GRAIN PROCESSING INTEGRATED PRODUCTION SYSTEMS

    OpenAIRE

    Baranovskaya T. P.; Loyko V. I.; Makarevich O. A.; Bogoslavskiy S. N.

    2014-01-01

    The article suggests a mathematical model of optimization of the volume of material flows: the model for the ideal conditions; the model for the working conditions; generalized model of determining the optimal input parameters. These models optimize such parameters of inventory management in technology-integrated grain production systems, as the number of cycles supply, the volume of the source material and financial flows. The study was carried out on the example of the integrated system of ...

  6. Application of an Evolutionary Algorithm for Parameter Optimization in a Gully Erosion Model

    Energy Technology Data Exchange (ETDEWEB)

    Rengers, Francis; Lunacek, Monte; Tucker, Gregory

    2016-06-01

    Herein we demonstrate how to use model optimization to determine a set of best-fit parameters for a landform model simulating gully incision and headcut retreat. To achieve this result we employed the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an iterative process in which samples are created based on a distribution of parameter values that evolve over time to better fit an objective function. CMA-ES efficiently finds optimal parameters, even with high-dimensional objective functions that are non-convex, multimodal, and non-separable. We ran model instances in parallel on a high-performance cluster, and from hundreds of model runs we obtained the best parameter choices. This method is far superior to brute-force search algorithms, and has great potential for many applications in earth science modeling. We found that parameters representing boundary conditions tended to converge toward an optimal single value, whereas parameters controlling geomorphic processes are defined by a range of optimal values.

  7. Optimizing ZigBee Security using Stochastic Model Checking

    DEFF Research Database (Denmark)

    Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming

    , we identify an important gap in the specification on key updates, and present a methodology for determining optimal key update policies and security parameters. We exploit the stochastic model checking approach using the probabilistic model checker PRISM, and assess the security needs for realistic......ZigBee is a fairly new but promising wireless sensor network standard that offers the advantages of simple and low resource communication. Nevertheless, security is of great concern to ZigBee, and enhancements are prescribed in the latest ZigBee specication: ZigBee-2007. In this technical report...

  8. Visual prosthesis wireless energy transfer system optimal modeling.

    Science.gov (United States)

    Li, Xueping; Yang, Yuan; Gao, Yong

    2014-01-16

    Wireless energy transfer system is an effective way to solve the visual prosthesis energy supply problems, theoretical modeling of the system is the prerequisite to do optimal energy transfer system design. On the basis of the ideal model of the wireless energy transfer system, according to visual prosthesis application condition, the system modeling is optimized. During the optimal modeling, taking planar spiral coils as the coupling devices between energy transmitter and receiver, the effect of the parasitic capacitance of the transfer coil is considered, and especially the concept of biological capacitance is proposed to consider the influence of biological tissue on the energy transfer efficiency, resulting in the optimal modeling's more accuracy for the actual application. The simulation data of the optimal model in this paper is compared with that of the previous ideal model, the results show that under high frequency condition, the parasitic capacitance of inductance and biological capacitance considered in the optimal model could have great impact on the wireless energy transfer system. The further comparison with the experimental data verifies the validity and accuracy of the optimal model proposed in this paper. The optimal model proposed in this paper has a higher theoretical guiding significance for the wireless energy transfer system's further research, and provide a more precise model reference for solving the power supply problem in visual prosthesis clinical application.

  9. Optimal blood glucose level control using dynamic programming based on minimal Bergman model

    Science.gov (United States)

    Rettian Anggita Sari, Maria; Hartono

    2018-03-01

    The purpose of this article is to simulate the glucose dynamic and the insulin kinetic of diabetic patient. The model used in this research is a non-linear Minimal Bergman model. Optimal control theory is then applied to formulate the problem in order to determine the optimal dose of insulin in the treatment of diabetes mellitus such that the glucose level is in the normal range for some specific time range. The optimization problem is solved using dynamic programming. The result shows that dynamic programming is quite reliable to represent the interaction between glucose and insulin levels in diabetes mellitus patient.

  10. Code Differentiation for Hydrodynamic Model Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Henninger, R.J.; Maudlin, P.J.

    1999-06-27

    Use of a hydrodynamics code for experimental data fitting purposes (an optimization problem) requires information about how a computed result changes when the model parameters change. These so-called sensitivities provide the gradient that determines the search direction for modifying the parameters to find an optimal result. Here, the authors apply code-based automatic differentiation (AD) techniques applied in the forward and adjoint modes to two problems with 12 parameters to obtain these gradients and compare the computational efficiency and accuracy of the various methods. They fit the pressure trace from a one-dimensional flyer-plate experiment and examine the accuracy for a two-dimensional jet-formation problem. For the flyer-plate experiment, the adjoint mode requires similar or less computer time than the forward methods. Additional parameters will not change the adjoint mode run time appreciably, which is a distinct advantage for this method. Obtaining ''accurate'' sensitivities for the j et problem parameters remains problematic.

  11. Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection.

    Directory of Open Access Journals (Sweden)

    Mark N Read

    2016-09-01

    Full Text Available The advent of two-photon microscopy now reveals unprecedented, detailed spatio-temporal data on cellular motility and interactions in vivo. Understanding cellular motility patterns is key to gaining insight into the development and possible manipulation of the immune response. Computational simulation has become an established technique for understanding immune processes and evaluating hypotheses in the context of experimental data, and there is clear scope to integrate microscopy-informed motility dynamics. However, determining which motility model best reflects in vivo motility is non-trivial: 3D motility is an intricate process requiring several metrics to characterize. This complicates model selection and parameterization, which must be performed against several metrics simultaneously. Here we evaluate Brownian motion, Lévy walk and several correlated random walks (CRWs against the motility dynamics of neutrophils and lymph node T cells under inflammatory conditions by simultaneously considering cellular translational and turn speeds, and meandering indices. Heterogeneous cells exhibiting a continuum of inherent translational speeds and directionalities comprise both datasets, a feature significantly improving capture of in vivo motility when simulated as a CRW. Furthermore, translational and turn speeds are inversely correlated, and the corresponding CRW simulation again improves capture of our in vivo data, albeit to a lesser extent. In contrast, Brownian motion poorly reflects our data. Lévy walk is competitive in capturing some aspects of neutrophil motility, but T cell directional persistence only, therein highlighting the importance of evaluating models against several motility metrics simultaneously. This we achieve through novel application of multi-objective optimization, wherein each model is independently implemented and then parameterized to identify optimal trade-offs in performance against each metric. The resultant Pareto

  12. Oyster Creek cycle 10 nodal model parameter optimization study using PSMS

    International Nuclear Information System (INIS)

    Dougher, J.D.

    1987-01-01

    The power shape monitoring system (PSMS) is an on-line core monitoring system that uses a three-dimensional nodal code (NODE-B) to perform nodal power calculations and compute thermal margins. The PSMS contains a parameter optimization function that improves the ability of NODE-B to accurately monitor core power distributions. This functions iterates on the model normalization parameters (albedos and mixing factors) to obtain the best agreement between predicted and measured traversing in-core probe (TIP) reading on a statepoint-by-statepoint basis. Following several statepoint optimization runs, an average set of optimized normalization parameters can be determined and can be implemented into the current or subsequent cycle core model for on-line core monitoring. A statistical analysis of 19 high-power steady-state state-points throughout Oyster Creek cycle 10 operation has shown a consistently poor virgin model performance. The normalization parameters used in the cycle 10 NODE-B model were based on a cycle 8 study, which evaluated only Exxon fuel types. The introduction of General Electric (GE) fuel into cycle 10 (172 assemblies) was a significant fuel/core design change that could have altered the optimum set of normalization parameters. Based on the need to evaluate a potential change in the model normalization parameters for cycle 11 and in an attempt to account for the poor cycle 10 model performance, a parameter optimization study was performed

  13. Following an Optimal Batch Bioreactor Operations Model

    DEFF Research Database (Denmark)

    Ibarra-Junquera, V.; Jørgensen, Sten Bay; Virgen-Ortíz, J.J.

    2012-01-01

    The problem of following an optimal batch operation model for a bioreactor in the presence of uncertainties is studied. The optimal batch bioreactor operation model (OBBOM) refers to the bioreactor trajectory for nominal cultivation to be optimal. A multiple-variable dynamic optimization of fed...... as the master system which includes the optimal cultivation trajectory for the feed flow rate and the substrate concentration. The “real” bioreactor, the one with unknown dynamics and perturbations, is considered as the slave system. Finally, the controller is designed such that the real bioreactor...

  14. STOCHASTIC MODELING OF OPTIMIZED CREDIT STRATEGY OF A DISTRIBUTING COMPANY ON THE PHARMACEUTICAL MARKET

    Directory of Open Access Journals (Sweden)

    M. Boychuk

    2015-10-01

    Full Text Available The activity of distribution companies is multifaceted. Ihey establish contacts with producers and consumers, determine the range of prices of medicines, do promotions, hold stocks of pharmaceuticals and take risks in their further selling.Their internal problems are complicated by the political crisis in the country, decreased purchasing power of national currency, and the rise in interest rates on loans. Therefore the usage of stochastic models of dynamic systems for the research into optimizing the management of pharmaceutical products distribution companies taking into account credit payments is of great current interest. A stochastic model of the optimal credit strategy of a pharmaceutical distributor in the market of pharmaceutical products has been constructed in the article considering credit payments and income limitations. From the mathematical point of view the obtained problem is the one of stochastic optimal control where the amount of monetary credit is the control and the amount of pharmaceutical product is the solution curve. The model allows to identify the optimal cash loan and the corresponding optimal quantity of pharmaceutical product that comply with the differential model of the existing quantity of pharmaceutical products in the form of Ito; the condition of the existing initial stock of pharmaceutical products; the limitation on the amount of credit and profit received from the product selling and maximize the average integral income. The research of the stochastic optimal control problem involves the construction of the left process of crediting with determination of the shift point of that control, the choice of the right crediting process and the formation of the optimal credit process. It was found that the optimal control of the credit amount and the shift point of that control are the determined values and don’t depend on the coefficient in the Wiener process and the optimal trajectory of the amount of

  15. The determination of optimal climate policy

    International Nuclear Information System (INIS)

    Aaheim, Asbjoern

    2010-01-01

    Analyses of the costs and benefits of climate policy, such as the Stern Review, evaluate alternative strategies to reduce greenhouse gas emissions by requiring that the cost of emission cuts in each and every year has to be covered by the associated value of avoided damage, discounted by a an exogenously chosen rate. An alternative is to optimize abatement programmes towards a stationary state, where the concentrations of greenhouse gases are stabilized and shadow prices, including the rate of discount, are determined endogenously. This paper examines the properties of optimized stabilization. It turns out that the implications for the evaluation of climate policy are substantial if compared with evaluations of the present value of costs and benefits based on exogenously chosen shadow prices. Comparisons of discounted costs and benefits tend to exaggerate the importance of the choice of discount rate, while ignoring the importance of future abatement costs, which turns out to be essential for the optimal abatement path. Numerical examples suggest that early action may be more beneficial than indicated by comparisons of costs and benefits discounted by a rate chosen on the basis of current observations. (author)

  16. A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering.

    Science.gov (United States)

    Klamt, Steffen; Müller, Stefan; Regensburger, Georg; Zanghellini, Jürgen

    2018-02-07

    The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small

  17. Determination of the optimal number of components in independent components analysis.

    Science.gov (United States)

    Kassouf, Amine; Jouan-Rimbaud Bouveresse, Delphine; Rutledge, Douglas N

    2018-03-01

    Independent components analysis (ICA) may be considered as one of the most established blind source separation techniques for the treatment of complex data sets in analytical chemistry. Like other similar methods, the determination of the optimal number of latent variables, in this case, independent components (ICs), is a crucial step before any modeling. Therefore, validation methods are required in order to decide about the optimal number of ICs to be used in the computation of the final model. In this paper, three new validation methods are formally presented. The first one, called Random_ICA, is a generalization of the ICA_by_blocks method. Its specificity resides in the random way of splitting the initial data matrix into two blocks, and then repeating this procedure several times, giving a broader perspective for the selection of the optimal number of ICs. The second method, called KMO_ICA_Residuals is based on the computation of the Kaiser-Meyer-Olkin (KMO) index of the transposed residual matrices obtained after progressive extraction of ICs. The third method, called ICA_corr_y, helps to select the optimal number of ICs by computing the correlations between calculated proportions and known physico-chemical information about samples, generally concentrations, or between a source signal known to be present in the mixture and the signals extracted by ICA. These three methods were tested using varied simulated and experimental data sets and compared, when necessary, to ICA_by_blocks. Results were relevant and in line with expected ones, proving the reliability of the three proposed methods. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Model-based dynamic control and optimization of gas networks

    Energy Technology Data Exchange (ETDEWEB)

    Hofsten, Kai

    2001-07-01

    This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum

  19. Dynamic optimization deterministic and stochastic models

    CERN Document Server

    Hinderer, Karl; Stieglitz, Michael

    2016-01-01

    This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.

  20. Intelligent structural optimization: Concept, Model and Methods

    International Nuclear Information System (INIS)

    Lu, Dagang; Wang, Guangyuan; Peng, Zhang

    2002-01-01

    Structural optimization has many characteristics of Soft Design, and so, it is necessary to apply the experience of human experts to solving the uncertain and multidisciplinary optimization problems in large-scale and complex engineering systems. With the development of artificial intelligence (AI) and computational intelligence (CI), the theory of structural optimization is now developing into the direction of intelligent optimization. In this paper, a concept of Intelligent Structural Optimization (ISO) is proposed. And then, a design process model of ISO is put forward in which each design sub-process model are discussed. Finally, the design methods of ISO are presented

  1. Using genetic algorithm to solve a new multi-period stochastic optimization model

    Science.gov (United States)

    Zhang, Xin-Li; Zhang, Ke-Cun

    2009-09-01

    This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.

  2. Determination of coefficient matrices for ARMA model

    International Nuclear Information System (INIS)

    Tran Dinh Tri.

    1990-10-01

    A new recursive algorithm for determining coefficient matrices of ARMA model from measured data is presented. The Yule-Walker equations for the case of ARMA model are derived from the ARMA innovation equation. The recursive algorithm is based on choosing appropriate form of the operator functions and suitable representation of the (n+1)-th order operator functions according to ones with the lower order. Two cases, when the order of the AR part is equal to one of the MA part, and the optimal case, were considered. (author) 5 refs

  3. DETERMINATION OF BRAKING OPTIMAL MODE OF CONTROLLED CUT OF DESIGN GROUP

    Directory of Open Access Journals (Sweden)

    A. S. Dorosh

    2015-06-01

    Full Text Available Purpose. The application of automation systems of breaking up process on the gravity hump is the efficiency improvement of their operation, absolute provision of trains breaking up safety demands, as well as improvement of hump staff working conditions. One of the main tasks of the indicated systems is the assurance of cuts reliable separation at all elements of their rolling route to the classification track. This task is a sophisticated optimization problem and has not received a final decision. Therefore, the task of determining the cuts braking mode is quite relevant. The purpose of this research is to find the optimal braking mode of control cut of design group. Methodology. In order to achieve the purpose is offered to use the direct search methods in the work, namely the Box complex method. This method does not require smoothness of the objective function, takes into account its limitations and does not require calculation of the function derivatives, and uses only its value. Findings. Using the Box method was developed iterative procedure for determining the control cut optimal braking mode of design group. The procedure maximizes the smallest controlled time interval in the group. To evaluate the effectiveness of designed procedure the series of simulation experiments of determining the control cut braking mode of design group was performed. The results confirmed the efficiency of the developed optimization procedure. Originality. The author formalized the task of optimizing control cut braking mode of design group, taking into account the cuts separation of design group at all elements (switches, retarders during cuts rolling to the classification track. The problem of determining the optimal control cut braking mode of design group was solved. The developed braking mode ensures cuts reliable separation of the group not only at the switches but at the retarders of brake position. Practical value. The developed procedure can be

  4. Pyomo optimization modeling in Python

    CERN Document Server

    Hart, William E; Watson, Jean-Paul; Woodruff, David L; Hackebeil, Gabriel A; Nicholson, Bethany L; Siirola, John D

    2017-01-01

    This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package fo...

  5. Polymer models with optimal good-solvent behavior

    Science.gov (United States)

    D'Adamo, Giuseppe; Pelissetto, Andrea

    2017-11-01

    We consider three different continuum polymer models, which all depend on a tunable parameter r that determines the strength of the excluded-volume interactions. In the first model, chains are obtained by concatenating hard spherocylinders of height b and diameter rb (we call them thick self-avoiding chains). The other two models are generalizations of the tangent hard-sphere and of the Kremer-Grest models. We show that for a specific value r* , all models show optimal behavior: asymptotic long-chain behavior is observed for relatively short chains. For r < r* , instead, the behavior can be parametrized by using the two-parameter model, which also describes the thermal crossover close to the θ point. The bonds of the thick self-avoiding chains cannot cross each other, and therefore the model is suited for the investigation of topological properties and for dynamical studies. Such a model also provides a coarse-grained description of double-stranded DNA, so that we can use our results to discuss under which conditions DNA can be considered as a model good-solvent polymer.

  6. Surrogate-Based Optimization of Biogeochemical Transport Models

    Science.gov (United States)

    Prieß, Malte; Slawig, Thomas

    2010-09-01

    First approaches towards a surrogate-based optimization method for a one-dimensional marine biogeochemical model of NPZD type are presented. The model, developed by Oschlies and Garcon [1], simulates the distribution of nitrogen, phytoplankton, zooplankton and detritus in a water column and is driven by ocean circulation data. A key issue is to minimize the misfit between the model output and given observational data. Our aim is to reduce the overall optimization cost avoiding expensive function and derivative evaluations by using a surrogate model replacing the high-fidelity model in focus. This in particular becomes important for more complex three-dimensional models. We analyse a coarsening in the discretization of the model equations as one way to create such a surrogate. Here the numerical stability crucially depends upon the discrete stepsize in time and space and the biochemical terms. We show that for given model parameters the level of grid coarsening can be choosen accordingly yielding a stable and satisfactory surrogate. As one example of a surrogate-based optimization method we present results of the Aggressive Space Mapping technique (developed by John W. Bandler [2, 3]) applied to the optimization of this one-dimensional biogeochemical transport model.

  7. An optimization model for transportation of hazardous materials

    International Nuclear Information System (INIS)

    Seyed-Hosseini, M.; Kheirkhah, A. S.

    2005-01-01

    In this paper, the optimal routing problem for transportation of hazardous materials is studied. Routing for the purpose of reducing the risk of transportation of hazardous materials has been studied and formulated by many researcher and several routing models have been presented up to now. These models can be classified into the categories: the models for routing a single movement and the models for routing multiple movements. In this paper, according to the current rules and regulations of road transportations of hazardous materials in Iran, a routing problem is designed. In this problem, the routs for several independent movements are simultaneously determined. To examine the model, the problem the transportations of two different dangerous materials in the road network of Mazandaran province in the north of Iran is formulated and solved by applying Integer programming model

  8. Experimental determination of optimal clamping torque for AB-PEM Fuel cell

    Directory of Open Access Journals (Sweden)

    Noor Ul Hassan

    2016-04-01

    Full Text Available Polymer electrolyte Membrane (PEM fuel cell is an electrochemical device producing electricity by the reaction of hydrogen and oxygen without combustion. PEM fuel cell stack is provided with an appropriate clamping torque to prevent leakage of reactant gases and to minimize the contact resistance between gas diffusion media (GDL and bipolar plates. GDL porous structure and gas permeability is directly affected by the compaction pressure which, consequently, drastically change the fuel cell performance. Various efforts were made to determine the optimal compaction pressure and pressure distributions through simulations and experimentation. Lower compaction pressure results in increase of contact resistance and also chances of leakage. On the other hand, higher compaction pressure decreases the contact resistance but also narrows down the diffusion path for mass transfer from gas channels to the catalyst layers, consequently, lowering cell performance. The optimal cell performance is related to the gasket thickness and compression pressure on GDL. Every stack has a unique assembly pressure due to differences in fuel cell components material and stack design. Therefore, there is still need to determine the optimal torque value for getting the optimal cell performance. This study has been carried out in continuation of deve­lopment of Air breathing PEM fuel cell for small Unmanned Aerial Vehicle (UAV application. Compaction pressure at minimum contact resistance was determined and clamping torque value was calcu­la­ted accordingly. Single cell performance tests were performed at five different clamping torque values i.e 0.5, 1.0, 1.5, 2.0 and 2.5 N m, for achieving optimal cell per­formance. Clamping pressure distribution tests were also performed at these torque values to verify uniform pressure distribution at optimal torque value. Experimental and theoretical results were compared for making inferences about optimal cell perfor­man­ce. A

  9. A kriging metamodel-assisted robust optimization method based on a reverse model

    Science.gov (United States)

    Zhou, Hui; Zhou, Qi; Liu, Congwei; Zhou, Taotao

    2018-02-01

    The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors. Most existing robust optimization approaches use outer-inner nested optimization structures where a large amount of computational effort is required because the robustness of each candidate solution delivered from the outer level should be evaluated in the inner level. In this article, a kriging metamodel-assisted robust optimization method based on a reverse model (K-RMRO) is first proposed, in which the nested optimization structure is reduced into a single-loop optimization structure to ease the computational burden. Ignoring the interpolation uncertainties from kriging, K-RMRO may yield non-robust optima. Hence, an improved kriging-assisted robust optimization method based on a reverse model (IK-RMRO) is presented to take the interpolation uncertainty of kriging metamodel into consideration. In IK-RMRO, an objective switching criterion is introduced to determine whether the inner level robust optimization or the kriging metamodel replacement should be used to evaluate the robustness of design alternatives. The proposed criterion is developed according to whether or not the robust status of the individual can be changed because of the interpolation uncertainties from the kriging metamodel. Numerical and engineering cases are used to demonstrate the applicability and efficiency of the proposed approach.

  10. Systemic Model for Optimal Regulation in Public Service

    Directory of Open Access Journals (Sweden)

    Lucica Matei

    2006-05-01

    Full Text Available The current paper inscribes within those approaching the issue of public services from the interdisciplinary perspective. Public service development and imposing standards of efficiency and effectiveness, as well as for citizens’ satisfaction bring in front line the systemic modelling and establishing optimal policies for organisation and functioning of public services. The issue under discussion imposes an interface with powerful determinations of social nature. Consequently, the most adequate modelling might be that with a probabilistic and statistic nature. The fundamental idea of this paper, that obviously can be broadly developed, starts with assimilating the way of organisation and functioning of a public service with a waiting thread, to which some hypotheses are associated concerning the order of provision, performance measurement through costs or waiting time in the system etc. We emphasise the openness and dynamics of the public service system, as well as modelling by turning into account the statistic knowledge and researches, and we do not make detailed remarks on the cybernetic characteristics of this system. The optimal adjustment is achieved through analysis on the feedback and its comparison with the current standards or good practices.

  11. Error propagation of partial least squares for parameters optimization in NIR modeling

    Science.gov (United States)

    Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

    2018-03-01

    A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models.

  12. Error propagation of partial least squares for parameters optimization in NIR modeling.

    Science.gov (United States)

    Du, Chenzhao; Dai, Shengyun; Qiao, Yanjiang; Wu, Zhisheng

    2018-03-05

    A novel methodology is proposed to determine the error propagation of partial least-square (PLS) for parameters optimization in near-infrared (NIR) modeling. The parameters include spectral pretreatment, latent variables and variable selection. In this paper, an open source dataset (corn) and a complicated dataset (Gardenia) were used to establish PLS models under different modeling parameters. And error propagation of modeling parameters for water quantity in corn and geniposide quantity in Gardenia were presented by both type І and type II error. For example, when variable importance in the projection (VIP), interval partial least square (iPLS) and backward interval partial least square (BiPLS) variable selection algorithms were used for geniposide in Gardenia, compared with synergy interval partial least squares (SiPLS), the error weight varied from 5% to 65%, 55% and 15%. The results demonstrated how and what extent the different modeling parameters affect error propagation of PLS for parameters optimization in NIR modeling. The larger the error weight, the worse the model. Finally, our trials finished a powerful process in developing robust PLS models for corn and Gardenia under the optimal modeling parameters. Furthermore, it could provide a significant guidance for the selection of modeling parameters of other multivariate calibration models. Copyright © 2017. Published by Elsevier B.V.

  13. Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

    Science.gov (United States)

    Aprasoff, Jonathan; Donchin, Opher

    2012-04-01

    Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

  14. The research on optimization of auto supply chain network robust model under macroeconomic fluctuations

    International Nuclear Information System (INIS)

    Guo, Chunxiang; Liu, Xiaoli; Jin, Maozhu; Lv, Zhihan

    2016-01-01

    Considering the uncertainty of the macroeconomic environment, the robust optimization method is studied for constructing and designing the automotive supply chain network, and based on the definition of robust solution a robust optimization model is built for integrated supply chain network design that consists of supplier selection problem and facility location–distribution problem. The tabu search algorithm is proposed for supply chain node configuration, analyzing the influence of the level of uncertainty on robust results, and by comparing the performance of supply chain network design through the stochastic programming model and robustness optimize model, on this basis, determining the rational layout of supply chain network under macroeconomic fluctuations. At last the contrastive test result validates that the performance of tabu search algorithm is outstanding on convergence and computational time. Meanwhile it is indicated that the robust optimization model can reduce investment risks effectively when it is applied to supply chain network design.

  15. Vector-model-supported approach in prostate plan optimization

    International Nuclear Information System (INIS)

    Liu, Eva Sau Fan; Wu, Vincent Wing Cheung; Harris, Benjamin; Lehman, Margot; Pryor, David; Chan, Lawrence Wing Chi

    2017-01-01

    Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

  16. Vector-model-supported approach in prostate plan optimization

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Eva Sau Fan [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Wu, Vincent Wing Cheung [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong); Harris, Benjamin [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); Lehman, Margot; Pryor, David [Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane (Australia); School of Medicine, University of Queensland (Australia); Chan, Lawrence Wing Chi, E-mail: wing.chi.chan@polyu.edu.hk [Department of Health Technology and Informatics, The Hong Kong Polytechnic University (Hong Kong)

    2017-07-01

    Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration

  17. A Monte Carlo simulation technique to determine the optimal portfolio

    Directory of Open Access Journals (Sweden)

    Hassan Ghodrati

    2014-03-01

    Full Text Available During the past few years, there have been several studies for portfolio management. One of the primary concerns on any stock market is to detect the risk associated with various assets. One of the recognized methods in order to measure, to forecast, and to manage the existing risk is associated with Value at Risk (VaR, which draws much attention by financial institutions in recent years. VaR is a method for recognizing and evaluating of risk, which uses the standard statistical techniques and the method has been used in other fields, increasingly. The present study has measured the value at risk of 26 companies from chemical industry in Tehran Stock Exchange over the period 2009-2011 using the simulation technique of Monte Carlo with 95% confidence level. The used variability in the present study has been the daily return resulted from the stock daily price change. Moreover, the weight of optimal investment has been determined using a hybrid model called Markowitz and Winker model in each determined stocks. The results showed that the maximum loss would not exceed from 1259432 Rials at 95% confidence level in future day.

  18. Determination of the optimal dose reduction level via iterative reconstruction using 640-slice volume chest CT in a pig model.

    Directory of Open Access Journals (Sweden)

    Xingli Liu

    Full Text Available To determine the optimal dose reduction level of iterative reconstruction technique for paediatric chest CT in pig models.27 infant pigs underwent 640-slice volume chest CT with 80kVp and different mAs. Automatic exposure control technique was used, and the index of noise was set to SD10 (Group A, routine dose, SD12.5, SD15, SD17.5, SD20 (Groups from B to E to reduce dose respectively. Group A was reconstructed with filtered back projection (FBP, and Groups from B to E were reconstructed using iterative reconstruction (IR. Objective and subjective image quality (IQ among groups were compared to determine an optimal radiation reduction level.The noise and signal-to-noise ratio (SNR in Group D had no significant statistical difference from that in Group A (P = 1.0. The scores of subjective IQ in Group A were not significantly different from those in Group D (P>0.05. There were no obvious statistical differences in the objective and subjective index values among the subgroups (small, medium and large subgroups of Group D. The effective dose (ED of Group D was 58.9% lower than that of Group A (0.20±0.05mSv vs 0.48±0.10mSv, p <0.001.In infant pig chest CT, using iterative reconstruction can provide diagnostic image quality; furthermore, it can reduce the dosage by 58.9%.

  19. A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting

    International Nuclear Information System (INIS)

    Su, Zhongyue; Wang, Jianzhou; Lu, Haiyan; Zhao, Ge

    2014-01-01

    Highlights: • A new hybrid model is developed for wind speed forecasting. • The model is based on the Kalman filter and the ARIMA. • An intelligent optimization method is employed in the hybrid model. • The new hybrid model has good performance in western China. - Abstract: Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily

  20. A two-stage stochastic rule-based model to determine pre-assembly buffer content

    Science.gov (United States)

    Gunay, Elif Elcin; Kula, Ufuk

    2018-01-01

    This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model.

  1. Statistical models for optimizing mineral exploration

    International Nuclear Information System (INIS)

    Wignall, T.K.; DeGeoffroy, J.

    1987-01-01

    The primary purpose of mineral exploration is to discover ore deposits. The emphasis of this volume is on the mathematical and computational aspects of optimizing mineral exploration. The seven chapters that make up the main body of the book are devoted to the description and application of various types of computerized geomathematical models. These chapters include: (1) the optimal selection of ore deposit types and regions of search, as well as prospecting selected areas, (2) designing airborne and ground field programs for the optimal coverage of prospecting areas, and (3) delineating and evaluating exploration targets within prospecting areas by means of statistical modeling. Many of these statistical programs are innovative and are designed to be useful for mineral exploration modeling. Examples of geomathematical models are applied to exploring for six main types of base and precious metal deposits, as well as other mineral resources (such as bauxite and uranium)

  2. Multi objective optimization model for minimizing production cost and environmental impact in CNC turning process

    Science.gov (United States)

    Widhiarso, Wahyu; Rosyidi, Cucuk Nur

    2018-02-01

    Minimizing production cost in a manufacturing company will increase the profit of the company. The cutting parameters will affect total processing time which then will affect the production cost of machining process. Besides affecting the production cost and processing time, the cutting parameters will also affect the environment. An optimization model is needed to determine the optimum cutting parameters. In this paper, we develop an optimization model to minimize the production cost and the environmental impact in CNC turning process. The model is used a multi objective optimization. Cutting speed and feed rate are served as the decision variables. Constraints considered are cutting speed, feed rate, cutting force, output power, and surface roughness. The environmental impact is converted from the environmental burden by using eco-indicator 99. Numerical example is given to show the implementation of the model and solved using OptQuest of Oracle Crystal Ball software. The results of optimization indicate that the model can be used to optimize the cutting parameters to minimize the production cost and the environmental impact.

  3. MENENTUKAN PORTOFOLIO OPTIMAL PADA PASAR SAHAM YANG BERGERAK DENGAN MODEL GERAK BROWN GEOMETRI MULTIDIMENSI

    Directory of Open Access Journals (Sweden)

    RISKA YUNITA

    2015-06-01

    Full Text Available Model of stock price movements that follow stochastic process can be formulated in Stochastic Diferential Equation (SDE. The exact solution of SDE model is called Geometric Brownian Motion (GBM model. Determination the optimal portfolio of three asset that follows Multidimensional GBM model is to be carried out in this research.Multidimensional GBM model represents stock price in the future is affected by three parameter, there are expectation of stock return, risk stock, and correlation between stock return. Therefore, theory of portfolio Markowitz is used on formation of optimal portfolio. Portfolio Markowitz formulates three of same parameter that is calculated on Multidimensional GBM model. The result of this research are optimal portfolio reaches with the proportion of fund are 39,38% for stock BBCA, 59,82% for stock ICBP, and 0,80% for stock INTP. This proportion of fund represents value of parameters that is calculated on modelling stock price.

  4. Optimization and validation of Folin-Ciocalteu method for the determination of total polyphenol content of Pu-erh tea.

    Science.gov (United States)

    Musci, Marilena; Yao, Shicong

    2017-12-01

    Pu-erh tea is a post-fermented tea that has recently gained popularity worldwide, due to potential health benefits related to the antioxidant activity resulting from its high polyphenolic content. The Folin-Ciocalteu method is a simple, rapid, and inexpensive assay widely applied for the determination of total polyphenol content. Over the past years, it has been subjected to many modifications, often without any systematic optimization or validation. In our study, we sought to optimize the Folin-Ciocalteu method, evaluate quality parameters including linearity, precision and stability, and then apply the optimized model to determine the total polyphenol content of 57 Chinese teas, including green tea, aged and ripened Pu-erh tea. Our optimized Folin-Ciocalteu method reduced analysis time, allowed for the analysis of a large number of samples, to discriminate among the different teas, and to assess the effect of the post-fermentation process on polyphenol content.

  5. Mathematical modeling and optimization of complex structures

    CERN Document Server

    Repin, Sergey; Tuovinen, Tero

    2016-01-01

    This volume contains selected papers in three closely related areas: mathematical modeling in mechanics, numerical analysis, and optimization methods. The papers are based upon talks presented  on the International Conference for Mathematical Modeling and Optimization in Mechanics, held in Jyväskylä, Finland, March 6-7, 2014 dedicated to Prof. N. Banichuk on the occasion of his 70th birthday. The articles are written by well-known scientists working in computational mechanics and in optimization of complicated technical models. Also, the volume contains papers discussing the historical development, the state of the art, new ideas, and open problems arising in  modern continuum mechanics and applied optimization problems. Several papers are concerned with mathematical problems in numerical analysis, which are also closely related to important mechanical models. The main topics treated include:  * Computer simulation methods in mechanics, physics, and biology;  * Variational problems and methods; minimiz...

  6. Model Based Optimization of Integrated Low Voltage DC-DC Converter for Energy Harvesting Applications

    Science.gov (United States)

    Jayaweera, H. M. P. C.; Muhtaroğlu, Ali

    2016-11-01

    A novel model based methodology is presented to determine optimal device parameters for the fully integrated ultra low voltage DC-DC converter for energy harvesting applications. The proposed model feasibly contributes to determine the maximum efficient number of charge pump stages to fulfill the voltage requirement of the energy harvester application. The proposed DC-DC converter based power consumption model enables the analytical derivation of the charge pump efficiency when utilized simultaneously with the known LC tank oscillator behavior under resonant conditions, and voltage step up characteristics of the cross-coupled charge pump topology. The verification of the model has been done using a circuit simulator. The optimized system through the established model achieves more than 40% maximum efficiency yielding 0.45 V output with single stage, 0.75 V output with two stages, and 0.9 V with three stages for 2.5 kΩ, 3.5 kΩ and 5 kΩ loads respectively using 0.2 V input.

  7. A combined geostatistical-optimization model for the optimal design of a groundwater quality monitoring network

    Science.gov (United States)

    Kolosionis, Konstantinos; Papadopoulou, Maria P.

    2017-04-01

    Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.

  8. Optimization Model for Machinery Selection of Multi-Crop Farms in Elsuki Agricultural Scheme

    Directory of Open Access Journals (Sweden)

    Mysara Ahmed Mohamed

    2017-07-01

    Full Text Available The optimization machinery model was developed to aid decision-makers and farm machinery managers in determining the optimal number of tractors, scheduling the agricultural operation and minimizing machinery total costs. For purpose of model verification, validation and application input data was collected from primary & secondary sources from Elsuki agricultural scheme for two seasons namely 2011-2012 and 2013-2014. Model verification was made by comparing the numbers of tractors of Elsuki agricultural scheme for season 2011-2012 with those estimated by the model. The model succeeded in reducing the number of tractors and operation total cost by 23%. The effect of optimization model on elements of direct cost saving indicated that the highest cost saving is reached with depreciation, repair and maintenance (23% and the minimum cost saving is attained with fuel cost (22%. Sensitivity analysis in terms of change in model input for each of cultivated area and total costs of operations showing that: Increasing the operation total cost by 10% decreased the total number of tractors after optimization by 23% and total cost of operations was also decreased by 23%. Increasing the cultivated area by 10%, decreased the total number of tractors after optimization by(12% and total cost of operations was also decreased by 12% (16669206 SDG(1111280 $ to 14636376 SDG(975758 $. For the case of multiple input effect of the area and operation total cost resulted in decrease maximum number of tractors by 12%, and the total cost of operations also decreased by 12%. It is recommended to apply the optimization model as pre-requisite for improving machinery management during implementation of machinery scheduling.

  9. Process optimization of friction stir welding based on thermal models

    DEFF Research Database (Denmark)

    Larsen, Anders Astrup

    2010-01-01

    This thesis investigates how to apply optimization methods to numerical models of a friction stir welding process. The work is intended as a proof-of-concept using different methods that are applicable to models of high complexity, possibly with high computational cost, and without the possibility...... information of the high-fidelity model. The optimization schemes are applied to stationary thermal models of differing complexity of the friction stir welding process. The optimization problems considered are based on optimizing the temperature field in the workpiece by finding optimal translational speed....... Also an optimization problem based on a microstructure model is solved, allowing the hardness distribution in the plate to be optimized. The use of purely thermal models represents a simplification of the real process; nonetheless, it shows the applicability of the optimization methods considered...

  10. An Optimization Principle for Deriving Nonequilibrium Statistical Models of Hamiltonian Dynamics

    Science.gov (United States)

    Turkington, Bruce

    2013-08-01

    A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. Given a vector of resolved variables, selected to describe the macroscopic state of the system, a family of quasi-equilibrium probability densities on phase space corresponding to the resolved variables is employed as a statistical model, and the evolution of the mean resolved vector is estimated by optimizing over paths of these densities. Specifically, a cost function is constructed to quantify the lack-of-fit to the microscopic dynamics of any feasible path of densities from the statistical model; it is an ensemble-averaged, weighted, squared-norm of the residual that results from submitting the path of densities to the Liouville equation. The path that minimizes the time integral of the cost function determines the best-fit evolution of the mean resolved vector. The closed reduced equations satisfied by the optimal path are derived by Hamilton-Jacobi theory. When expressed in terms of the macroscopic variables, these equations have the generic structure of governing equations for nonequilibrium thermodynamics. In particular, the value function for the optimization principle coincides with the dissipation potential that defines the relation between thermodynamic forces and fluxes. The adjustable closure parameters in the best-fit reduced equations depend explicitly on the arbitrary weights that enter into the lack-of-fit cost function. Two particular model reductions are outlined to illustrate the general method. In each example the set of weights in the optimization principle contracts into a single effective closure parameter.

  11. Mathematical model of highways network optimization

    Science.gov (United States)

    Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.

    2017-12-01

    The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.

  12. Simulation of optimal arctic routes using a numerical sea ice model based on an ice-coupled ocean circulation method

    Directory of Open Access Journals (Sweden)

    Jong-Ho Nam

    2013-06-01

    Full Text Available Ever since the Arctic region has opened its mysterious passage to mankind, continuous attempts to take advantage of its fastest route across the region has been made. The Arctic region is still covered by thick ice and thus finding a feasible navigating route is essential for an economical voyage. To find the optimal route, it is necessary to establish an efficient transit model that enables us to simulate every possible route in advance. In this work, an enhanced algorithm to determine the optimal route in the Arctic region is introduced. A transit model based on the simulated sea ice and environmental data numerically modeled in the Arctic is developed. By integrating the simulated data into a transit model, further applications such as route simulation, cost estimation or hindcast can be easily performed. An interactive simulation system that determines the optimal Arctic route using the transit model is developed. The simulation of optimal routes is carried out and the validity of the results is discussed.

  13. Optimized dispatch in a first-principles concentrating solar power production model

    Energy Technology Data Exchange (ETDEWEB)

    Wagner, Michael J.; Newman, Alexandra M.; Hamilton, William T.; Braun, Robert J.

    2017-10-01

    Concentrating solar power towers, which include a steam-Rankine cycle with molten salt thermal energy storage, is an emerging technology whose maximum effectiveness relies on an optimal operational and dispatch policy. Given parameters such as start-up and shut-down penalties, expected electricity price profiles, solar availability, and system interoperability requirements, this paper seeks a profit-maximizing solution that determines start-up and shut-down times for the power cycle and solar receiver, and the times at which to dispatch stored and instantaneous quantities of energy over a 48-h horizon at hourly fidelity. The mixed-integer linear program (MIP) is subject to constraints including: (i) minimum and maximum rates of start-up and shut-down, (ii) energy balance, including energetic state of the system as a whole and its components, (iii) logical rules governing the operational modes of the power cycle and solar receiver, and (iv) operational consistency between time periods. The novelty in this work lies in the successful integration of a dispatch optimization model into a detailed techno-economic analysis tool, specifically, the National Renewable Energy Laboratory's System Advisor Model (SAM). The MIP produces an optimized operating strategy, historically determined via a heuristic. Using several market electricity pricing profiles, we present comparative results for a system with and without dispatch optimization, indicating that dispatch optimization can improve plant profitability by 5-20% and thereby alter the economics of concentrating solar power technology. While we examine a molten salt power tower system, this analysis is equally applicable to the more mature concentrating solar parabolic trough system with thermal energy storage.

  14. Modeling and optimization of HVAC energy consumption

    Energy Technology Data Exchange (ETDEWEB)

    Kusiak, Andrew; Li, Mingyang; Tang, Fan [Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, IA 52242 - 1527 (United States)

    2010-10-15

    A data-driven approach for minimization of the energy to air condition a typical office-type facility is presented. Eight data-mining algorithms are applied to model the nonlinear relationship among energy consumption, control settings (supply air temperature and supply air static pressure), and a set of uncontrollable parameters. The multiple-linear perceptron (MLP) ensemble outperforms other models tested in this research, and therefore it is selected to model a chiller, a pump, a fan, and a reheat device. These four models are integrated into an energy optimization model with two decision variables, the setpoint of the supply air temperature and the static pressure in the air handling unit. The model is solved with a particle swarm optimization algorithm. The optimization results have demonstrated the total energy consumed by the heating, ventilation, and air-conditioning system is reduced by over 7%. (author)

  15. Optimal Hedging with the Vector Autoregressive Model

    NARCIS (Netherlands)

    L. Gatarek (Lukasz); S.G. Johansen (Soren)

    2014-01-01

    markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be

  16. A network society communicative model for optimizing the Refugee Status Determination (RSD procedures

    Directory of Open Access Journals (Sweden)

    Andrea Pacheco Pacífico

    2013-01-01

    Full Text Available This article recommends a new way to improve Refugee Status Determination (RSD procedures by proposing a network society communicative model based on active involvement and dialogue among all implementing partners. This model, named after proposals from Castells, Habermas, Apel, Chimni, and Betts, would be mediated by the United Nations High Commissioner for Refugees (UNHCR, whose role would be modeled after that of the International Committee of the Red Cross (ICRC practice.

  17. Confined compressive strength model of rock for drilling optimization

    Directory of Open Access Journals (Sweden)

    Xiangchao Shi

    2015-03-01

    Full Text Available The confined compressive strength (CCS plays a vital role in drilling optimization. On the basis of Jizba's experimental results, a new CCS model considering the effects of the porosity and nonlinear characteristics with increasing confining pressure has been developed. Because the confining pressure plays a fundamental role in determining the CCS of bottom-hole rock and because the theory of Terzaghi's effective stress principle is founded upon soil mechanics, which is not suitable for calculating the confining pressure in rock mechanics, the double effective stress theory, which treats the porosity as a weighting factor of the formation pore pressure, is adopted in this study. The new CCS model combined with the mechanical specific energy equation is employed to optimize the drilling parameters in two practical wells located in Sichuan basin, China, and the calculated results show that they can be used to identify the inefficient drilling situations of underbalanced drilling (UBD and overbalanced drilling (OBD.

  18. Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm

    International Nuclear Information System (INIS)

    Wang, Jianzhou; Jiang, He; Wu, Yujie; Dong, Yao

    2015-01-01

    Due to energy crisis and environmental problems, it is very urgent to find alternative energy sources nowadays. Solar energy, as one of the great potential clean energies, has widely attracted the attention of researchers. In this paper, an optimized hybrid method by CS (Cuckoo Search) on the basis of the OP-ELM (Optimally Pruned Extreme Learning Machine), called CS-OP-ELM, is developed to forecast clear sky and real sky global horizontal radiation. First, MRSR (Multiresponse Sparse Regression) and LOO-CV (leave-one-out cross-validation) can be applied to rank neurons and prune the possibly meaningless neurons of the FFNN (Feed Forward Neural Network), respectively. Then, Direct strategy and Direct-Recursive strategy based on OP-ELM are introduced to build a hybrid model. Furthermore, CS (Cuckoo Search) optimized algorithm is employed to determine the proper weight coefficients. In order to verify the effectiveness of the developed method, hourly solar radiation data from six sites of the United States has been collected, and methods like ARMA (Autoregression moving average), BP (Back Propagation) neural network and OP-ELM can be compared with CS-OP-ELM. Experimental results show the optimized hybrid method CS-OP-ELM has the best forecasting performance. - Highlights: • An optimized hybrid method called CS-OP-ELM is proposed to forecast solar radiation. • CS-OP-ELM adopts multiple variables dataset as input variables. • Direct and Direct-Recursive strategy are introduced to build a hybrid model. • CS (Cuckoo Search) algorithm is used to determine the optimal weight coefficients. • The proposed method has the best performance compared with other methods

  19. Optimization of multi-environment trials for genomic selection based on crop models.

    Science.gov (United States)

    Rincent, R; Kuhn, E; Monod, H; Oury, F-X; Rousset, M; Allard, V; Le Gouis, J

    2017-08-01

    We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the environments composing this MET used for calibration. The objective of this study was to determine a method to optimize the set of environments composing the MET for estimating genetic parameters in this context. A criterion called OptiMET was defined to this aim, and was evaluated on simulated and real data, with the example of wheat phenology. The MET defined with OptiMET allowed estimating the genetic parameters with lower error, leading to higher QTL detection power and higher prediction accuracies. MET defined with OptiMET was on average more efficient than random MET composed of twice as many environments, in terms of quality of the parameter estimates. OptiMET is thus a valuable tool to determine optimal experimental conditions to best exploit MET and the phenotyping tools that are currently developed.

  20. A new enhanced index tracking model in portfolio optimization with sum weighted approach

    Science.gov (United States)

    Siew, Lam Weng; Jaaman, Saiful Hafizah; Hoe, Lam Weng

    2017-04-01

    Index tracking is a portfolio management which aims to construct the optimal portfolio to achieve similar return with the benchmark index return at minimum tracking error without purchasing all the stocks that make up the index. Enhanced index tracking is an improved portfolio management which aims to generate higher portfolio return than the benchmark index return besides minimizing the tracking error. The objective of this paper is to propose a new enhanced index tracking model with sum weighted approach to improve the existing index tracking model for tracking the benchmark Technology Index in Malaysia. The optimal portfolio composition and performance of both models are determined and compared in terms of portfolio mean return, tracking error and information ratio. The results of this study show that the optimal portfolio of the proposed model is able to generate higher mean return than the benchmark index at minimum tracking error. Besides that, the proposed model is able to outperform the existing model in tracking the benchmark index. The significance of this study is to propose a new enhanced index tracking model with sum weighted apporach which contributes 67% improvement on the portfolio mean return as compared to the existing model.

  1. Rethinking exchange market models as optimization algorithms

    Science.gov (United States)

    Luquini, Evandro; Omar, Nizam

    2018-02-01

    The exchange market model has mainly been used to study the inequality problem. Although the human society inequality problem is very important, the exchange market models dynamics until stationary state and its capability of ranking individuals is interesting in itself. This study considers the hypothesis that the exchange market model could be understood as an optimization procedure. We present herein the implications for algorithmic optimization and also the possibility of a new family of exchange market models

  2. Optimization method to determine mass transfer variables in a PWR crud deposition risk assessment tool

    International Nuclear Information System (INIS)

    Do, Chuong; Hussey, Dennis; Wells, Daniel M.; Epperson, Kenny

    2016-01-01

    Optimization numerical method was implemented to determine several mass transfer coefficients in a crud-induced power shift risk assessment code. The approach was to utilize a multilevel strategy that targets different model parameters that first changes the major order variables, mass transfer inputs, then calibrates the minor order variables, crud source terms, according to available plant data. In this manner, the mass transfer inputs are effectively simplified as 'dependent' on the crud source terms. Two optimization studies were performed using DAKOTA, a design and analysis toolkit, with the difference between the runs, being the number of model runs using BOA, allowed for adjusting the crud source terms, therefore, reducing the uncertainty with calibration. The result of the first case showed that the current best estimated values for the mass transfer coefficients, which were derived from first principle analysis, can be considered an optimized set. When the run limit of BOA was increased for the second case, an improvement in the prediction was obtained with the results deviating slightly from the best estimated values. (author)

  3. Pareto-Optimal Model Selection via SPRINT-Race.

    Science.gov (United States)

    Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2018-02-01

    In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.

  4. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design-Part I. Model development

    Energy Technology Data Exchange (ETDEWEB)

    He, L., E-mail: li.he@ryerson.ca [Department of Civil Engineering, Faculty of Engineering, Architecture and Science, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3 (Canada); Huang, G.H. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada); College of Urban Environmental Sciences, Peking University, Beijing 100871 (China); Lu, H.W. [Environmental Systems Engineering Program, Faculty of Engineering, University of Regina, Regina, Saskatchewan, S4S 0A2 (Canada)

    2010-04-15

    Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the 'true' ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes.

  5. Optimal input shaping for Fisher identifiability of control-oriented lithium-ion battery models

    Science.gov (United States)

    Rothenberger, Michael J.

    This dissertation examines the fundamental challenge of optimally shaping input trajectories to maximize parameter identifiability of control-oriented lithium-ion battery models. Identifiability is a property from information theory that determines the solvability of parameter estimation for mathematical models using input-output measurements. This dissertation creates a framework that exploits the Fisher information metric to quantify the level of battery parameter identifiability, optimizes this metric through input shaping, and facilitates faster and more accurate estimation. The popularity of lithium-ion batteries is growing significantly in the energy storage domain, especially for stationary and transportation applications. While these cells have excellent power and energy densities, they are plagued with safety and lifespan concerns. These concerns are often resolved in the industry through conservative current and voltage operating limits, which reduce the overall performance and still lack robustness in detecting catastrophic failure modes. New advances in automotive battery management systems mitigate these challenges through the incorporation of model-based control to increase performance, safety, and lifespan. To achieve these goals, model-based control requires accurate parameterization of the battery model. While many groups in the literature study a variety of methods to perform battery parameter estimation, a fundamental issue of poor parameter identifiability remains apparent for lithium-ion battery models. This fundamental challenge of battery identifiability is studied extensively in the literature, and some groups are even approaching the problem of improving the ability to estimate the model parameters. The first approach is to add additional sensors to the battery to gain more information that is used for estimation. The other main approach is to shape the input trajectories to increase the amount of information that can be gained from input

  6. Stochastic modelling of turbulent combustion for design optimization of gas turbine combustors

    Science.gov (United States)

    Mehanna Ismail, Mohammed Ali

    The present work covers the development and the implementation of an efficient algorithm for the design optimization of gas turbine combustors. The purpose is to explore the possibilities and indicate constructive suggestions for optimization techniques as alternative methods for designing gas turbine combustors. The algorithm is general to the extent that no constraints are imposed on the combustion phenomena or on the combustor configuration. The optimization problem is broken down into two elementary problems: the first is the optimum search algorithm, and the second is the turbulent combustion model used to determine the combustor performance parameters. These performance parameters constitute the objective and physical constraints in the optimization problem formulation. The examination of both turbulent combustion phenomena and the gas turbine design process suggests that the turbulent combustion model represents a crucial part of the optimization algorithm. The basic requirements needed for a turbulent combustion model to be successfully used in a practical optimization algorithm are discussed. In principle, the combustion model should comply with the conflicting requirements of high fidelity, robustness and computational efficiency. To that end, the problem of turbulent combustion is discussed and the current state of the art of turbulent combustion modelling is reviewed. According to this review, turbulent combustion models based on the composition PDF transport equation are found to be good candidates for application in the present context. However, these models are computationally expensive. To overcome this difficulty, two different models based on the composition PDF transport equation were developed: an improved Lagrangian Monte Carlo composition PDF algorithm and the generalized stochastic reactor model. Improvements in the Lagrangian Monte Carlo composition PDF model performance and its computational efficiency were achieved through the

  7. Optimal velocity difference model for a car-following theory

    International Nuclear Information System (INIS)

    Peng, G.H.; Cai, X.H.; Liu, C.Q.; Cao, B.F.; Tuo, M.X.

    2011-01-01

    In this Letter, we present a new optimal velocity difference model for a car-following theory based on the full velocity difference model. The linear stability condition of the new model is obtained by using the linear stability theory. The unrealistically high deceleration does not appear in OVDM. Numerical simulation of traffic dynamics shows that the new model can avoid the disadvantage of negative velocity occurred at small sensitivity coefficient λ in full velocity difference model by adjusting the coefficient of the optimal velocity difference, which shows that collision can disappear in the improved model. -- Highlights: → A new optimal velocity difference car-following model is proposed. → The effects of the optimal velocity difference on the stability of traffic flow have been explored. → The starting and braking process were carried out through simulation. → The effects of the optimal velocity difference can avoid the disadvantage of negative velocity.

  8. A model for optimizing the production of pharmaceutical products

    Directory of Open Access Journals (Sweden)

    Nevena Gospodinova

    2017-05-01

    Full Text Available The problem associated with the optimal production planning is especially relevant in modern industrial enterprises. The most commonly used optimality criteria in this context are: maximizing the total profit; minimizing the cost per unit of production; maximizing the capacity utilization; minimizing the total production costs. This article aims to explore the possibility for optimizing the production of pharmaceutical products through the construction of a mathematical model that can be viewed in two ways – as a single-product model and a multi-product model. As an optimality criterion it is set the minimization of the cost per unit of production for a given planning period. The author proposes an analytical method for solving the nonlinear optimization problem. An optimal production plan of Tylosin tartrate is found using the single-product model.

  9. OPTIMIZATION OF LAND USE SUITABILITY FOR AGRICULTURE USING INTEGRATED GEOSPATIAL MODEL AND GENETIC ALGORITHMS

    Directory of Open Access Journals (Sweden)

    S. B. Mansor

    2012-08-01

    Full Text Available In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

  10. Fuzzy Simulation-Optimization Model for Waste Load Allocation

    Directory of Open Access Journals (Sweden)

    Motahhare Saadatpour

    2006-01-01

    Full Text Available This paper present simulation-optimization models for waste load allocation from multiple point sources which include uncertainty due to vagueness of the parameters and goals. This model employs fuzzy sets with appropriate membership functions to deal with uncertainties due to vagueness. The fuzzy waste load allocation model (FWLAM incorporate QUAL2E as a water quality simulation model and Genetic Algorithm (GA as an optimization tool to find the optimal combination of the fraction removal level to the dischargers and pollution control agency (PCA. Penalty functions are employed to control the violations in the system.  The results demonstrate that the goal of PCA to achieve the best water quality and the goal of the dischargers to use the full assimilative capacity of the river have not been satisfied completely and a compromise solution between these goals is provided. This fuzzy optimization model with genetic algorithm has been used for a hypothetical problem. Results demonstrate a very suitable convergence of proposed optimization algorithm to the global optima.

  11. A risk-based multi-objective model for optimal placement of sensors in water distribution system

    Science.gov (United States)

    Naserizade, Sareh S.; Nikoo, Mohammad Reza; Montaseri, Hossein

    2018-02-01

    In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation-optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value

  12. A Goal Programming Optimization Model for The Allocation of Liquid Steel Production

    Science.gov (United States)

    Hapsari, S. N.; Rosyidi, C. N.

    2018-03-01

    This research was conducted in one of the largest steel companies in Indonesia which has several production units and produces a wide range of steel products. One of the important products in the company is billet steel. The company has four Electric Arc Furnace (EAF) which produces liquid steel which must be procesed further to be billet steel. The billet steel plant needs to make their production process more efficient to increase the productvity. The management has four goals to be achieved and hence the optimal allocation of the liquid steel production is needed to achieve those goals. In this paper, a goal programming optimization model is developed to determine optimal allocation of liquid steel production in each EAF, to satisfy demand in 3 periods and the company goals, namely maximizing the volume of production, minimizing the cost of raw materials, minimizing maintenance costs, maximizing sales revenues, and maximizing production capacity. From the results of optimization, only maximizing production capacity goal can not achieve the target. However, the model developed in this papare can optimally allocate liquid steel so the allocation of production does not exceed the maximum capacity of the machine work hours and maximum production capacity.

  13. Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

    Science.gov (United States)

    Janardhanan, S.; Datta, B.

    2011-12-01

    Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of

  14. Application of genetic algorithm in radio ecological models parameter determination

    Energy Technology Data Exchange (ETDEWEB)

    Pantelic, G. [Institute of Occupatioanl Health and Radiological Protection ' Dr Dragomir Karajovic' , Belgrade (Serbia)

    2006-07-01

    The method of genetic algorithms was used to determine the biological half-life of 137 Cs in cow milk after the accident in Chernobyl. Methodologically genetic algorithms are based on the fact that natural processes tend to optimize themselves and therefore this method should be more efficient in providing optimal solutions in the modeling of radio ecological and environmental events. The calculated biological half-life of 137 Cs in milk is (32 {+-} 3) days and transfer coefficient from grass to milk is (0.019 {+-} 0.005). (authors)

  15. Hierarchical Swarm Model: A New Approach to Optimization

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2010-01-01

    Full Text Available This paper presents a novel optimization model called hierarchical swarm optimization (HSO, which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O, based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.

  16. A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

    Science.gov (United States)

    He, L; Huang, G H; Lu, H W

    2010-04-15

    Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

  17. Capacitated vehicle-routing problem model for scheduled solid waste collection and route optimization using PSO algorithm.

    Science.gov (United States)

    Hannan, M A; Akhtar, Mahmuda; Begum, R A; Basri, H; Hussain, A; Scavino, Edgar

    2018-01-01

    Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Layout optimization of DRAM cells using rigorous simulation model for NTD

    Science.gov (United States)

    Jeon, Jinhyuck; Kim, Shinyoung; Park, Chanha; Yang, Hyunjo; Yim, Donggyu; Kuechler, Bernd; Zimmermann, Rainer; Muelders, Thomas; Klostermann, Ulrich; Schmoeller, Thomas; Do, Mun-hoe; Choi, Jung-Hoe

    2014-03-01

    DRAM chip space is mainly determined by the size of the memory cell array patterns which consist of periodic memory cell features and edges of the periodic array. Resolution Enhancement Techniques (RET) are used to optimize the periodic pattern process performance. Computational Lithography such as source mask optimization (SMO) to find the optimal off axis illumination and optical proximity correction (OPC) combined with model based SRAF placement are applied to print patterns on target. For 20nm Memory Cell optimization we see challenges that demand additional tool competence for layout optimization. The first challenge is a memory core pattern of brick-wall type with a k1 of 0.28, so it allows only two spectral beams to interfere. We will show how to analytically derive the only valid geometrically limited source. Another consequence of two-beam interference limitation is a "super stable" core pattern, with the advantage of high depth of focus (DoF) but also low sensitivity to proximity corrections or changes of contact aspect ratio. This makes an array edge correction very difficult. The edge can be the most critical pattern since it forms the transition from the very stable regime of periodic patterns to non-periodic periphery, so it combines the most critical pitch and highest susceptibility to defocus. Above challenge makes the layout correction to a complex optimization task demanding a layout optimization that finds a solution with optimal process stability taking into account DoF, exposure dose latitude (EL), mask error enhancement factor (MEEF) and mask manufacturability constraints. This can only be achieved by simultaneously considering all criteria while placing and sizing SRAFs and main mask features. The second challenge is the use of a negative tone development (NTD) type resist, which has a strong resist effect and is difficult to characterize experimentally due to negative resist profile taper angles that perturb CD at bottom characterization by

  19. A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

    Science.gov (United States)

    Moghaddam, Kamran S.; Usher, John S.

    2011-07-01

    In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

  20. A model for an economically optimal replacement of a breeder flock.

    Science.gov (United States)

    Yassin, Hurria; Velthuis, Annet G J; Giesen, Gerard W J; Oude Lansink, Alfons G J M

    2012-12-01

    A deterministic model is developed to support the tactical and operational replacement decisions at broiler breeder farms. The marginal net revenue approach is applied to determine the optimal replacement age of a flock. The objective function of the model maximizes the annual gross margin over the flock's production cycle. To calculate the gross margin, future egg production, fertility, or hatchability of the eggs, revenues and variable costs of a flock were estimated. For tactical decisions, the optimal laying length is the age at which the average gross margin of an average flock is maximal. For operational decisions, a flock should be replaced when the marginal gross margin of a replaceable flock is less than the average gross margin of an average flock. To demonstrate the model, a broiler breeder flock from a Dutch breeder farm was used. A sensitivity analysis showed that the optimal replacement decision, for both tactical and operational management, is sensitive to the decrease in the weekly egg production after the peak and the prices of feed and hatching eggs. The effect of the decrease in weekly fertility after the peak on the replacement decision is related to the payment system for hatching eggs.

  1. Heuristic Optimization Techniques for Determining Optimal Reserve Structure of Power Generating Systems

    DEFF Research Database (Denmark)

    Ding, Yi; Goel, Lalit; Wang, Peng

    2012-01-01

    cost of the system will also increase. The reserve structure of a MSS should be determined based on striking a balance between the required reliability and the reserve cost. The objective of reserve management for a MSS is to schedule the reserve at the minimum system reserve cost while maintaining......Electric power generating systems are typical examples of multi-state systems (MSS). Sufficient reserve is critically important for maintaining generating system reliabilities. The reliability of a system can be increased by increasing the reserve capacity, noting that at the same time the reserve...... the required level of supply reliability to its customers. In previous research, Genetic Algorithm (GA) has been used to solve most reliability optimization problems. However, the GA is not very computationally efficient in some cases. In this chapter a new heuristic optimization technique—the particle swarm...

  2. Creative Destruction and Optimal Patent Life in a Variety-Expanding Growth Model

    OpenAIRE

    Lin, Hwan C.

    2013-01-01

    This paper presents more channels through which the optimal patent life is determined in a R&D-based endogenous growth model that permits growth of new varieties of consumer goods over time. Its modeling features include an endogenous hazard rate facing incumbent monopolists, the prevalence of research congestion, and the aggregate welfare importance of product differentiation. As a result, a patent’s effective life is endogenized and less than its legal life. The model is calibrated to a glo...

  3. Optimization model of energy mix taking into account the environmental impact

    International Nuclear Information System (INIS)

    Gruenwald, O.; Oprea, D.

    2012-01-01

    At present, the energy system in the Czech Republic needs to decide some important issues regarding limited fossil resources, greater efficiency in producing of electrical energy and reducing emission levels of pollutants. These problems can be decided only by formulating and implementing an energy mix that will meet these conditions: rational, reliable, sustainable and competitive. The aim of this article is to find a new way of determining an optimal mix for the energy system in the Czech Republic. To achieve the aim, the linear optimization model comprising several economics, environmental and technical aspects will be applied. (Authors)

  4. Pavement maintenance optimization model using Markov Decision Processes

    Science.gov (United States)

    Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.

    2017-09-01

    This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.

  5. Maintenance Optimization of High Voltage Substation Model

    Directory of Open Access Journals (Sweden)

    Radim Bris

    2008-01-01

    Full Text Available The real system from practice is selected for optimization purpose in this paper. We describe the real scheme of a high voltage (HV substation in different work states. Model scheme of the HV substation 22 kV is demonstrated within the paper. The scheme serves as input model scheme for the maintenance optimization. The input reliability and cost parameters of all components are given: the preventive and corrective maintenance costs, the actual maintenance period (being optimized, the failure rate and mean time to repair - MTTR.

  6. A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies.

    Science.gov (United States)

    Foo, Lee Kien; McGree, James; Duffull, Stephen

    2012-01-01

    Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models. Copyright © 2012 John Wiley & Sons, Ltd.

  7. Modeling and optimization of wet sizing process

    International Nuclear Information System (INIS)

    Thai Ba Cau; Vu Thanh Quang and Nguyen Ba Tien

    2004-01-01

    Mathematical simulation on basis of Stock law has been done for wet sizing process on cylinder equipment of laboratory and semi-industrial scale. The model consists of mathematical equations describing relations between variables, such as: - Resident time distribution function of emulsion particles in the separating zone of the equipment depending on flow-rate, height, diameter and structure of the equipment. - Size-distribution function in the fine and coarse parts depending on resident time distribution function of emulsion particles, characteristics of the material being processed, such as specific density, shapes, and characteristics of the environment of classification, such as specific density, viscosity. - Experimental model was developed on data collected from an experimental cylindrical equipment with diameter x height of sedimentation chamber equal to 50 x 40 cm for an emulsion of zirconium silicate in water. - Using this experimental model allows to determine optimal flow-rate in order to obtain product with desired grain size in term of average size or size distribution function. (author)

  8. Modelling and Optimal Control of Typhoid Fever Disease with Cost-Effective Strategies.

    Science.gov (United States)

    Tilahun, Getachew Teshome; Makinde, Oluwole Daniel; Malonza, David

    2017-01-01

    We propose and analyze a compartmental nonlinear deterministic mathematical model for the typhoid fever outbreak and optimal control strategies in a community with varying population. The model is studied qualitatively using stability theory of differential equations and the basic reproductive number that represents the epidemic indicator is obtained from the largest eigenvalue of the next-generation matrix. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined. The model exhibits a forward transcritical bifurcation and the sensitivity analysis is performed. The optimal control problem is designed by applying Pontryagin maximum principle with three control strategies, namely, the prevention strategy through sanitation, proper hygiene, and vaccination; the treatment strategy through application of appropriate medicine; and the screening of the carriers. The cost functional accounts for the cost involved in prevention, screening, and treatment together with the total number of the infected persons averted. Numerical results for the typhoid outbreak dynamics and its optimal control revealed that a combination of prevention and treatment is the best cost-effective strategy to eradicate the disease.

  9. Hierarchical models and iterative optimization of hybrid systems

    Energy Technology Data Exchange (ETDEWEB)

    Rasina, Irina V. [Ailamazyan Program Systems Institute, Russian Academy of Sciences, Peter One str. 4a, Pereslavl-Zalessky, 152021 (Russian Federation); Baturina, Olga V. [Trapeznikov Control Sciences Institute, Russian Academy of Sciences, Profsoyuznaya str. 65, 117997, Moscow (Russian Federation); Nasatueva, Soelma N. [Buryat State University, Smolina str.24a, Ulan-Ude, 670000 (Russian Federation)

    2016-06-08

    A class of hybrid control systems on the base of two-level discrete-continuous model is considered. The concept of this model was proposed and developed in preceding works as a concretization of the general multi-step system with related optimality conditions. A new iterative optimization procedure for such systems is developed on the base of localization of the global optimality conditions via contraction the control set.

  10. Modeling and Optimizing Antennas for Rotational Spectroscopy Applications

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-12-01

    Full Text Available In the paper, dielectric and metallic lenses are modeled and optimized in order to enhance the gain of a horn antenna in the frequency range from 60 GHz to 100 GHz. Properties of designed lenses are compared and discussed. The structures are modeled in CST Microwave Studio and optimized by Particle Swarm Optimization (PSO in order to get required antenna parameters.

  11. Optimization of automation: III. Development of optimization method for determining automation rate in nuclear power plants

    International Nuclear Information System (INIS)

    Lee, Seung Min; Kim, Jong Hyun; Kim, Man Cheol; Seong, Poong Hyun

    2016-01-01

    Highlights: • We propose an appropriate automation rate that enables the best human performance. • We analyze the shortest working time considering Situation Awareness Recovery (SAR). • The optimized automation rate is estimated by integrating the automation and ostracism rate estimation methods. • The process to derive the optimized automation rate is demonstrated through case studies. - Abstract: Automation has been introduced in various industries, including the nuclear field, because it is commonly believed that automation promises greater efficiency, lower workloads, and fewer operator errors through reducing operator errors and enhancing operator and system performance. However, the excessive introduction of automation has deteriorated operator performance due to the side effects of automation, which are referred to as Out-of-the-Loop (OOTL), and this is critical issue that must be resolved. Thus, in order to determine the optimal level of automation introduction that assures the best human operator performance, a quantitative method of optimizing the automation is proposed in this paper. In order to propose the optimization method for determining appropriate automation levels that enable the best human performance, the automation rate and ostracism rate, which are estimation methods that quantitatively analyze the positive and negative effects of automation, respectively, are integrated. The integration was conducted in order to derive the shortest working time through considering the concept of situation awareness recovery (SAR), which states that the automation rate with the shortest working time assures the best human performance. The process to derive the optimized automation rate is demonstrated through an emergency operation scenario-based case study. In this case study, four types of procedures are assumed through redesigning the original emergency operating procedure according to the introduced automation and ostracism levels. Using the

  12. A study on a new algorithm to optimize ball mill system based on modeling and GA

    International Nuclear Information System (INIS)

    Wang Heng; Jia Minping; Huang Peng; Chen Zuoliang

    2010-01-01

    Aiming at the disadvantage of conventional optimization method for ball mill pulverizing system, a novel approach based on RBF neural network and genetic algorithm was proposed in the present paper. Firstly, the experiments and measurement for fill level based on vibration signals of mill shell was introduced. Then, main factors which affected the power consumption of ball mill pulverizing system were analyzed, and the input variables of RBF neural network were determined. RBF neural network was used to map the complex non-linear relationship between the electric consumption and process parameters and the non-linear model of power consumption was built. Finally, the model was optimized by genetic algorithm and the optimal work conditions of ball mill pulverizing system were determined. The results demonstrate that the method is reliable and practical, and can reduce the electric consumption obviously and effectively.

  13. A study on an optimal movement model

    Energy Technology Data Exchange (ETDEWEB)

    Feng Jianfeng [COGS, Sussex University, Brighton BN1 9QH, UK (United Kingdom); Zhang, Kewei [SMS, Sussex University, Brighton BN1 9QH (United Kingdom); Luo Yousong [Department of Mathematics and Statistics, RMIT University, GOP Box 2476V, Melbourne, Vic 3001 (Australia)

    2003-07-11

    We present an analytical and rigorous study on a TOPS (task optimization in the presence of signal-dependent noise) model with a hold-on or an end-point control. Optimal control signals are rigorously obtained, which enables us to investigate various issues about the model including its trajectories, velocities, control signals, variances and the dependence of these quantities on various model parameters. With the hold-on control, we find that the optimal control can be implemented with an almost 'nil' hold-on period. The optimal control signal is a linear combination of two sub-control signals. One of the sub-control signals is positive and the other is negative. With the end-point control, the end-point variance is dramatically reduced, in comparison with the hold-on control. However, the velocity is not symmetric (bell shape). Finally, we point out that the velocity with a hold-on control takes the bell shape only within a limited parameter region.

  14. Application of genetic algorithm in radio ecological models parameter determination

    International Nuclear Information System (INIS)

    Pantelic, G.

    2006-01-01

    The method of genetic algorithms was used to determine the biological half-life of 137 Cs in cow milk after the accident in Chernobyl. Methodologically genetic algorithms are based on the fact that natural processes tend to optimize themselves and therefore this method should be more efficient in providing optimal solutions in the modeling of radio ecological and environmental events. The calculated biological half-life of 137 Cs in milk is (32 ± 3) days and transfer coefficient from grass to milk is (0.019 ± 0.005). (authors)

  15. OPTIMIZING CONDITIONS FOR SPECTROPHOTOMETRIC DETERMINATION OF TOTAL POLYPHENOLS IN WINES USING FOLIN-CIOCALTEU REAGENT

    Directory of Open Access Journals (Sweden)

    Daniel Bajčan

    2013-02-01

    Full Text Available Wine is a complex beverage that obtains its properties mainly due to synergistic effect of alcohol, organic acids, arbohydrates, as well as the phenolic and aromatic substances. At present days, we can observe an increased interest in the study of polyphenols in wines that have antioxidant, antimicrobial, anti-inflammatory, anti-cancer and many other beneficial effects. Moderate and regular consumption of the red wine especially, with a high content of phenolic compounds, has a beneficial effect on human health. The aim of this work was to optimize conditions for spectrophotometric determination of total polyphenols in winwas to optimize conditions for spectrophotometric determination of total polyphenols in winwas to optimize conditions for spectrophotometric determination of total polyphenols in winwas to optimize conditions for pectrophotometric determination of total polyphenols in wine using Folin-Ciocaulteu reagent. Based on several studies, in order to minimize chemical use and optimize analysis time, we have proposed a method for the determination of total polyphenols using 0.25 ml Folin-Ciocaulteu reagent, 3 ml of 20% Na2CO3 solution and time of coloring complex 1.5 hour. We f

  16. Is the Linear Modeling Technique Good Enough for Optimal Form Design? A Comparison of Quantitative Analysis Models

    Science.gov (United States)

    Lin, Yang-Cheng; Yeh, Chung-Hsing; Wang, Chen-Cheng; Wei, Chun-Chun

    2012-01-01

    How to design highly reputable and hot-selling products is an essential issue in product design. Whether consumers choose a product depends largely on their perception of the product image. A consumer-oriented design approach presented in this paper helps product designers incorporate consumers' perceptions of product forms in the design process. The consumer-oriented design approach uses quantification theory type I, grey prediction (the linear modeling technique), and neural networks (the nonlinear modeling technique) to determine the optimal form combination of product design for matching a given product image. An experimental study based on the concept of Kansei Engineering is conducted to collect numerical data for examining the relationship between consumers' perception of product image and product form elements of personal digital assistants (PDAs). The result of performance comparison shows that the QTTI model is good enough to help product designers determine the optimal form combination of product design. Although the PDA form design is used as a case study, the approach is applicable to other consumer products with various design elements and product images. The approach provides an effective mechanism for facilitating the consumer-oriented product design process. PMID:23258961

  17. Optimizing Real-Time Vaccine Allocation in a Stochastic SIR Model.

    Directory of Open Access Journals (Sweden)

    Chantal Nguyen

    Full Text Available Real-time vaccination following an outbreak can effectively mitigate the damage caused by an infectious disease. However, in many cases, available resources are insufficient to vaccinate the entire at-risk population, logistics result in delayed vaccine deployment, and the interaction between members of different cities facilitates a wide spatial spread of infection. Limited vaccine, time delays, and interaction (or coupling of cities lead to tradeoffs that impact the overall magnitude of the epidemic. These tradeoffs mandate investigation of optimal strategies that minimize the severity of the epidemic by prioritizing allocation of vaccine to specific subpopulations. We use an SIR model to describe the disease dynamics of an epidemic which breaks out in one city and spreads to another. We solve a master equation to determine the resulting probability distribution of the final epidemic size. We then identify tradeoffs between vaccine, time delay, and coupling, and we determine the optimal vaccination protocols resulting from these tradeoffs.

  18. A Framework for the Optimization of Discrete-Event Simulation Models

    Science.gov (United States)

    Joshi, B. D.; Unal, R.; White, N. H.; Morris, W. D.

    1996-01-01

    With the growing use of computer modeling and simulation, in all aspects of engineering, the scope of traditional optimization has to be extended to include simulation models. Some unique aspects have to be addressed while optimizing via stochastic simulation models. The optimization procedure has to explicitly account for the randomness inherent in the stochastic measures predicted by the model. This paper outlines a general purpose framework for optimization of terminating discrete-event simulation models. The methodology combines a chance constraint approach for problem formulation, together with standard statistical estimation and analyses techniques. The applicability of the optimization framework is illustrated by minimizing the operation and support resources of a launch vehicle, through a simulation model.

  19. Modified Chaos Particle Swarm Optimization-Based Optimized Operation Model for Stand-Alone CCHP Microgrid

    Directory of Open Access Journals (Sweden)

    Fei Wang

    2017-07-01

    Full Text Available The optimized dispatch of different distributed generations (DGs in stand-alone microgrid (MG is of great significance to the operation’s reliability and economy, especially for energy crisis and environmental pollution. Based on controllable load (CL and combined cooling-heating-power (CCHP model of micro-gas turbine (MT, a multi-objective optimization model with relevant constraints to optimize the generation cost, load cut compensation and environmental benefit is proposed in this paper. The MG studied in this paper consists of photovoltaic (PV, wind turbine (WT, fuel cell (FC, diesel engine (DE, MT and energy storage (ES. Four typical scenarios were designed according to different day types (work day or weekend and weather conditions (sunny or rainy in view of the uncertainty of renewable energy in variable situations and load fluctuation. A modified dispatch strategy for CCHP is presented to further improve the operation economy without reducing the consumers’ comfort feeling. Chaotic optimization and elite retention strategy are introduced into basic particle swarm optimization (PSO to propose modified chaos particle swarm optimization (MCPSO whose search capability and convergence speed are improved greatly. Simulation results validate the correctness of the proposed model and the effectiveness of MCPSO algorithm in the optimized operation application of stand-alone MG.

  20. One approach to determining the optimal coefficient of slenderness scraping and tool materials when processing on a lathe for known machine

    Directory of Open Access Journals (Sweden)

    Pejović Branko B.

    2014-01-01

    Full Text Available Starting from the expression for the thrust in the field of full utilization cross section scrapings and tool life, an expression is derived for the maximum required thrust of universal machine. Then, using a working diagram the analysis of the main features of the simultaneous utilization of machines was performed and determined the optimal area of its utilization for given optimal diameter of treatment. Based on this, the well-known machine using its structural details and the corresponding function workability, derived relations to determine the optimal coefficient slenderness of scrapings suitable for practical use. In this case we think that is known critical material for work piece. Considering the critical and authoritative material of the work piece, based on the expression for the cutting speed was determined by the characteristic constant workability as the basis for establishing optimum tool material which is adequate for optimum regime. Both obtained relation can be considered as general model that can be applied directly to solving setting problems. Also, given the possibilities of practical application of the presented relations, especially in the case of other typical kinds of treatment. Finally, the model is verified on a one calculation example from practice for a Specific machine tool, where certain important characteristics of the optimal treatment are defined.

  1. Optimizing a gap conductance model applicable to VVER-1000 thermal–hydraulic model

    International Nuclear Information System (INIS)

    Rahgoshay, M.; Hashemi-Tilehnoee, M.

    2012-01-01

    Highlights: ► Two known conductance models for application in VVER-1000 thermal–hydraulic code are examined. ► An optimized gap conductance model is developed which can predict the gap conductance in good agreement with FSAR data. ► The licensed thermal–hydraulic code is coupled with the gap conductance model predictor externally. -- Abstract: The modeling of gap conductance for application in VVER-1000 thermal–hydraulic codes is addressed. Two known models, namely CALZA-BINI and RELAP5 gap conductance models, are examined. By externally linking of gap conductance models and COBRA-EN thermal hydraulic code, the acceptable range of each model is specified. The result of each gap conductance model versus linear heat rate has been compared with FSAR data. A linear heat rate of about 9 kW/m is the boundary for optimization process. Since each gap conductance model has its advantages and limitation, the optimized gap conductance model can predict the gap conductance better than each of the two other models individually.

  2. PEMILIHAN SAHAM YANG OPTIMAL MENGGUNAKAN CAPITAL ASSET PRICING MODEL (CAPM

    Directory of Open Access Journals (Sweden)

    Dioda Ardi Wibisono

    2017-08-01

    Full Text Available Optimal portfolio is the basis for investors to invest in stock. Capital Asset Pricing Model (CAPM is a method to determine the value of the risk and return of a company stock. This research uses a secondary data from the closing price of the monthly stock price (monthly closing price, Stock Price Index (SPI, and the monthly SBI rate. The samples of this research are 41 stocks in LQ45 February-July 2015 on the Indonesian Stock Exchange (ISE. The study period is during 5 year from October 2010 - October 2015. The result of analysis shows that the optimal portfolio consists of 18 companies. The average return of the optimal portfolio is higher than the average risk-free return (SBI rate and the average market return. This proves that investing in stocks is more profitable than a risk-free investment. � Keywords: Stock, CAPM, return, risk�

  3. Problems in determining the optimal use of road safety measures

    DEFF Research Database (Denmark)

    Elvik, Rune

    2014-01-01

    for intervention that ensures maximum safety benefits. The third problem is how to develop policy options to minimise the risk of indivisibilities and irreversible choices. The fourth problem is how to account for interaction effects between road safety measures when determining their optimal use. The fifth......This paper discusses some problems in determining the optimal use of road safety measures. The first of these problems is how best to define the baseline option, i.e. what will happen if no new safety measures are introduced. The second problem concerns choice of a method for selection of targets...... problem is how to obtain the best mix of short-term and long-term measures in a safety programme. The sixth problem is how fixed parameters for analysis, including the monetary valuation of road safety, influence the results of analyses. It is concluded that it is at present not possible to determine...

  4. Optimization model for the design of distributed wastewater treatment networks

    Directory of Open Access Journals (Sweden)

    Ibrić Nidret

    2012-01-01

    Full Text Available In this paper we address the synthesis problem of distributed wastewater networks using mathematical programming approach based on the superstructure optimization. We present a generalized superstructure and optimization model for the design of the distributed wastewater treatment networks. The superstructure includes splitters, treatment units, mixers, with all feasible interconnections including water recirculation. Based on the superstructure the optimization model is presented. The optimization model is given as a nonlinear programming (NLP problem where the objective function can be defined to minimize the total amount of wastewater treated in treatment operations or to minimize the total treatment costs. The NLP model is extended to a mixed integer nonlinear programming (MINLP problem where binary variables are used for the selection of the wastewater treatment technologies. The bounds for all flowrates and concentrations in the wastewater network are specified as general equations. The proposed models are solved using the global optimization solvers (BARON and LINDOGlobal. The application of the proposed models is illustrated on the two wastewater network problems of different complexity. First one is formulated as the NLP and the second one as the MINLP. For the second one the parametric and structural optimization is performed at the same time where optimal flowrates, concentrations as well as optimal technologies for the wastewater treatment are selected. Using the proposed model both problems are solved to global optimality.

  5. Empty tracks optimization based on Z-Map model

    Science.gov (United States)

    Liu, Le; Yan, Guangrong; Wang, Zaijun; Zang, Genao

    2017-12-01

    For parts with many features, there are more empty tracks during machining. If these tracks are not optimized, the machining efficiency will be seriously affected. In this paper, the characteristics of the empty tracks are studied in detail. Combining with the existing optimization algorithm, a new tracks optimization method based on Z-Map model is proposed. In this method, the tool tracks are divided into the unit processing section, and then the Z-Map model simulation technique is used to analyze the order constraint between the unit segments. The empty stroke optimization problem is transformed into the TSP with sequential constraints, and then through the genetic algorithm solves the established TSP problem. This kind of optimization method can not only optimize the simple structural parts, but also optimize the complex structural parts, so as to effectively plan the empty tracks and greatly improve the processing efficiency.

  6. DETERMINATION OF OPTIMAL CONTOURS OF OPEN PIT MINE DURING OIL SHALE EXPLOITATION, BY MINEX 5.2.3. PROGRAM

    Directory of Open Access Journals (Sweden)

    Miroslav Ignjatović

    2013-04-01

    Full Text Available By examination and determination of optimal solution of technological processes of exploitation and oil shale processing from Aleksinac site and with adopted technical solution and exploitation of oil shale, derived a technical solution that optimize contour of the newly defined open pit mine. In the world, this problem is solved by using a computer program that has become the established standard for quick and efficient solution for this problem. One of the computer’s program, which can be used for determination of the optimal contours of open pit mines is Minex 5.2.3. program, produced in Australia in the Surpac Minex Group Pty Ltd Company, which is applied at the Mining and Metallurgy Institute Bor (no. of licenses are SSI - 24765 and SSI - 24766. In this study, authors performed 11 optimization of deposit geo - models in Minex 5.2.3. based on the tests results, performed in a laboratory for soil mechanics of Mining and Metallurgy Institute, Bor, on samples from the site of Aleksinac deposits.

  7. Statistical surrogate model based sampling criterion for stochastic global optimization of problems with constraints

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Su Gil; Jang, Jun Yong; Kim, Ji Hoon; Lee, Tae Hee [Hanyang University, Seoul (Korea, Republic of); Lee, Min Uk [Romax Technology Ltd., Seoul (Korea, Republic of); Choi, Jong Su; Hong, Sup [Korea Research Institute of Ships and Ocean Engineering, Daejeon (Korea, Republic of)

    2015-04-15

    Sequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiency of commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacks because there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to select the points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowest objective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundary sampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located within extremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergence properties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.

  8. Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

    Science.gov (United States)

    Chiadamrong, N.; Piyathanavong, V.

    2017-12-01

    Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

  9. Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence

    Science.gov (United States)

    Nur Asmawisham Alel, Mohd; Ruben Anak Upom, Mark; Asnida Abdullah, Rini; Hazreek Zainal Abidin, Mohd

    2018-04-01

    Air overpressure (AOp) resulting from blasting can cause damage and nuisance to nearby civilians. Thus, it is important to be able to predict AOp accurately. In this study, 8 different Artificial Neural Network (ANN) were developed for the purpose of prediction of AOp. The ANN models were trained using different variants of Particle Swarm Optimization (PSO) algorithm. AOp predictions were also made using an empirical equation, as suggested by United States Bureau of Mines (USBM), to serve as a benchmark. In order to develop the models, 76 blasting operations in Hulu Langat were investigated. All the ANN models were found to outperform the USBM equation in three performance metrics; root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). Using a performance ranking method, MSO-Rand-Mut was determined to be the best prediction model for AOp with a performance metric of RMSE=2.18, MAPE=1.73% and R2=0.97. The result shows that ANN models trained using PSO are capable of predicting AOp with great accuracy.

  10. Optimal treatment interruptions control of TB transmission model

    Science.gov (United States)

    Nainggolan, Jonner; Suparwati, Titik; Kawuwung, Westy B.

    2018-03-01

    A tuberculosis model which incorporates treatment interruptions of infectives is established. Optimal control of individuals infected with active TB is given in the model. It is obtained that the control reproduction numbers is smaller than the reproduction number, this means treatment controls could optimize the decrease in the spread of active TB. For this model, controls on treatment of infection individuals to reduce the actively infected individual populations, by application the Pontryagins Maximum Principle for optimal control. The result further emphasized the importance of controlling disease relapse in reducing the number of actively infected and treatment interruptions individuals with tuberculosis.

  11. Modeling, simulation and optimization of bipedal walking

    CERN Document Server

    Berns, Karsten

    2013-01-01

    The model-based investigation of motions of anthropomorphic systems is an important interdisciplinary research topic involving specialists from many fields such as Robotics, Biomechanics, Physiology, Orthopedics, Psychology, Neurosciences, Sports, Computer Graphics and Applied Mathematics. This book presents a study of basic locomotion forms such as walking and running is of particular interest due to the high demand on dynamic coordination, actuator efficiency and balance control. Mathematical models and numerical simulation and optimization techniques are explained, in combination with experimental data, which can help to better understand the basic underlying mechanisms of these motions and to improve them. Example topics treated in this book are Modeling techniques for anthropomorphic bipedal walking systems Optimized walking motions for different objective functions Identification of objective functions from measurements Simulation and optimization approaches for humanoid robots Biologically inspired con...

  12. Computer models for optimizing radiation therapy

    International Nuclear Information System (INIS)

    Duechting, W.

    1998-01-01

    The aim of this contribution is to outline how methods of system analysis, control therapy and modelling can be applied to simulate normal and malignant cell growth and to optimize cancer treatment as for instance radiation therapy. Based on biological observations and cell kinetic data, several types of models have been developed describing the growth of tumor spheroids and the cell renewal of normal tissue. The irradiation model is represented by the so-called linear-quadratic model describing the survival fraction as a function of the dose. Based thereon, numerous simulation runs for different treatment schemes can be performed. Thus, it is possible to study the radiation effect on tumor and normal tissue separately. Finally, this method enables a computer-assisted recommendation for an optimal patient-specific treatment schedule prior to clinical therapy. (orig.) [de

  13. Modeling and optimization of LCD optical performance

    CERN Document Server

    Yakovlev, Dmitry A; Kwok, Hoi-Sing

    2015-01-01

    The aim of this book is to present the theoretical foundations of modeling the optical characteristics of liquid crystal displays, critically reviewing modern modeling methods and examining areas of applicability. The modern matrix formalisms of optics of anisotropic stratified media, most convenient for solving problems of numerical modeling and optimization of LCD, will be considered in detail. The benefits of combined use of the matrix methods will be shown, which generally provides the best compromise between physical adequacy and accuracy with computational efficiency and optimization fac

  14. Transport Routes Optimization Model Through Application of Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Ivan Bortas

    2018-03-01

    Full Text Available The transport policy of the European Union is based on the mission of restructuring road traffic into other and energy-favourable transport modes which have not been sufficiently represented yet. Therefore, the development of the inland waterway and rail transport, and connectivity in the intermodal transport network are development planning priorities of the European transport strategy. The aim of this research study was to apply the scientific methodology and thus analyse the factors that affect the distribution of the goods flows and by using the fuzzy logic to make an optimization model, according to the criteria of minimizing the costs and negative impact on the environment, for the selection of the optimal transport route. Testing of the model by simulation, was performed on the basis of evaluating the criteria of the influential parameters with unprecise and indefinite input parameters. The testing results show that by the distribution of the goods flow from road transport network to inland waterways or rail transport, can be predicted in advance and determine the transport route with optimal characteristics. The results of the performed research study will be used to improve the process of planning the transport service, with the aim of reducing the transport costs and environmental pollution.

  15. Two-phase strategy of controlling motor coordination determined by task performance optimality.

    Science.gov (United States)

    Shimansky, Yury P; Rand, Miya K

    2013-02-01

    A quantitative model of optimal coordination between hand transport and grip aperture has been derived in our previous studies of reach-to-grasp movements without utilizing explicit knowledge of the optimality criterion or motor plant dynamics. The model's utility for experimental data analysis has been demonstrated. Here we show how to generalize this model for a broad class of reaching-type, goal-directed movements. The model allows for measuring the variability of motor coordination and studying its dependence on movement phase. The experimentally found characteristics of that dependence imply that execution noise is low and does not affect motor coordination significantly. From those characteristics it is inferred that the cost of neural computations required for information acquisition and processing is included in the criterion of task performance optimality as a function of precision demand for state estimation and decision making. The precision demand is an additional optimized control variable that regulates the amount of neurocomputational resources activated dynamically. It is shown that an optimal control strategy in this case comprises two different phases. During the initial phase, the cost of neural computations is significantly reduced at the expense of reducing the demand for their precision, which results in speed-accuracy tradeoff violation and significant inter-trial variability of motor coordination. During the final phase, neural computations and thus motor coordination are considerably more precise to reduce the cost of errors in making a contact with the target object. The generality of the optimal coordination model and the two-phase control strategy is illustrated on several diverse examples.

  16. Optimal time points sampling in pathway modelling.

    Science.gov (United States)

    Hu, Shiyan

    2004-01-01

    Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

  17. DETERMINATION OF THE OPTIMAL CAPITAL INVESTMENTS TO ENSURE THE SUSTAINABLE DEVELOPMENT OF THE RAILWAY

    Directory of Open Access Journals (Sweden)

    O. I. Kharchenko

    2015-04-01

    Full Text Available Purpose. Every year more attention is paid for the theoretical and practical issue of sustainable development of railway transport. But today the mechanisms of financial support of this development are poorly understood. Therefore, the aim of this article is to determine the optimal investment allocation to ensure sustainable development of the railway transport on the example of State Enterprise «Prydniprovsk Railway» and the creation of preconditions for the mathematical model development. Methodology. The ensuring task for sustainable development of railway transport is solved on the basis of the integral indicator of sustainable development effectiveness and defined as the maximization of this criterion. The optimization of measures technological and technical characters are proposed to carry out for increasing values of the integral performance measure components. To the optimization activities of technological nature that enhance the performance criteria belongs: optimization of the number of train and shunting locomotives, optimization of power handling mechanisms at the stations, optimization of routes of train flows. The activities related to the technical nature include: modernization of railways in the direction of their electrification and modernization of the running gear and coupler drawbars of rolling stock, as well as means of separators mechanization at stations to reduce noise impacts on the environment. Findings. The work resulted in the optimal allocation of investments to ensure the sustainable development of railway transportation of State Enterprise «Prydniprovsk Railway». This allows providing such kind of railway development when functioning of State Enterprise «Prydniprovsk Railway» is characterized by a maximum value of the integral indicator of efficiency. Originality. The work was reviewed and the new approach was proposed to determine the optimal allocation of capital investments to ensure sustainable

  18. Optimization in Fuzzy Economic Order Quantity (FEOQ Model with Deteriorating Inventory and Units Lost

    Directory of Open Access Journals (Sweden)

    Monalisha Pattnaik

    2014-09-01

    Full Text Available Background: This model presents the effect of deteriorating items in fuzzy optimal instantaneous replenishment for finite planning horizon. Accounting for holding cost per unit per unit time and ordering cost per order have traditionally been the case of modeling inventory systems in fuzzy environment. These imprecise parameters defined on a bounded interval on the axis of real numbers and the physical characteristics of stocked items dictate the nature of inventory policies implemented to manage and control in the production system.   Methods: The modified fuzzy EOQ (FEOQ model is introduced, it assumes that a percentage of the on-hand inventory is wasted due to deterioration and considered as an enhancement to EOQ model to determine the optimal replenishment quantity so that the net profit is maximized. In theoretical analysis, the necessary and sufficient conditions of the existence and uniqueness of the optimal solutions are proved and further the concavity of the fuzzy net profit function is established. Computational algorithm using the software LINGO 13.0 version is developed to find the optimal solution.   Results and conclusions: The results of the numerical analysis enable decision-makers to quantify the effect of units lost due to deterioration on optimizing the fuzzy net profit for the retailer. Finally, sensitivity analyses of the optimal solution with respect the major parameters are also carried out. Furthermore fuzzy decision making is shown to be superior then crisp decision making in terms of profit maximization. 

  19. Modelling and optimization of environmental conditions for kefiran production by Lactobacillus kefiranofaciens.

    Science.gov (United States)

    Cheirsilp, B; Shimizu, H; Shioya, S

    2001-12-01

    A mathematical model for kefiran production by Lactobacillus kefiranofaciens was established, in which the effects of pH, substrate and product on cell growth, exopolysaccharide formation and substrate assimilation were considered. The model gave a good representation both of the formation of exopolysaccharides (which are not only attached to cells but also released into the medium) and of the time courses of the production of galactose and glucose in the medium (which are produced and consumed by the cells). Since pH and both lactose and lactic acid concentrations differently affected production and growth activity, the model included the effects of pH and the concentrations of lactose and lactic acid. Based on the mathematical model, an optimal pH profile for the maximum production of kefiran in batch culture was obtained. In this study, a simplified optimization method was developed, in which the optimal pH profile was determined at a particular final fermentation time. This was based on the principle that, at a certain time, switching from the maximum specific growth rate to the critical one (which yields the maximum specific production rate) results in maximum production. Maximum kefiran production was obtained, which was 20% higher than that obtained in the constant-pH control fermentation. A genetic algorithm (GA) was also applied to obtain the optimal pH profile; and it was found that practically the same solution was obtained using the GA.

  20. A feasibility investigation for modeling and optimization of temperature in bone drilling using fuzzy logic and Taguchi optimization methodology.

    Science.gov (United States)

    Pandey, Rupesh Kumar; Panda, Sudhansu Sekhar

    2014-11-01

    Drilling of bone is a common procedure in orthopedic surgery to produce hole for screw insertion to fixate the fracture devices and implants. The increase in temperature during such a procedure increases the chances of thermal invasion of bone which can cause thermal osteonecrosis resulting in the increase of healing time or reduction in the stability and strength of the fixation. Therefore, drilling of bone with minimum temperature is a major challenge for orthopedic fracture treatment. This investigation discusses the use of fuzzy logic and Taguchi methodology for predicting and minimizing the temperature produced during bone drilling. The drilling experiments have been conducted on bovine bone using Taguchi's L25 experimental design. A fuzzy model is developed for predicting the temperature during orthopedic drilling as a function of the drilling process parameters (point angle, helix angle, feed rate and cutting speed). Optimum bone drilling process parameters for minimizing the temperature are determined using Taguchi method. The effect of individual cutting parameters on the temperature produced is evaluated using analysis of variance. The fuzzy model using triangular and trapezoidal membership predicts the temperature within a maximum error of ±7%. Taguchi analysis of the obtained results determined the optimal drilling conditions for minimizing the temperature as A3B5C1.The developed system will simplify the tedious task of modeling and determination of the optimal process parameters to minimize the bone drilling temperature. It will reduce the risk of thermal osteonecrosis and can be very effective for the online condition monitoring of the process. © IMechE 2014.

  1. Optimal hedging with the cointegrated vector autoregressive model

    DEFF Research Database (Denmark)

    Gatarek, Lukasz; Johansen, Søren

    We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated with the...

  2. Simulation-optimization model of reservoir operation based on target storage curves

    Directory of Open Access Journals (Sweden)

    Hong-bin Fang

    2014-10-01

    Full Text Available This paper proposes a new storage allocation rule based on target storage curves. Joint operating rules are also proposed to solve the operation problems of a multi-reservoir system with joint demands and water transfer-supply projects. The joint operating rules include a water diversion rule to determine the amount of diverted water in a period, a hedging rule based on an aggregated reservoir to determine the total release from the system, and a storage allocation rule to specify the release from each reservoir. A simulation-optimization model was established to optimize the key points of the water diversion curves, the hedging rule curves, and the target storage curves using the improved particle swarm optimization (IPSO algorithm. The multi-reservoir water supply system located in Liaoning Province, China, including a water transfer-supply project, was employed as a case study to verify the effectiveness of the proposed join operating rules and target storage curves. The results indicate that the proposed operating rules are suitable for the complex system. The storage allocation rule based on target storage curves shows an improved performance with regard to system storage distribution.

  3. A cost optimization model for 100% renewable residential energy supply systems

    DEFF Research Database (Denmark)

    Milan, Christian; Bojesen, Carsten; Nielsen, Mads Pagh

    2012-01-01

    The concept of net zero energy buildings (Net ZEB) has received increased attention throughout the last years. A well adapted and optimized design of the energy supply system is crucial for the performance of these buildings. To achieve this, a holistic approach is needed which accounts for the i......The concept of net zero energy buildings (Net ZEB) has received increased attention throughout the last years. A well adapted and optimized design of the energy supply system is crucial for the performance of these buildings. To achieve this, a holistic approach is needed which accounts......'s involving on-site production of heat and electricity in combination with electricity exchanged with the public grid. The model is based on linear programming and determines the optimal capacities for each relevant supply technology in terms of the overall system costs. It has been successfully applied...

  4. Parametrical Method for Determining Optimal Ship Carrying Capacity and Performance of Handling Equipment

    Directory of Open Access Journals (Sweden)

    Michalski Jan P.

    2016-04-01

    Full Text Available The paper presents a method of evaluating the optimal value of the cargo ships deadweight and the coupled optimal value of cargo handling capacity. The method may be useful at the stage of establishing the main owners requirements concerning the ship design parameters as well as for choosing a proper second hand ship for a given transportation task. The deadweight and the capacity are determined on the basis of a selected economic measure of the transport effectiveness of ship – the Required Freight Rate. The mathematical model of the problem is of a deterministic character and the simplifying assumptions are justified for ships operating in the liner trade. The assumptions are so selected that solution of the problem is obtained in analytical closed form. The presented method can be useful for application in the preliminary ship design or in the simulation of pre-investment transportation task studies.

  5. Modeling and Optimization of a CoolingTower-Assisted Heat Pump System

    Directory of Open Access Journals (Sweden)

    Xiaoqing Wei

    2017-05-01

    Full Text Available To minimize the total energy consumption of a cooling tower-assisted heat pump (CTAHP system in cooling mode, a model-based control strategy with hybrid optimization algorithm for the system is presented in this paper. An existing experimental device, which mainly contains a closed wet cooling tower with counter flow construction, a condenser water loop and a water-to-water heat pump unit, is selected as the study object. Theoretical and empirical models of the related components and their interactions are developed. The four variables, viz. desired cooling load, ambient wet-bulb temperature, temperature and flow rate of chilled water at the inlet of evaporator, are set to independent variables. The system power consumption can be minimized by optimizing input powers of cooling tower fan, spray water pump, condenser water pump and compressor. The optimal input power of spray water pump is determined experimentally. Implemented on MATLAB, a hybrid optimization algorithm, which combines the Limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS algorithm with the greedy diffusion search (GDS algorithm, is incorporated to solve the minimization problem of energy consumption and predict the system’s optimal set-points under quasi-steady-state conditions. The integrated simulation tool is validated against experimental data. The results obtained demonstrate the proposed operation strategy is reliable, and can save energy by 20.8% as compared to an uncontrolled system under certain testing conditions.

  6. Optimization and evaluation of probabilistic-logic sequence models

    DEFF Research Database (Denmark)

    Christiansen, Henning; Lassen, Ole Torp

    to, in principle, Turing complete languages. In general, such models are computationally far to complex for direct use, so optimization by pruning and approximation are needed. % The first steps are made towards a methodology for optimizing such models by approximations using auxiliary models......Analysis of biological sequence data demands more and more sophisticated and fine-grained models, but these in turn introduce hard computational problems. A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and context-free languages...

  7. METHODS FOR DETERMINATION AND OPTIMIZATION OF LOGISTICS COSTS

    OpenAIRE

    Mihaela STET

    2016-01-01

    The paper is dealing with the problems of logistics costs, highlighting some methods for estimation and determination of specific costs for different transport modes in freight distribution. There are highlighted, besides costs of transports, the other costs in supply chain, as well as costing methods used in logistics activities. In this context, there are also revealed some optimization means of transport costs in logistics chain.

  8. MARKOV CHAIN PORTFOLIO LIQUIDITY OPTIMIZATION MODEL

    Directory of Open Access Journals (Sweden)

    Eder Oliveira Abensur

    2014-05-01

    Full Text Available The international financial crisis of September 2008 and May 2010 showed the importance of liquidity as an attribute to be considered in portfolio decisions. This study proposes an optimization model based on available public data, using Markov chain and Genetic Algorithms concepts as it considers the classic duality of risk versus return and incorporating liquidity costs. The work intends to propose a multi-criterion non-linear optimization model using liquidity based on a Markov chain. The non-linear model was tested using Genetic Algorithms with twenty five Brazilian stocks from 2007 to 2009. The results suggest that this is an innovative development methodology and useful for developing an efficient and realistic financial portfolio, as it considers many attributes such as risk, return and liquidity.

  9. Optimal Power Flow Modelling and Analysis of Hybrid AC-DC Grids with Offshore Wind Power Plant

    DEFF Research Database (Denmark)

    Dhua, Debasish; Huang, Shaojun; Wu, Qiuwei

    2017-01-01

    In order to develop renewables based energy systems, the installation of the offshore wind power plants (WPPs) is globally encouraged. However, wind power generation is intermittent and uncertain. An accurate modelling and evaluation reduces investment and provide better operation. Hence......, the wind power production level also plays a major role in a hybrid system on transmission loss evaluation. The developed model is tested in Low, Medium and High wind power production levels to determine the objective function of the OPF solution. MATLAB Optimization Toolbox and MATLAB script are used......, it is essential to develop a suitable model and apply optimization algorithms for different application scenarios. The objective of this work is to develop a generalized model and evaluate the Optimal Power Flow (OPF) solutions in a hybrid AC/DC system including HVDC (LCC based) and offshore WPP (VSC based...

  10. Combining spatial modeling and choice experiments for the optimal spatial allocation of wind turbines

    International Nuclear Information System (INIS)

    Drechsler, Martin; Ohl, Cornelia; Meyerhoff, Juergen; Eichhorn, Marcus; Monsees, Jan

    2011-01-01

    Although wind power is currently the most efficient source of renewable energy, the installation of wind turbines (WT) in landscapes often leads to conflicts in the affected communities. We propose that such conflicts can be mitigated by a welfare-optimal spatial allocation of WT in the landscape so that a given energy target is reached at minimum social costs. The energy target is motivated by the fact that wind power production is associated with relatively low CO 2 emissions. Social costs comprise energy production costs as well as external costs caused by harmful impacts on humans and biodiversity. We present a modeling approach that combines spatially explicit ecological-economic modeling and choice experiments to determine the welfare-optimal spatial allocation of WT in West Saxony, Germany. The welfare-optimal sites balance production and external costs. Results indicate that in the welfare-optimal allocation the external costs represent about 14% of the total costs (production costs plus external costs). Optimizing wind power production without consideration of the external costs would lead to a very different allocation of WT that would marginally reduce the production costs but strongly increase the external costs and thus lead to substantial welfare losses. - Highlights: → We combine modeling and economic valuation to optimally allocate wind turbines. → Welfare-optimal allocation balances energy production costs and external costs. → External costs (impacts on the environment) can be substantial. → Ignoring external costs leads to suboptimal allocations and welfare losses.

  11. Optimal Model-Based Control in HVAC Systems

    DEFF Research Database (Denmark)

    Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik

    2008-01-01

    is developed. Then the optimal control structure is designed and implemented. The HVAC system is splitted into two subsystems. By selecting the right set-points and appropriate cost functions for each subsystem controller the optimal control strategy is respected to gaurantee the minimum thermal and electrical......This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...... energy consumption. Finally, the controller is applied to control the mentioned HVAC system and the results show that the expected goals are fulfilled....

  12. Flow injection analysis simulations and diffusion coefficient determination by stochastic and deterministic optimization methods.

    Science.gov (United States)

    Kucza, Witold

    2013-07-25

    Stochastic and deterministic simulations of dispersion in cylindrical channels on the Poiseuille flow have been presented. The random walk (stochastic) and the uniform dispersion (deterministic) models have been used for computations of flow injection analysis responses. These methods coupled with the genetic algorithm and the Levenberg-Marquardt optimization methods, respectively, have been applied for determination of diffusion coefficients. The diffusion coefficients of fluorescein sodium, potassium hexacyanoferrate and potassium dichromate have been determined by means of the presented methods and FIA responses that are available in literature. The best-fit results agree with each other and with experimental data thus validating both presented approaches. Copyright © 2013 The Author. Published by Elsevier B.V. All rights reserved.

  13. Support Vector Data Description Model to Map Specific Land Cover with Optimal Parameters Determined from a Window-Based Validation Set

    Directory of Open Access Journals (Sweden)

    Jinshui Zhang

    2017-04-01

    Full Text Available This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD, to determine optimal parameters for support vector data description (SVDD model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C and kernel width (s, in mapping homogeneous specific land cover.

  14. Experimental Modeling of Monolithic Resistors for Silicon ICS with a Robust Optimizer-Driving Scheme

    Directory of Open Access Journals (Sweden)

    Philippe Leduc

    2002-06-01

    Full Text Available Today, an exhaustive library of models describing the electrical behavior of integrated passive components in the radio-frequency range is essential for the simulation and optimization of complex circuits. In this work, a preliminary study has been done on Tantalum Nitride (TaN resistors integrated on silicon, and this leads to a single p-type lumped-element circuit. An efficient extraction technique will be presented to provide a computer-driven optimizer with relevant initial model parameter values (the "guess-timate". The results show the unicity in most cases of the lumped element determination, which leads to a precise simulation of self-resonant frequencies.

  15. Discrete Optimization Model for Vehicle Routing Problem with Scheduling Side Cosntraints

    Science.gov (United States)

    Juliandri, Dedy; Mawengkang, Herman; Bu'ulolo, F.

    2018-01-01

    Vehicle Routing Problem (VRP) is an important element of many logistic systems which involve routing and scheduling of vehicles from a depot to a set of customers node. This is a hard combinatorial optimization problem with the objective to find an optimal set of routes used by a fleet of vehicles to serve the demands a set of customers It is required that these vehicles return to the depot after serving customers’ demand. The problem incorporates time windows, fleet and driver scheduling, pick-up and delivery in the planning horizon. The goal is to determine the scheduling of fleet and driver and routing policies of the vehicles. The objective is to minimize the overall costs of all routes over the planning horizon. We model the problem as a linear mixed integer program. We develop a combination of heuristics and exact method for solving the model.

  16. Determining the optimal spacing of deepening of vertical mine

    Energy Technology Data Exchange (ETDEWEB)

    Durov, Ye.M.

    1983-01-01

    Light is shed on a technique for determining the optimal spacing of shafts for deepening for the examined parameters of operational and deepening operations. The presented results of studies may be used in designing new shafts, in preparing levels and in reconstruction of existing shafts with slanted and steep stratum bedding.

  17. Analysis and optimization of a camber morphing wing model

    Directory of Open Access Journals (Sweden)

    Bing Li

    2016-09-01

    Full Text Available This article proposes a camber morphing wing model that can continuously change its camber. A mathematical model is proposed and a kinematic simulation is performed to verify the wing’s ability to change camber. An aerodynamic model is used to test its aerodynamic characteristics. Some important aerodynamic analyses are performed. A comparative analysis is conducted to explore the relationships between aerodynamic parameters, the rotation angle of the trailing edge, and the angle of attack. An improved artificial fish swarm optimization algorithm is proposed, referred to as the weighted adaptive artificial fish-swarm with embedded Hooke–Jeeves search method. Some comparison tests are used to test the performance of the improved optimization algorithm. Finally, the proposed optimization algorithm is used to optimize the proposed camber morphing wing model.

  18. Modeling and energy efficiency optimization of belt conveyors

    International Nuclear Information System (INIS)

    Zhang, Shirong; Xia, Xiaohua

    2011-01-01

    Highlights: → We take optimization approach to improve operation efficiency of belt conveyors. → An analytical energy model, originating from ISO 5048, is proposed. → Then an off-line and an on-line parameter estimation schemes are investigated. → In a case study, six optimization problems are formulated with solutions in simulation. - Abstract: The improvement of the energy efficiency of belt conveyor systems can be achieved at equipment and operation levels. Specifically, variable speed control, an equipment level intervention, is recommended to improve operation efficiency of belt conveyors. However, the current implementations mostly focus on lower level control loops without operational considerations at the system level. This paper intends to take a model based optimization approach to improve the efficiency of belt conveyors at the operational level. An analytical energy model, originating from ISO 5048, is firstly proposed, which lumps all the parameters into four coefficients. Subsequently, both an off-line and an on-line parameter estimation schemes are applied to identify the new energy model, respectively. Simulation results are presented for the estimates of the four coefficients. Finally, optimization is done to achieve the best operation efficiency of belt conveyors under various constraints. Six optimization problems of a typical belt conveyor system are formulated, respectively, with solutions in simulation for a case study.

  19. Optimization, Monotonicity and the Determination of Nash Equilibria — An Algorithmic Analysis

    Science.gov (United States)

    Lozovanu, D.; Pickl, S. W.; Weber, G.-W.

    2004-08-01

    This paper is concerned with the optimization of a nonlinear time-discrete model exploiting the special structure of the underlying cost game and the property of inverse matrices. The costs are interlinked by a system of linear inequalities. It is shown that, if the players cooperate, i.e., minimize the sum of all the costs, they achieve a Nash equilibrium. In order to determine Nash equilibria, the simplex method can be applied with respect to the dual problem. An introduction into the TEM model and its relationship to an economic Joint Implementation program is given. The equivalence problem is presented. The construction of the emission cost game and the allocation problem is explained. The assumption of inverse monotony for the matrices leads to a new result in the area of such allocation problems. A generalization of such problems is presented.

  20. Model reduction for dynamic real-time optimization of chemical processes

    NARCIS (Netherlands)

    Van den Berg, J.

    2005-01-01

    The value of models in process industries becomes apparent in practice and literature where numerous successful applications are reported. Process models are being used for optimal plant design, simulation studies, for off-line and online process optimization. For online optimization applications

  1. Optimization of DNA Sensor Model Based Nanostructured Graphene Using Particle Swarm Optimization Technique

    Directory of Open Access Journals (Sweden)

    Hediyeh Karimi

    2013-01-01

    Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.

  2. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

    Energy Technology Data Exchange (ETDEWEB)

    Bellary, Sayed Ahmed Imran; Samad, Abdus [Indian Institute of Technology Madras, Chennai (India); Husain, Afzal [Sultan Qaboos University, Al-Khoudh (Oman)

    2014-12-15

    The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

  3. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

    International Nuclear Information System (INIS)

    Bellary, Sayed Ahmed Imran; Samad, Abdus; Husain, Afzal

    2014-01-01

    The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

  4. METHODS FOR DETERMINATION AND OPTIMIZATION OF LOGISTICS COSTS

    Directory of Open Access Journals (Sweden)

    Mihaela STET

    2016-12-01

    Full Text Available The paper is dealing with the problems of logistics costs, highlighting some methods for estimation and determination of specific costs for different transport modes in freight distribution. There are highlighted, besides costs of transports, the other costs in supply chain, as well as costing methods used in logistics activities. In this context, there are also revealed some optimization means of transport costs in logistics chain.

  5. An Analytical Planning Model to Estimate the Optimal Density of Charging Stations for Electric Vehicles.

    Directory of Open Access Journals (Sweden)

    Yongjun Ahn

    Full Text Available The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station's density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive

  6. An Analytical Planning Model to Estimate the Optimal Density of Charging Stations for Electric Vehicles.

    Science.gov (United States)

    Ahn, Yongjun; Yeo, Hwasoo

    2015-01-01

    The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC) stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station's density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive adoption of electric

  7. Efficient Iris Localization via Optimization Model

    Directory of Open Access Journals (Sweden)

    Qi Wang

    2017-01-01

    Full Text Available Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

  8. Spatial Model for Determining the Optimum Placement of Logistics Centers in a Predefined Economic Area

    Directory of Open Access Journals (Sweden)

    Ramona Iulia Țarțavulea (Dieaconescu

    2016-08-01

    Full Text Available The process of globalization has stimulated the demand for logistics services at a level of speed and increased efficiency, which involves using of techniques, tools, technologies and modern models in supply chain management. The aim of this research paper is to present a model that can be used in order to achieve an optimized supply chain, associated with minimum transportation costs. The utilization of spatial modeling for determining the optimal locations for logistics centers in a predefined economic area is proposd in this paper. The principal methods used to design the model are mathematic optimization and linear programming. The output data of the model are the precise placement of one up to ten logistics centers, in terms of minimum operational costs for delivery from the optimum locations to consumer points. The results of the research indicate that by using the proposed model, an efficient supply chain that is consistent with optimization of transport can be designed, in order to streamline the delivery process and thus reduce operational costs

  9. Adaptive surrogate model based multiobjective optimization for coastal aquifer management

    Science.gov (United States)

    Song, Jian; Yang, Yun; Wu, Jianfeng; Wu, Jichun; Sun, Xiaomin; Lin, Jin

    2018-06-01

    In this study, a novel surrogate model assisted multiobjective memetic algorithm (SMOMA) is developed for optimal pumping strategies of large-scale coastal groundwater problems. The proposed SMOMA integrates an efficient data-driven surrogate model with an improved non-dominated sorted genetic algorithm-II (NSGAII) that employs a local search operator to accelerate its convergence in optimization. The surrogate model based on Kernel Extreme Learning Machine (KELM) is developed and evaluated as an approximate simulator to generate the patterns of regional groundwater flow and salinity levels in coastal aquifers for reducing huge computational burden. The KELM model is adaptively trained during evolutionary search to satisfy desired fidelity level of surrogate so that it inhibits error accumulation of forecasting and results in correctly converging to true Pareto-optimal front. The proposed methodology is then applied to a large-scale coastal aquifer management in Baldwin County, Alabama. Objectives of minimizing the saltwater mass increase and maximizing the total pumping rate in the coastal aquifers are considered. The optimal solutions achieved by the proposed adaptive surrogate model are compared against those solutions obtained from one-shot surrogate model and original simulation model. The adaptive surrogate model does not only improve the prediction accuracy of Pareto-optimal solutions compared with those by the one-shot surrogate model, but also maintains the equivalent quality of Pareto-optimal solutions compared with those by NSGAII coupled with original simulation model, while retaining the advantage of surrogate models in reducing computational burden up to 94% of time-saving. This study shows that the proposed methodology is a computationally efficient and promising tool for multiobjective optimizations of coastal aquifer managements.

  10. Optimal policies for cumulative damage models with maintenance last and first

    International Nuclear Information System (INIS)

    Zhao, Xufeng; Qian, Cunhua; Nakagawa, Toshio

    2013-01-01

    From the economical viewpoint of several combined PM policies in reliability theory, this paper takes up a standard cumulative damage model in which the notion of maintenance last is applied, i.e., the unit undergoes preventive maintenances before failure at a planned time T, at a damage level Z, or at a shock number N, whichever occurs last. Expected cost rates are detailedly formulated, and optimal problems of two alternative policies which combine time-based with condition-based preventive maintenances are discussed, i.e., optimal T L ⁎ for N, Z L ⁎ for T, and N L ⁎ for T are rigorously obtained. Comparison methods between such maintenance last and conventional maintenance first are explored. It is determined theoretically and numerically which policy should be adopted, according to the different methods in different cases when the time-based or the condition-based PM policy is optimized.

  11. The State Fiscal Policy: Determinants and Optimization of Financial Flows

    Directory of Open Access Journals (Sweden)

    Sitash Tetiana D.

    2017-03-01

    Full Text Available The article outlines the determinants of the state fiscal policy at the present stage of global transformations. Using the principles of financial science it is determined that regulation of financial flows within the fiscal sphere, namely centralization and redistribution of the GDP, which results in the regulation of the financial capacity of economic agents, is of importance. It is emphasized that the urgent measure for improving the tax model is re-considering the provision of fiscal incentives, which are used to stimulate the accumulation of capital, investment activity, innovation, increase of the competitiveness of national products, expansion of exports, increase of the level of the population employment. The necessity of applying the instruments of fiscal regulation of financial flows, which should take place on the basis of institutional economics emphasizing the analysis of institutional changes, the evolution of institutions and their impact on the behavior of participants of economic relations. At the same time it is determined that the maximum effect of fiscal regulation of financial flows is ensured when application of fiscal instruments is aimed not only at achieving the target values of parameters of financial flows but at overcoming institutional deformations as well. It is determined that the optimal movement of financial flows enables creating favorable conditions for development and maintenance of financial balance in the society and achievement of the necessary level of competitiveness of the national economy.

  12. Constrained optimization via simulation models for new product innovation

    Science.gov (United States)

    Pujowidianto, Nugroho A.

    2017-11-01

    We consider the problem of constrained optimization where the decision makers aim to optimize the primary performance measure while constraining the secondary performance measures. This paper provides a brief overview of stochastically constrained optimization via discrete event simulation. Most review papers tend to be methodology-based. This review attempts to be problem-based as decision makers may have already decided on the problem formulation. We consider constrained optimization models as there are usually constraints on secondary performance measures as trade-off in new product development. It starts by laying out different possible methods and the reasons using constrained optimization via simulation models. It is then followed by the review of different simulation optimization approach to address constrained optimization depending on the number of decision variables, the type of constraints, and the risk preferences of the decision makers in handling uncertainties.

  13. Research on the decision-making model of land-use spatial optimization

    Science.gov (United States)

    He, Jianhua; Yu, Yan; Liu, Yanfang; Liang, Fei; Cai, Yuqiu

    2009-10-01

    Using the optimization result of landscape pattern and land use structure optimization as constraints of CA simulation results, a decision-making model of land use spatial optimization is established coupled the landscape pattern model with cellular automata to realize the land use quantitative and spatial optimization simultaneously. And Huangpi district is taken as a case study to verify the rationality of the model.

  14. A model of optimal voluntary muscular control.

    Science.gov (United States)

    FitzHugh, R

    1977-07-19

    In the absence of detailed knowledge of how the CNS controls a muscle through its motor fibers, a reasonable hypothesis is that of optimal control. This hypothesis is studied using a simplified mathematical model of a single muscle, based on A.V. Hill's equations, with series elastic element omitted, and with the motor signal represented by a single input variable. Two cost functions were used. The first was total energy expended by the muscle (work plus heat). If the load is a constant force, with no inertia, Hill's optimal velocity of shortening results. If the load includes a mass, analysis by optimal control theory shows that the motor signal to the muscle consists of three phases: (1) maximal stimulation to accelerate the mass to the optimal velocity as quickly as possible, (2) an intermediate level of stimulation to hold the velocity at its optimal value, once reached, and (3) zero stimulation, to permit the mass to slow down, as quickly as possible, to zero velocity at the specified distance shortened. If the latter distance is too small, or the mass too large, the optimal velocity is not reached, and phase (2) is absent. For lengthening, there is no optimal velocity; there are only two phases, zero stimulation followed by maximal stimulation. The second cost function was total time. The optimal control for shortening consists of only phases (1) and (3) above, and is identical to the minimal energy control whenever phase (2) is absent from the latter. Generalization of this model to include viscous loads and a series elastic element are discussed.

  15. Determining the optimal mix of federal and contract fire crews: a case study from the Pacific Northwest.

    Science.gov (United States)

    Geoffrey H. Donovan

    2006-01-01

    Federal land management agencies in the United States are increasingly relying on contract crews as opposed to agency fire crews. Despite this increasing reliance on contractors, there have been no studies to determine what the optimal mix of contract and agency fire crews should be. A mathematical model is presented to address this question and is applied to a case...

  16. A Simulation-Optimization Model for Seawater Intrusion Management at Pingtung Coastal Area, Taiwan

    Directory of Open Access Journals (Sweden)

    Po-Syun Huang

    2018-02-01

    Full Text Available The coastal regions of Pingtung Plain in southern Taiwan rely on groundwater as their main source of fresh water for aquaculture, agriculture, domestic, and industrial sectors. The availability of fresh groundwater is threatened by unsustainable groundwater extraction and the over-pumpage leads to the serious problem of seawater intrusion. It is desired to find appropriate management strategies to control groundwater salinity and mitigate seawater intrusion. In this study, a simulation–optimization model has been presented to solve the problem of seawater intrusion along the coastal aquifers in Pingtung Plain and the objective is using injection well barriers and minimizing the total injection rate based on the pre-determined locations of injection barriers. The SEAWAT code is used to simulate the process of seawater intrusion and the surrogate model of artificial neural networks (ANNs is used to approximate the seawater intrusion (SWI numerical model to increase the computational efficiency during the optimization process. The heuristic optimization scheme of differential evolution (DE algorithm is selected to identify the global optimal management solution. Two different management scenarios, one is the injection barriers located along the coast and the other is the injection barrier located at the inland, are considered and the optimized results show that the deployment of injection barriers at the inland is more effective to reduce total dissolved solids (TDS concentrations and mitigate seawater intrusion than that along the coast. The computational time can be reduced by more than 98% when using ANNs to replace the numerical model and the DE algorithm has been confirmed as a robust optimization scheme to solve groundwater management problems. The proposed framework can identify the most reliable management strategies and provide a reference tool for decision making with regard to seawater intrusion remediation.

  17. An optimization model to agroindustrial sector in antioquia (Colombia, South America)

    Science.gov (United States)

    Fernandez, J.

    2015-06-01

    This paper develops a proposal of a general optimization model for the flower industry, which is defined by using discrete simulation and nonlinear optimization, whose mathematical models have been solved by using ProModel simulation tools and Gams optimization. It defines the operations that constitute the production and marketing of the sector, statistically validated data taken directly from each operation through field work, the discrete simulation model of the operations and the linear optimization model of the entire industry chain are raised. The model is solved with the tools described above and presents the results validated in a case study.

  18. Development of optimized dosimetric models for HDR brachytherapy

    International Nuclear Information System (INIS)

    Thayalan, K.; Jagadeesan, M.

    2003-01-01

    High dose rate brachytherapy (HDRB) systems are in clinical use for more than four decades particularly in cervical cancer. Optimization is the method to produce dose distribution which assures that doses are not compromised at the treatment sites whilst reducing the risk of overdosing critical organs. Hence HDRB optimization begins with the desired dose distribution and requires the calculations of the relative weighting factors for each dwell position with out changing the source activity. The optimization for Ca. uterine cervix treatment is simply duplication of the dose distribution used for Low dose rate (LDR) applications. In the present work, two optimized dosimetric models were proposed and studied thoroughly, to suit the local clinical conditions. These models are named as HDR-C and HDR-D, where C and D represent configuration and distance respectively. These models duplicate exactly the LDR pear shaped dose distribution, which is a golden standard. The validity of these models is tested in different clinical situations and in actual patients (n=92). These models: HDR-C and HDR-D reduce bladder dose by 11.11% and 10% and rectal dose by 8% and 7% respectively. The treatment time is also reduced by 12-14%. In a busy hospital setup, these models find a place to cater large number of patients, while addressing individual patients geometry. (author)

  19. Procedural Optimization Models for Multiobjective Flexible JSSP

    Directory of Open Access Journals (Sweden)

    Elena Simona NICOARA

    2013-01-01

    Full Text Available The most challenging issues related to manufacturing efficiency occur if the jobs to be sched-uled are structurally different, if these jobs allow flexible routings on the equipments and mul-tiple objectives are required. This framework, called Multi-objective Flexible Job Shop Scheduling Problems (MOFJSSP, applicable to many real processes, has been less reported in the literature than the JSSP framework, which has been extensively formalized, modeled and analyzed from many perspectives. The MOFJSSP lie, as many other NP-hard problems, in a tedious place where the vast optimization theory meets the real world context. The paper brings to discussion the most optimization models suited to MOFJSSP and analyzes in detail the genetic algorithms and agent-based models as the most appropriate procedural models.

  20. A New Method for Determining Optimal Regularization Parameter in Near-Field Acoustic Holography

    Directory of Open Access Journals (Sweden)

    Yue Xiao

    2018-01-01

    Full Text Available Tikhonov regularization method is effective in stabilizing reconstruction process of the near-field acoustic holography (NAH based on the equivalent source method (ESM, and the selection of the optimal regularization parameter is a key problem that determines the regularization effect. In this work, a new method for determining the optimal regularization parameter is proposed. The transfer matrix relating the source strengths of the equivalent sources to the measured pressures on the hologram surface is augmented by adding a fictitious point source with zero strength. The minimization of the norm of this fictitious point source strength is as the criterion for choosing the optimal regularization parameter since the reconstructed value should tend to zero. The original inverse problem in calculating the source strengths is converted into a univariate optimization problem which is solved by a one-dimensional search technique. Two numerical simulations with a point driven simply supported plate and a pulsating sphere are investigated to validate the performance of the proposed method by comparison with the L-curve method. The results demonstrate that the proposed method can determine the regularization parameter correctly and effectively for the reconstruction in NAH.

  1. Optimizing Biorefinery Design and Operations via Linear Programming Models

    Energy Technology Data Exchange (ETDEWEB)

    Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric

    2017-03-28

    The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for

  2. An optimization strategy for a biokinetic model of inhaled radionuclides

    International Nuclear Information System (INIS)

    Shyr, L.J.; Griffith, W.C.; Boecker, B.B.

    1991-01-01

    Models for material disposition and dosimetry involve predictions of the biokinetics of the material among compartments representing organs and tissues in the body. Because of a lack of human data for most toxicants, many of the basic data are derived by modeling the results obtained from studies using laboratory animals. Such a biomathematical model is usually developed by adjusting the model parameters to make the model predictions match the measured retention and excretion data visually. The fitting process can be very time-consuming for a complicated model, and visual model selections may be subjective and easily biased by the scale or the data used. Due to the development of computerized optimization methods, manual fitting could benefit from an automated process. However, for a complicated model, an automated process without an optimization strategy will not be efficient, and may not produce fruitful results. In this paper, procedures for, and implementation of, an optimization strategy for a complicated mathematical model is demonstrated by optimizing a biokinetic model for 144Ce in fused aluminosilicate particles inhaled by beagle dogs. The optimized results using SimuSolv were compared to manual fitting results obtained previously using the model simulation software GASP. Also, statistical criteria provided by SimuSolv, such as likelihood function values, were used to help or verify visual model selections

  3. Cost optimization model and its heuristic genetic algorithms

    International Nuclear Information System (INIS)

    Liu Wei; Wang Yongqing; Guo Jilin

    1999-01-01

    Interest and escalation are large quantity in proportion to the cost of nuclear power plant construction. In order to optimize the cost, the mathematics model of cost optimization for nuclear power plant construction was proposed, which takes the maximum net present value as the optimization goal. The model is based on the activity networks of the project and is an NP problem. A heuristic genetic algorithms (HGAs) for the model was introduced. In the algorithms, a solution is represented with a string of numbers each of which denotes the priority of each activity for assigned resources. The HGAs with this encoding method can overcome the difficulty which is harder to get feasible solutions when using the traditional GAs to solve the model. The critical path of the activity networks is figured out with the concept of predecessor matrix. An example was computed with the HGAP programmed in C language. The results indicate that the model is suitable for the objectiveness, the algorithms is effective to solve the model

  4. Optimizing Cardiovascular Benefits of Exercise: A Review of Rodent Models

    Science.gov (United States)

    Davis, Brittany; Moriguchi, Takeshi; Sumpio, Bauer

    2013-01-01

    Although research unanimously maintains that exercise can ward off cardiovascular disease (CVD), the optimal type, duration, intensity, and combination of forms are yet not clear. In our review of existing rodent-based studies on exercise and cardiovascular health, we attempt to find the optimal forms, intensities, and durations of exercise. Using Scopus and Medline, a literature review of English language comparative journal studies of cardiovascular benefits and exercise was performed. This review examines the existing literature on rodent models of aerobic, anaerobic, and power exercise and compares the benefits of various training forms, intensities, and durations. The rodent studies reviewed in this article correlate with reports on human subjects that suggest regular aerobic exercise can improve cardiac and vascular structure and function, as well as lipid profiles, and reduce the risk of CVD. Findings demonstrate an abundance of rodent-based aerobic studies, but a lack of anaerobic and power forms of exercise, as well as comparisons of these three components of exercise. Thus, further studies must be conducted to determine a truly optimal regimen for cardiovascular health. PMID:24436579

  5. Optimal model of congestion management in deregulated environment of power sector with promotion of renewable energy sources

    International Nuclear Information System (INIS)

    Sood, Yog Raj; Singh, Randhir

    2010-01-01

    In the competitive electricity market it becomes very much important to give special consideration for development of renewable energy sources (RESs) due to environmental and other social problems related with conventional generations. So this paper presents an optimal model of congestion management with special emphasis for promotion of RES in competitive electricity market. This paper presents a generalized optimal model of congestion management for deregulated power sector that dispatches the pool in combination with privately negotiated bilateral and multilateral contracts while maximizing social benefit. This model determines the locational marginal pricing (LMP) based on marginal cost theory. It also determines the size of non-firm transactions as well as pool demand and generations. Both firms as well as non-firm transactions are considered in this model. The proposed model has been applied to IEEE-30 bus test system with addition of some RES for analysis of the proposed model. The RES supplies its power to load either through the firm transaction or through power pool. The power from RES is not subjected to any curtailment in proposed model of congestion management. (author)

  6. Optimization of spectral printer modeling based on a modified cellular Yule-Nielsen spectral Neugebauer model.

    Science.gov (United States)

    Liu, Qiang; Wan, Xiaoxia; Xie, Dehong

    2014-06-01

    The study presented here optimizes several steps in the spectral printer modeling workflow based on a cellular Yule-Nielsen spectral Neugebauer (CYNSN) model. First, a printer subdividing method was developed that reduces the number of sub-models while maintaining the maximum device gamut. Second, the forward spectral prediction accuracy of the CYNSN model for each subspace of the printer was improved using back propagation artificial neural network (BPANN) estimated n values. Third, a sequential gamut judging method, which clearly reduced the complexity of the optimal sub-model and cell searching process during printer backward modeling, was proposed. After that, we further modified the use of the modeling color metric and comprehensively improved the spectral and perceptual accuracy of the spectral printer model. The experimental results show that the proposed optimization approaches provide obvious improvements in aspects of the modeling accuracy or efficiency for each of the corresponding steps, and an overall improvement of the optimized spectral printer modeling workflow was also demonstrated.

  7. Particle swarm optimization for determining shortest distance to voltage collapse

    Energy Technology Data Exchange (ETDEWEB)

    Arya, L.D.; Choube, S.C. [Electrical Engineering Department, S.G.S.I.T.S. Indore, MP 452 003 (India); Shrivastava, M. [Electrical Engineering Department, Government Engineering College Ujjain, MP 456 010 (India); Kothari, D.P. [Centre for Energy Studies, Indian Institute of Technology, Delhi (India)

    2007-12-15

    This paper describes an algorithm for computing shortest distance to voltage collapse or determination of CSNBP using PSO technique. A direction along CSNBP gives conservative results from voltage security view point. This information is useful to the operator to steer the system away from this point by taking corrective actions. The distance to a closest bifurcation is a minimum of the loadability given a slack bus or participation factors for increasing generation as the load increases. CSNBP determination has been formulated as an optimization problem to be used in PSO technique. PSO is a new evolutionary algorithm (EA) which is population based inspired by the social behavior of animals such as fish schooling and birds flocking. It can handle optimization problems with any complexity since mechanization is simple with few parameters to be tuned. The developed algorithm has been implemented on two standard test systems. (author)

  8. Weighted sum of gray gases model optimization for numerical investigations of processes inside pulverized coal-fired furnaces

    Science.gov (United States)

    Crnomarkovic, Nenad; Belosevic, Srdjan; Tomanovic, Ivan; Milicevic, Aleksandar

    2017-12-01

    The effects of the number of significant figures (NSF) in the interpolation polynomial coefficients (IPCs) of the weighted sum of gray gases model (WSGM) on results of numerical investigations and WSGM optimization were investigated. The investigation was conducted using numerical simulations of the processes inside a pulverized coal-fired furnace. The radiative properties of the gas phase were determined using the simple gray gas model (SG), two-term WSGM (W2), and three-term WSGM (W3). Ten sets of the IPCs with the same NSF were formed for every weighting coefficient in both W2 and W3. The average and maximal relative difference values of the flame temperatures, wall temperatures, and wall heat fluxes were determined. The investigation showed that the results of numerical investigations were affected by the NSF unless it exceeded certain value. The increase in the NSF did not necessarily lead to WSGM optimization. The combination of the NSF (CNSF) was the necessary requirement for WSGM optimization.

  9. Optimization of ecosystem model parameters with different temporal variabilities using tower flux data and an ensemble Kalman filter

    Science.gov (United States)

    He, L.; Chen, J. M.; Liu, J.; Mo, G.; Zhen, T.; Chen, B.; Wang, R.; Arain, M.

    2013-12-01

    Terrestrial ecosystem models have been widely used to simulate carbon, water and energy fluxes and climate-ecosystem interactions. In these models, some vegetation and soil parameters are determined based on limited studies from literatures without consideration of their seasonal variations. Data assimilation (DA) provides an effective way to optimize these parameters at different time scales . In this study, an ensemble Kalman filter (EnKF) is developed and applied to optimize two key parameters of an ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS): (1) the maximum photosynthetic carboxylation rate (Vcmax) at 25 °C, and (2) the soil water stress factor (fw) for stomatal conductance formulation. These parameters are optimized through assimilating observations of gross primary productivity (GPP) and latent heat (LE) fluxes measured in a 74 year-old pine forest, which is part of the Turkey Point Flux Station's age-sequence sites. Vcmax is related to leaf nitrogen concentration and varies slowly over the season and from year to year. In contrast, fw varies rapidly in response to soil moisture dynamics in the root-zone. Earlier studies suggested that DA of vegetation parameters at daily time steps leads to Vcmax values that are unrealistic. To overcome the problem, we developed a three-step scheme to optimize Vcmax and fw. First, the EnKF is applied daily to obtain precursor estimates of Vcmax and fw. Then Vcmax is optimized at different time scales assuming fw is unchanged from first step. The best temporal period or window size is then determined by analyzing the magnitude of the minimized cost-function, and the coefficient of determination (R2) and Root-mean-square deviation (RMSE) of GPP and LE between simulation and observation. Finally, the daily fw value is optimized for rain free days corresponding to the Vcmax curve from the best window size. The optimized fw is then used to model its relationship with soil moisture. We found that

  10. AN OPTIMAL MAINTENANCE MANAGEMENT MODEL FOR AIRPORT CONCRETE PAVEMENT

    Science.gov (United States)

    Shimomura, Taizo; Fujimori, Yuji; Kaito, Kiyoyuki; Obama, Kengo; Kobayashi, Kiyoshi

    In this paper, an optimal management model is formulated for the performance-based rehabilitation/maintenance contract for airport concrete pavement, whereby two types of life cycle cost risks, i.e., ground consolidation risk and concrete depreciation risk, are explicitly considered. The non-homogenous Markov chain model is formulated to represent the deterioration processes of concrete pavement which are conditional upon the ground consolidation processes. The optimal non-homogenous Markov decision model with multiple types of risk is presented to design the optimal rehabilitation/maintenance plans. And the methodology to revise the optimal rehabilitation/maintenance plans based upon the monitoring data by the Bayesian up-to-dating rules. The validity of the methodology presented in this paper is examined based upon the case studies carried out for the H airport.

  11. System modelling and online optimal management of MicroGrid using Mesh Adaptive Direct Search

    Energy Technology Data Exchange (ETDEWEB)

    Mohamed, Faisal A. [Department of Electrical Engineering, Omar Al-Mukhtar University, P.O. Box 919, El-Bieda (Libya); Koivo, Heikki N. [Department of Automation and Systems Technology, Helsinki University of Technology, P.O. Box 5500, FIN-02015 HUT (Finland)

    2010-06-15

    This paper presents a generalized formulation to determine the optimal operating strategy and cost optimization scheme for a MicroGrid. Prior to the optimization of the MicroGrid itself, models for the system components are determined using real data. The proposed cost function takes into consideration the costs of the emissions, NO{sub x}, SO{sub 2}, and CO{sub 2}, start-up costs, as well as the operation and maintenance costs. A daily income and outgo from sold or purchased power is also added. The MicroGrid considered in this paper consists of a wind turbine, a micro turbine, a diesel generator, a photovoltaic array, a fuel cell, and a battery storage. In this work, the Mesh Adaptive Direct Search (MADS) algorithm is used to minimize the cost function of the system while constraining it to meet the customer demand and safety of the system. In comparison with previously proposed techniques, a significant reduction is obtained. (author)

  12. Optimality models in the age of experimental evolution and genomics

    OpenAIRE

    Bull, J. J.; Wang, I.-N.

    2010-01-01

    Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimen...

  13. Optimization Models and Methods Developed at the Energy Systems Institute

    OpenAIRE

    N.I. Voropai; V.I. Zorkaltsev

    2013-01-01

    The paper presents shortly some optimization models of energy system operation and expansion that have been created at the Energy Systems Institute of the Siberian Branch of the Russian Academy of Sciences. Consideration is given to the optimization models of energy development in Russia, a software package intended for analysis of power system reliability, and model of flow distribution in hydraulic systems. A general idea of the optimization methods developed at the Energy Systems Institute...

  14. Multi-objective optimization of GENIE Earth system models.

    Science.gov (United States)

    Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

    2009-07-13

    The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

  15. Multiple Surrogate Modeling for Wire-Wrapped Fuel Assembly Optimization

    International Nuclear Information System (INIS)

    Raza, Wasim; Kim, Kwang-Yong

    2007-01-01

    In this work, shape optimization of seven pin wire wrapped fuel assembly has been carried out in conjunction with RANS analysis in order to evaluate the performances of surrogate models. Previously, Ahmad and Kim performed the flow and heat transfer analysis based on the three-dimensional RANS analysis. But numerical optimization has not been applied to the design of wire-wrapped fuel assembly, yet. Surrogate models are being widely used in multidisciplinary optimization. Queipo et al. reviewed various surrogates based models used in aerospace applications. Goel et al. developed weighted average surrogate model based on response surface approximation (RSA), radial basis neural network (RBNN) and Krigging (KRG) models. In addition to the three basic models, RSA, RBNN and KRG, the multiple surrogate model, PBA also has been employed. Two geometric design variables and a multi-objective function with a weighting factor have been considered for this problem

  16. Stochastic Robust Mathematical Programming Model for Power System Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Cong; Changhyeok, Lee; Haoyong, Chen; Mehrotra, Sanjay

    2016-01-01

    This paper presents a stochastic robust framework for two-stage power system optimization problems with uncertainty. The model optimizes the probabilistic expectation of different worst-case scenarios with ifferent uncertainty sets. A case study of unit commitment shows the effectiveness of the proposed model and algorithms.

  17. Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach

    Science.gov (United States)

    Aguilo, Miguel A.; Warner, James E.

    2017-01-01

    This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.

  18. Optimal replenishment policy for fuzzy inventory model with deteriorating items and allowable shortages under inflationary conditions

    Directory of Open Access Journals (Sweden)

    Jaggi Chandra K.

    2016-01-01

    Full Text Available This study develops an inventory model to determine ordering policy for deteriorating items with constant demand rate under inflationary condition over a fixed planning horizon. Shortages are allowed and are partially backlogged. In today’s wobbling economy, especially for long term investment, the effects of inflation cannot be disregarded as uncertainty about future inflation may influence the ordering policy. Therefore, in this paper a fuzzy model is developed that fuzzify the inflation rate, discount rate, deterioration rate, and backlogging parameter by using triangular fuzzy numbers to represent the uncertainty. For Defuzzification, the well known signed distance method is employed to find the total profit over the planning horizon. The objective of the study is to derive the optimal number of cycles and their optimal length so to maximize the net present value of the total profit over a fixed planning horizon. The necessary and sufficient conditions for an optimal solution are characterized. An algorithm is proposed to find the optimal solution. Finally, the proposed model has been validated with numerical example. Sensitivity analysis has been performed to study the impact of various parameters on the optimal solution, and some important managerial implications are presented.

  19. GEMSFITS: Code package for optimization of geochemical model parameters and inverse modeling

    International Nuclear Information System (INIS)

    Miron, George D.; Kulik, Dmitrii A.; Dmytrieva, Svitlana V.; Wagner, Thomas

    2015-01-01

    Highlights: • Tool for generating consistent parameters against various types of experiments. • Handles a large number of experimental data and parameters (is parallelized). • Has a graphical interface and can perform statistical analysis on the parameters. • Tested on fitting the standard state Gibbs free energies of aqueous Al species. • Example on fitting interaction parameters of mixing models and thermobarometry. - Abstract: GEMSFITS is a new code package for fitting internally consistent input parameters of GEM (Gibbs Energy Minimization) geochemical–thermodynamic models against various types of experimental or geochemical data, and for performing inverse modeling tasks. It consists of the gemsfit2 (parameter optimizer) and gfshell2 (graphical user interface) programs both accessing a NoSQL database, all developed with flexibility, generality, efficiency, and user friendliness in mind. The parameter optimizer gemsfit2 includes the GEMS3K chemical speciation solver ( (http://gems.web.psi.ch/GEMS3K)), which features a comprehensive suite of non-ideal activity- and equation-of-state models of solution phases (aqueous electrolyte, gas and fluid mixtures, solid solutions, (ad)sorption. The gemsfit2 code uses the robust open-source NLopt library for parameter fitting, which provides a selection between several nonlinear optimization algorithms (global, local, gradient-based), and supports large-scale parallelization. The gemsfit2 code can also perform comprehensive statistical analysis of the fitted parameters (basic statistics, sensitivity, Monte Carlo confidence intervals), thus supporting the user with powerful tools for evaluating the quality of the fits and the physical significance of the model parameters. The gfshell2 code provides menu-driven setup of optimization options (data selection, properties to fit and their constraints, measured properties to compare with computed counterparts, and statistics). The practical utility, efficiency, and

  20. On Optimal Input Design and Model Selection for Communication Channels

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL

    2013-01-01

    In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

  1. A useful framework for optimal replacement models

    International Nuclear Information System (INIS)

    Aven, Terje; Dekker, Rommert

    1997-01-01

    In this note we present a general framework for optimization of replacement times. It covers a number of models, including various age and block replacement models, and allows a uniform analysis for all these models. A relation to the marginal cost concept is described

  2. Modelling and optimization of combined cycle power plant based on exergoeconomic and environmental analyses

    International Nuclear Information System (INIS)

    Ganjehkaviri, A.; Mohd Jaafar, M.N.; Ahmadi, P.; Barzegaravval, H.

    2014-01-01

    This research paper presents a study on a comprehensive thermodynamic modelling of a combined cycle power plant (CCPP). The effects of economic strategies and design parameters on the plant optimization are also studied. Exergoeconomic analysis is conducted in order to determine the cost of electricity and cost of exergy destruction. In addition, a comprehensive optimization study is performed to determine the optimal design parameters of the power plant. Next, the effects of economic parameters variations on the sustainability, carbon dioxide emission and fuel consumption of the plant are investigated and are presented for a typical combined cycle power plant. Therefore, the changes in economic parameters caused the balance between cash flows and fix costs of the plant changes at optimum point. Moreover, economic strategies greatly limited the maximum reasonable carbon emission and fuel consumption reduction. The results showed that by using the optimum values, the exergy efficiency increases for about 6%, while CO 2 emission decreases by 5.63%. However, the variation in the cost was less than 1% due to the fact that a cost constraint was implemented. In addition, the sensitivity analysis for the optimization study was curtailed to be carried out; therefore, the optimization process and results to two important parameters are presented and discussed.

  3. Uncertain and multi-objective programming models for crop planting structure optimization

    Directory of Open Access Journals (Sweden)

    Mo LI,Ping GUO,Liudong ZHANG,Chenglong ZHANG

    2016-03-01

    Full Text Available Crop planting structure optimization is a significant way to increase agricultural economic benefits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic profits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study, three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming (IFCCP model and an inexact fuzzy linear programming (IFLP model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimization-theory-based fuzzy linear multi-objective programming model was developed, which is capable of reflecting both uncertainties and multi-objective. In addition, a multi-objective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic benefits and the denominator representing minimum crop planting area allocation. These models better reflect actual situations, considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in Minqin County, north-west China. The advantages, the applicable conditions and the solution methods

  4. A concurrent optimization model for supplier selection with fuzzy quality loss

    International Nuclear Information System (INIS)

    Rosyidi, C.; Murtisari, R.; Jauhari, W.

    2017-01-01

    The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.

  5. A concurrent optimization model for supplier selection with fuzzy quality loss

    Energy Technology Data Exchange (ETDEWEB)

    Rosyidi, C.; Murtisari, R.; Jauhari, W.

    2017-07-01

    The purpose of this research is to develop a concurrent supplier selection model to minimize the purchasing cost and fuzzy quality loss considering process capability and assembled product specification. Design/methodology/approach: This research integrates fuzzy quality loss in the model to concurrently solve the decision making in detailed design stage and manufacturing stage. Findings: The resulted model can be used to concurrently select the optimal supplier and determine the tolerance of the components. The model balances the purchasing cost and fuzzy quality loss. Originality/value: An assembled product consists of many components which must be purchased from the suppliers. Fuzzy quality loss is integrated in the supplier selection model to allow the vagueness in final assembly by grouping the assembly into several grades according to the resulted assembly tolerance.

  6. Joint global optimization of tomographic data based on particle swarm optimization and decision theory

    Science.gov (United States)

    Paasche, H.; Tronicke, J.

    2012-04-01

    In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto

  7. An Optimization Model for the Selection of Bus-Only Lanes in a City.

    Science.gov (United States)

    Chen, Qun

    2015-01-01

    The planning of urban bus-only lane networks is an important measure to improve bus service and bus priority. To determine the effective arrangement of bus-only lanes, a bi-level programming model for urban bus lane layout is developed in this study that considers accessibility and budget constraints. The goal of the upper-level model is to minimize the total travel time, and the lower-level model is a capacity-constrained traffic assignment model that describes the passenger flow assignment on bus lines, in which the priority sequence of the transfer times is reflected in the passengers' route-choice behaviors. Using the proposed bi-level programming model, optimal bus lines are selected from a set of candidate bus lines; thus, the corresponding bus lane network on which the selected bus lines run is determined. The solution method using a genetic algorithm in the bi-level programming model is developed, and two numerical examples are investigated to demonstrate the efficacy of the proposed model.

  8. An Optimization Model for the Selection of Bus-Only Lanes in a City.

    Directory of Open Access Journals (Sweden)

    Qun Chen

    Full Text Available The planning of urban bus-only lane networks is an important measure to improve bus service and bus priority. To determine the effective arrangement of bus-only lanes, a bi-level programming model for urban bus lane layout is developed in this study that considers accessibility and budget constraints. The goal of the upper-level model is to minimize the total travel time, and the lower-level model is a capacity-constrained traffic assignment model that describes the passenger flow assignment on bus lines, in which the priority sequence of the transfer times is reflected in the passengers' route-choice behaviors. Using the proposed bi-level programming model, optimal bus lines are selected from a set of candidate bus lines; thus, the corresponding bus lane network on which the selected bus lines run is determined. The solution method using a genetic algorithm in the bi-level programming model is developed, and two numerical examples are investigated to demonstrate the efficacy of the proposed model.

  9. Optimized combination model and algorithm of parking guidance information configuration

    Directory of Open Access Journals (Sweden)

    Tian Ye

    2011-01-01

    Full Text Available Abstract Operators of parking guidance and information (PGI systems often have difficulty in providing the best car park availability information to drivers in periods of high demand. A new PGI configuration model based on the optimized combination method was proposed by analyzing of parking choice behavior. This article first describes a parking choice behavioral model incorporating drivers perceptions of waiting times at car parks based on PGI signs. This model was used to predict the influence of PGI signs on the overall performance of the traffic system. Then relationships were developed for estimating the arrival rates at car parks based on driver characteristics, car park attributes as well as the car park availability information displayed on PGI signs. A mathematical program was formulated to determine the optimal display PGI sign configuration to minimize total travel time. A genetic algorithm was used to identify solutions that significantly reduced queue lengths and total travel time compared with existing practices. These procedures were applied to an existing PGI system operating in Deqing Town and Xiuning City. Significant reductions in total travel time of parking vehicles with PGI being configured. This would reduce traffic congestion and lead to various environmental benefits.

  10. Economic Order Quality Model for Determining the Sales Prices of Fresh Goods at Various Points in Time

    Directory of Open Access Journals (Sweden)

    Po-Yu Chen

    2017-01-01

    Full Text Available Although the safe consumption of goods such as food products, medicine, and vaccines is related to their freshness, consumers frequently understand less than suppliers about the freshness of goods when they purchase them. Because of this lack of information, apart from sales prices, consumers refer only to the manufacturing and expiration dates when deciding whether to purchase and how many of these goods to buy. If dealers could determine the sales price at each point in time and customers’ intention to buy goods of varying freshness, then dealers could set an optimal inventory cycle and allocate a weekly sales price for each point in time, thereby maximizing the profit per unit time. Therefore, in this study, an economic order quality model was established to enable discussion of the optimal control of sales prices. The technique for identifying the optimal solution for the model was determined, the characteristics of the optimal solution were demonstrated, and the implications of the solution’s sensitivity analysis were explained.

  11. A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation

    International Nuclear Information System (INIS)

    Jiang, He; Dong, Yao

    2016-01-01

    Highlights: • Eclat data mining algorithm is used to determine the possible predictors. • Support vector machine is converted into a ridge regularization problem. • Hard penalty selects the number of radial basis functions to simply the structure. • Glowworm swarm optimization is utilized to determine the optimal parameters. - Abstract: For a portion of the power which is generated by grid connected photovoltaic installations, an effective solar irradiation forecasting approach must be crucial to ensure the quality and the security of power grid. This paper develops and investigates a novel model to forecast 30 daily global solar radiation at four given locations of the United States. Eclat data mining algorithm is first presented to discover association rules between solar radiation and several meteorological factors laying a theoretical foundation for these correlative factors as input vectors. An effective and innovative intelligent optimization model based on nonlinear support vector machine and hard penalty function is proposed to forecast solar radiation by converting support vector machine into a regularization problem with ridge penalty, adding a hard penalty function to select the number of radial basis functions, and using glowworm swarm optimization algorithm to determine the optimal parameters of the model. In order to illustrate our validity of the proposed method, the datasets at four sites of the United States are split to into training data and test data, separately. The experiment results reveal that the proposed model delivers the best forecasting performances comparing with other competitors.

  12. Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

    Science.gov (United States)

    Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

    2016-01-01

    CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

  13. Application of numerical optimization techniques to control system design for nonlinear dynamic models of aircraft

    Science.gov (United States)

    Lan, C. Edward; Ge, Fuying

    1989-01-01

    Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.

  14. An integrated model of water resources optimization allocation based on projection pursuit model - Grey wolf optimization method in a transboundary river basin

    Science.gov (United States)

    Yu, Sen; Lu, Hongwei

    2018-04-01

    Under the effects of global change, water crisis ranks as the top global risk in the future decade, and water conflict in transboundary river basins as well as the geostrategic competition led by it is most concerned. This study presents an innovative integrated PPMGWO model of water resources optimization allocation in a transboundary river basin, which is integrated through the projection pursuit model (PPM) and Grey wolf optimization (GWO) method. This study uses the Songhua River basin and 25 control units as examples, adopting the PPMGWO model proposed in this study to allocate the water quantity. Using water consumption in all control units in the Songhua River basin in 2015 as reference to compare with optimization allocation results of firefly algorithm (FA) and Particle Swarm Optimization (PSO) algorithms as well as the PPMGWO model, results indicate that the average difference between corresponding allocation results and reference values are 0.195 bil m3, 0.151 bil m3, and 0.085 bil m3, respectively. Obviously, the average difference of the PPMGWO model is the lowest and its optimization allocation result is closer to reality, which further confirms the reasonability, feasibility, and accuracy of the PPMGWO model. And then the PPMGWO model is adopted to simulate allocation of available water quantity in Songhua River basin in 2018, 2020, and 2030. The simulation results show water quantity which could be allocated in all controls demonstrates an overall increasing trend with reasonable and equal exploitation and utilization of water resources in the Songhua River basin in future. In addition, this study has a certain reference value and application meaning to comprehensive management and water resources allocation in other transboundary river basins.

  15. Modeling and Optimization of Class-E Amplifier at Subnominal Condition in a Wireless Power Transfer System for Biomedical Implants.

    Science.gov (United States)

    Liu, Hao; Shao, Qi; Fang, Xuelin

    2017-02-01

    For the class-E amplifier in a wireless power transfer (WPT) system, the design parameters are always determined by the nominal model. However, this model neglects the conduction loss and voltage stress of MOSFET and cannot guarantee the highest efficiency in the WPT system for biomedical implants. To solve this problem, this paper proposes a novel circuit model of the subnominal class-E amplifier. On a WPT platform for capsule endoscope, the proposed model was validated to be effective and the relationship between the amplifier's design parameters and its characteristics was analyzed. At a given duty ratio, the design parameters with the highest efficiency and safe voltage stress are derived and the condition is called 'optimal subnominal condition.' The amplifier's efficiency can reach the highest of 99.3% at the 0.097 duty ratio. Furthermore, at the 0.5 duty ratio, the measured efficiency of the optimal subnominal condition can reach 90.8%, which is 15.2% higher than that of the nominal condition. Then, a WPT experiment with a receiving unit was carried out to validate the feasibility of the optimized amplifier. In general, the design parameters of class-E amplifier in a WPT system for biomedical implants can be determined with the proposed optimization method in this paper.

  16. Space engineering modeling and optimization with case studies

    CERN Document Server

    Pintér, János

    2016-01-01

    This book presents a selection of advanced case studies that cover a substantial range of issues and real-world challenges and applications in space engineering. Vital mathematical modeling, optimization methodologies and numerical solution aspects of each application case study are presented in detail, with discussions of a range of advanced model development and solution techniques and tools. Space engineering challenges are discussed in the following contexts: •Advanced Space Vehicle Design •Computation of Optimal Low Thrust Transfers •Indirect Optimization of Spacecraft Trajectories •Resource-Constrained Scheduling, •Packing Problems in Space •Design of Complex Interplanetary Trajectories •Satellite Constellation Image Acquisition •Re-entry Test Vehicle Configuration Selection •Collision Risk Assessment on Perturbed Orbits •Optimal Robust Design of Hybrid Rocket Engines •Nonlinear Regression Analysis in Space Engineering< •Regression-Based Sensitivity Analysis and Robust Design ...

  17. Network models for solving the problem of multicriterial adaptive optimization of investment projects control with several acceptable technologies

    Science.gov (United States)

    Shorikov, A. F.; Butsenko, E. V.

    2017-10-01

    This paper discusses the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. On the basis of network modeling proposed a new economic and mathematical model and a method for solving the problem of multicriterial adaptive optimization the control of investment projects in the presence of several technologies. Network economic and mathematical modeling allows you to determine the optimal time and calendar schedule for the implementation of the investment project and serves as an instrument to increase the economic potential and competitiveness of the enterprise. On a meaningful practical example, the processes of forming network models are shown, including the definition of the sequence of actions of a particular investment projecting process, the network-based work schedules are constructed. The calculation of the parameters of network models is carried out. Optimal (critical) paths have been formed and the optimal time for implementing the chosen technologies of the investment project has been calculated. It also shows the selection of the optimal technology from a set of possible technologies for project implementation, taking into account the time and cost of the work. The proposed model and method for solving the problem of managing investment projects can serve as a basis for the development, creation and application of appropriate computer information systems to support the adoption of managerial decisions by business people.

  18. Fuzzy multiobjective models for optimal operation of a hydropower system

    Science.gov (United States)

    Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.

    2013-06-01

    Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

  19. A multi-objective simulation-optimization model for in situ bioremediation of groundwater contamination: Application of bargaining theory

    Science.gov (United States)

    Raei, Ehsan; Nikoo, Mohammad Reza; Pourshahabi, Shokoufeh

    2017-08-01

    In the present study, a BIOPLUME III simulation model is coupled with a non-dominating sorting genetic algorithm (NSGA-II)-based model for optimal design of in situ groundwater bioremediation system, considering preferences of stakeholders. Ministry of Energy (MOE), Department of Environment (DOE), and National Disaster Management Organization (NDMO) are three stakeholders in the groundwater bioremediation problem in Iran. Based on the preferences of these stakeholders, the multi-objective optimization model tries to minimize: (1) cost; (2) sum of contaminant concentrations that violate standard; (3) contaminant plume fragmentation. The NSGA-II multi-objective optimization method gives Pareto-optimal solutions. A compromised solution is determined using fallback bargaining with impasse to achieve a consensus among the stakeholders. In this study, two different approaches are investigated and compared based on two different domains for locations of injection and extraction wells. At the first approach, a limited number of predefined locations is considered according to previous similar studies. At the second approach, all possible points in study area are investigated to find optimal locations, arrangement, and flow rate of injection and extraction wells. Involvement of the stakeholders, investigating all possible points instead of a limited number of locations for wells, and minimizing the contaminant plume fragmentation during bioremediation are new innovations in this research. Besides, the simulation period is divided into smaller time intervals for more efficient optimization. Image processing toolbox in MATLAB® software is utilized for calculation of the third objective function. In comparison with previous studies, cost is reduced using the proposed methodology. Dispersion of the contaminant plume is reduced in both presented approaches using the third objective function. Considering all possible points in the study area for determining the optimal locations

  20. Modeling and Optimization : Theory and Applications Conference

    CERN Document Server

    Terlaky, Tamás

    2017-01-01

    This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 17-19, 2016. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, health, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.

  1. Modeling and Optimization : Theory and Applications Conference

    CERN Document Server

    Terlaky, Tamás

    2015-01-01

    This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 13-15, 2014. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, healthcare, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.

  2. Optimal blood glucose control in diabetes mellitus treatment using dynamic programming based on Ackerman’s linear model

    Science.gov (United States)

    Pradanti, Paskalia; Hartono

    2018-03-01

    Determination of insulin injection dose in diabetes mellitus treatment can be considered as an optimal control problem. This article is aimed to simulate optimal blood glucose control for patient with diabetes mellitus. The blood glucose regulation of diabetic patient is represented by Ackerman’s Linear Model. This problem is then solved using dynamic programming method. The desired blood glucose level is obtained by minimizing the performance index in Lagrange form. The results show that dynamic programming based on Ackerman’s Linear Model is quite good to solve the problem.

  3. Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

    Science.gov (United States)

    Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

    2018-04-01

    The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

  4. Optimal control of information epidemics modeled as Maki Thompson rumors

    Science.gov (United States)

    Kandhway, Kundan; Kuri, Joy

    2014-12-01

    We model the spread of information in a homogeneously mixed population using the Maki Thompson rumor model. We formulate an optimal control problem, from the perspective of single campaigner, to maximize the spread of information when the campaign budget is fixed. Control signals, such as advertising in the mass media, attempt to convert ignorants and stiflers into spreaders. We show the existence of a solution to the optimal control problem when the campaigning incurs non-linear costs under the isoperimetric budget constraint. The solution employs Pontryagin's Minimum Principle and a modified version of forward backward sweep technique for numerical computation to accommodate the isoperimetric budget constraint. The techniques developed in this paper are general and can be applied to similar optimal control problems in other areas. We have allowed the spreading rate of the information epidemic to vary over the campaign duration to model practical situations when the interest level of the population in the subject of the campaign changes with time. The shape of the optimal control signal is studied for different model parameters and spreading rate profiles. We have also studied the variation of the optimal campaigning costs with respect to various model parameters. Results indicate that, for some model parameters, significant improvements can be achieved by the optimal strategy compared to the static control strategy. The static strategy respects the same budget constraint as the optimal strategy and has a constant value throughout the campaign horizon. This work finds application in election and social awareness campaigns, product advertising, movie promotion and crowdfunding campaigns.

  5. Determination of an Optimal Control Strategy for a Generic Surface Vehicle

    Science.gov (United States)

    2014-06-18

    TERMS Autonomous Vehicles Boundary Value Problem Dynamic Programming Surface Vehicles Optimal Control Path Planning 16...to follow prescribed motion trajectories. In particular, for autonomous vehicles , this motion trajectory is given by the determination of the

  6. Groundwater Pollution Source Identification using Linked ANN-Optimization Model

    Science.gov (United States)

    Ayaz, Md; Srivastava, Rajesh; Jain, Ashu

    2014-05-01

    Groundwater is the principal source of drinking water in several parts of the world. Contamination of groundwater has become a serious health and environmental problem today. Human activities including industrial and agricultural activities are generally responsible for this contamination. Identification of groundwater pollution source is a major step in groundwater pollution remediation. Complete knowledge of pollution source in terms of its source characteristics is essential to adopt an effective remediation strategy. Groundwater pollution source is said to be identified completely when the source characteristics - location, strength and release period - are known. Identification of unknown groundwater pollution source is an ill-posed inverse problem. It becomes more difficult for real field conditions, when the lag time between the first reading at observation well and the time at which the source becomes active is not known. We developed a linked ANN-Optimization model for complete identification of an unknown groundwater pollution source. The model comprises two parts- an optimization model and an ANN model. Decision variables of linked ANN-Optimization model contain source location and release period of pollution source. An objective function is formulated using the spatial and temporal data of observed and simulated concentrations, and then minimized to identify the pollution source parameters. In the formulation of the objective function, we require the lag time which is not known. An ANN model with one hidden layer is trained using Levenberg-Marquardt algorithm to find the lag time. Different combinations of source locations and release periods are used as inputs and lag time is obtained as the output. Performance of the proposed model is evaluated for two and three dimensional case with error-free and erroneous data. Erroneous data was generated by adding uniformly distributed random error (error level 0-10%) to the analytically computed concentration

  7. DETERMINATION ОF DRESS ROLL OPTIMAL RADIUS WHILE PRODUCING PARTS WITH TROCHOIDAL PROFILE

    Directory of Open Access Journals (Sweden)

    E. N. Yankevich

    2008-01-01

    Full Text Available The paper considers determination of the dress roll optimal radius while producing parts having trohoidal profile with the help of grinding method that presupposes application of grinding disk. In this case disk profile has been cut-in by diamond dressing. Two methods for determination of calculation of the dress roll optimal radius have been proposed in the paper. On the basis of the satellite gear of the planetary pin reducer whose profile presents a trochoid it has been shown that the obtained results pertaining to two proposed methods conform with each other.

  8. Robust and fast nonlinear optimization of diffusion MRI microstructure models.

    Science.gov (United States)

    Harms, R L; Fritz, F J; Tobisch, A; Goebel, R; Roebroeck, A

    2017-07-15

    Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of

  9. Optimality models in the age of experimental evolution and genomics.

    Science.gov (United States)

    Bull, J J; Wang, I-N

    2010-09-01

    Optimality models have been used to predict evolution of many properties of organisms. They typically neglect genetic details, whether by necessity or design. This omission is a common source of criticism, and although this limitation of optimality is widely acknowledged, it has mostly been defended rather than evaluated for its impact. Experimental adaptation of model organisms provides a new arena for testing optimality models and for simultaneously integrating genetics. First, an experimental context with a well-researched organism allows dissection of the evolutionary process to identify causes of model failure--whether the model is wrong about genetics or selection. Second, optimality models provide a meaningful context for the process and mechanics of evolution, and thus may be used to elicit realistic genetic bases of adaptation--an especially useful augmentation to well-researched genetic systems. A few studies of microbes have begun to pioneer this new direction. Incompatibility between the assumed and actual genetics has been demonstrated to be the cause of model failure in some cases. More interestingly, evolution at the phenotypic level has sometimes matched prediction even though the adaptive mutations defy mechanisms established by decades of classic genetic studies. Integration of experimental evolutionary tests with genetics heralds a new wave for optimality models and their extensions that does not merely emphasize the forces driving evolution.

  10. Safety constraints applied to an adaptive Bayesian condition-based maintenance optimization model

    International Nuclear Information System (INIS)

    Flage, Roger; Coit, David W.; Luxhøj, James T.; Aven, Terje

    2012-01-01

    A model is described that determines an optimal inspection and maintenance scheme for a deteriorating unit with a stochastic degradation process with independent and stationary increments and for which the parameters are uncertain. This model and resulting maintenance plans offers some distinct benefits compared to prior research because the uncertainty of the degradation process is accommodated by a Bayesian approach and two new safety constraints have been applied to the problem: (1) with a given subjective probability (degree of belief), the limiting relative frequency of one or more failures during a fixed time interval is bounded; or (2) the subjective probability of one or more failures during a fixed time interval is bounded. In the model, the parameter(s) of a condition-based inspection scheduling function and a preventive replacement threshold are jointly optimized upon each replacement and inspection such as to minimize the expected long run cost per unit of time, but also considering one of the specified safety constraints. A numerical example is included to illustrate the effect of imposing each of the two different safety constraints.

  11. Modeling of biological intelligence for SCM system optimization.

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  12. Modeling of Biological Intelligence for SCM System Optimization

    Directory of Open Access Journals (Sweden)

    Shengyong Chen

    2012-01-01

    Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

  13. Modeling of Biological Intelligence for SCM System Optimization

    Science.gov (United States)

    Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

    2012-01-01

    This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

  14. Group Elevator Peak Scheduling Based on Robust Optimization Model

    Directory of Open Access Journals (Sweden)

    ZHANG, J.

    2013-08-01

    Full Text Available Scheduling of Elevator Group Control System (EGCS is a typical combinatorial optimization problem. Uncertain group scheduling under peak traffic flows has become a research focus and difficulty recently. RO (Robust Optimization method is a novel and effective way to deal with uncertain scheduling problem. In this paper, a peak scheduling method based on RO model for multi-elevator system is proposed. The method is immune to the uncertainty of peak traffic flows, optimal scheduling is realized without getting exact numbers of each calling floor's waiting passengers. Specifically, energy-saving oriented multi-objective scheduling price is proposed, RO uncertain peak scheduling model is built to minimize the price. Because RO uncertain model could not be solved directly, RO uncertain model is transformed to RO certain model by elevator scheduling robust counterparts. Because solution space of elevator scheduling is enormous, to solve RO certain model in short time, ant colony solving algorithm for elevator scheduling is proposed. Based on the algorithm, optimal scheduling solutions are found quickly, and group elevators are scheduled according to the solutions. Simulation results show the method could improve scheduling performances effectively in peak pattern. Group elevators' efficient operation is realized by the RO scheduling method.

  15. Reduced order modeling in topology optimization of vibroacoustic problems

    DEFF Research Database (Denmark)

    Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Brunskog, Jonas

    2017-01-01

    complex 3D parts. The optimization process can therefore become highly time consuming due to the need to solve a large system of equations at each iteration. Projection-based parametric Model Order Reduction (pMOR) methods have successfully been applied for reducing the computational cost of material......There is an interest in introducing topology optimization techniques in the design process of structural-acoustic systems. In topology optimization, the design space must be finely meshed in order to obtain an accurate design, which results in large numbers of degrees of freedom when designing...... or size optimization in large vibroacoustic models; however, new challenges are encountered when dealing with topology optimization. Since a design parameter per element is considered, the total number of design variables becomes very large; this poses a challenge to most existing pMOR techniques, which...

  16. Optimal Aging and Death

    DEFF Research Database (Denmark)

    Dalgaard, Carl-Johan Lars; Strulik, Holger

    2010-01-01

    This study introduces physiological aging into a simple model of optimal intertemporal consumption. In this endeavor we draw on the natural science literature on aging. According to the purposed theory, the speed of the aging process and the time of death are endogenously determined by optimal...

  17. Optimal Control Inventory Stochastic With Production Deteriorating

    Science.gov (United States)

    Affandi, Pardi

    2018-01-01

    In this paper, we are using optimal control approach to determine the optimal rate in production. Most of the inventory production models deal with a single item. First build the mathematical models inventory stochastic, in this model we also assume that the items are in the same store. The mathematical model of the problem inventory can be deterministic and stochastic models. In this research will be discussed how to model the stochastic as well as how to solve the inventory model using optimal control techniques. The main tool in the study problems for the necessary optimality conditions in the form of the Pontryagin maximum principle involves the Hamilton function. So we can have the optimal production rate in a production inventory system where items are subject deterioration.

  18. Optlang: An algebraic modeling language for mathematical optimization

    DEFF Research Database (Denmark)

    Jensen, Kristian; Cardoso, Joao; Sonnenschein, Nikolaus

    2016-01-01

    Optlang is a Python package implementing a modeling language for solving mathematical optimization problems, i.e., maximizing or minimizing an objective function over a set of variables subject to a number of constraints. It provides a common native Python interface to a series of optimization...

  19. In vitro placental model optimization for nanoparticle transport studies

    Directory of Open Access Journals (Sweden)

    Cartwright L

    2012-01-01

    Full Text Available Laura Cartwright1, Marie Sønnegaard Poulsen2, Hanne Mørck Nielsen3, Giulio Pojana4, Lisbeth E Knudsen2, Margaret Saunders1, Erik Rytting2,51Bristol Initiative for Research of Child Health (BIRCH, Biophysics Research Unit, St Michael's Hospital, UH Bristol NHS Foundation Trust, Bristol, UK; 2University of Copenhagen, Faculty of Health Sciences, Department of Public Health, 3University of Copenhagen, Faculty of Pharmaceutical Sciences, Department of Pharmaceutics and Analytical Chemistry, Copenhagen, Denmark; 4Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari Venice, Venice, Italy; 5Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, Texas, USABackground: Advances in biomedical nanotechnology raise hopes in patient populations but may also raise questions regarding biodistribution and biocompatibility, especially during pregnancy. Special consideration must be given to the placenta as a biological barrier because a pregnant woman's exposure to nanoparticles could have significant effects on the fetus developing in the womb. Therefore, the purpose of this study is to optimize an in vitro model for characterizing the transport of nanoparticles across human placental trophoblast cells.Methods: The growth of BeWo (clone b30 human placental choriocarcinoma cells for nanoparticle transport studies was characterized in terms of optimized Transwell® insert type and pore size, the investigation of barrier properties by transmission electron microscopy, tight junction staining, transepithelial electrical resistance, and fluorescein sodium transport. Following the determination of nontoxic concentrations of fluorescent polystyrene nanoparticles, the cellular uptake and transport of 50 nm and 100 nm diameter particles was measured using the in vitro BeWo cell model.Results: Particle size measurements, fluorescence readings, and confocal microscopy indicated both cellular uptake of

  20. Aerodynamic modelling and optimization of axial fans

    Energy Technology Data Exchange (ETDEWEB)

    Noertoft Soerensen, Dan

    1998-01-01

    A numerically efficient mathematical model for the aerodynamics of low speed axial fans of the arbitrary vortex flow type has been developed. The model is based on a blade-element principle, whereby the rotor is divided into a number of annular stream tubes. For each of these stream tubes relations for velocity, pressure and radial position are derived from the conservation laws for mass, tangential momentum and energy. The equations are solved using the Newton-Raphson methods, and solutions converged to machine accuracy are found at small computing costs. The model has been validated against published measurements on various fan configurations, comprising two rotor-only fan stages, a counter-rotating fan unit and a stator-rotor stator stage. Comparisons of local and integrated properties show that the computed results agree well with the measurements. Optimizations have been performed to maximize the mean value of fan efficiency in a design interval of flow rates, thus designing a fan which operates well over a range of different flow conditions. The optimization scheme was used to investigate the dependence of maximum efficiency on 1: the number of blades, 2: the width of the design interval and 3: the hub radius. The degree of freedom in the choice of design variable and constraints, combined with the design interval concept, provides a valuable design-tool for axial fans. To further investigate the use of design optimization, a model for the vortex shedding noise from the trailing edge of the blades has been incorporated into the optimization scheme. The noise emission from the blades was minimized in a flow rate design point. Optimizations were performed to investigate the dependence of the noise on 1: the number of blades, 2: a constraint imposed on efficiency and 3: the hub radius. The investigations showed, that a significant reduction of noise could be achieved, at the expense of a small reduction in fan efficiency. (EG) 66 refs.

  1. What does optimization theory actually predict about crown profiles of photosynthetic capacity when models incorporate greater realism?

    Science.gov (United States)

    Buckley, Thomas N; Cescatti, Alessandro; Farquhar, Graham D

    2013-08-01

    Measured profiles of photosynthetic capacity in plant crowns typically do not match those of average irradiance: the ratio of capacity to irradiance decreases as irradiance increases. This differs from optimal profiles inferred from simple models. To determine whether this could be explained by omission of physiological or physical details from such models, we performed a series of thought experiments using a new model that included more realism than previous models. We used ray-tracing to simulate irradiance for 8000 leaves in a horizontally uniform canopy. For a subsample of 500 leaves, we simultaneously optimized both nitrogen allocation (among pools representing carboxylation, electron transport and light capture) and stomatal conductance using a transdermally explicit photosynthesis model. Few model features caused the capacity/irradiance ratio to vary systematically with irradiance. However, when leaf absorptance varied as needed to optimize distribution of light-capture N, the capacity/irradiance ratio increased up through the crown - that is, opposite to the observed pattern. This tendency was counteracted by constraints on stomatal or mesophyll conductance, which caused chloroplastic CO(2) concentration to decline systematically with increasing irradiance. Our results suggest that height-related constraints on stomatal conductance can help to reconcile observations with the hypothesis that photosynthetic N is allocated optimally. © 2013 John Wiley & Sons Ltd.

  2. Integrated production planning and control: A multi-objective optimization model

    Directory of Open Access Journals (Sweden)

    Cheng Wang

    2013-09-01

    Full Text Available Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP. For the defects of ERP system, many local improvement and optimization schemes have been proposed, and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. The validity and practicability of the model will be verified by the instance in the last part of the paper. Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process, and building a multi-objective optimization model can effectively optimize the management and control of enterprise

  3. A stochastic discrete optimization model for designing container terminal facilities

    Science.gov (United States)

    Zukhruf, Febri; Frazila, Russ Bona; Burhani, Jzolanda Tsavalista

    2017-11-01

    As uncertainty essentially affect the total transportation cost, it remains important in the container terminal that incorporates several modes and transshipments process. This paper then presents a stochastic discrete optimization model for designing the container terminal, which involves the decision of facilities improvement action. The container terminal operation model is constructed by accounting the variation of demand and facilities performance. In addition, for illustrating the conflicting issue that practically raises in the terminal operation, the model also takes into account the possible increment delay of facilities due to the increasing number of equipment, especially the container truck. Those variations expectantly reflect the uncertainty issue in the container terminal operation. A Monte Carlo simulation is invoked to propagate the variations by following the observed distribution. The problem is constructed within the framework of the combinatorial optimization problem for investigating the optimal decision of facilities improvement. A new variant of glow-worm swarm optimization (GSO) is thus proposed for solving the optimization, which is rarely explored in the transportation field. The model applicability is tested by considering the actual characteristics of the container terminal.

  4. Determination of optimal pollution levels through multiple-criteria decision making: an application to the Spanish electricity sector

    International Nuclear Information System (INIS)

    Linares, P.

    1999-01-01

    An efficient pollution management requires the harmonisation of often conflicting economic and environmental aspects. A compromise has to be found, in which social welfare is maximised. The determination of this social optimum has been attempted with different tools, of which the most correct according to neo-classical economics may be the one based on the economic valuation of the externalities of pollution. However, this approach is still controversial, and few decision makers trust the results obtained enough to apply them. But a very powerful alternative exists, which avoids the problem of monetizing physical impacts. Multiple-criteria decision making provides methodologies for dealing with impacts in different units, and for incorporating the preferences of decision makers or society as a whole, thus allowing for the determination of social optima under heterogeneous criteria, which is usually the case of pollution management decisions. In this paper, a compromise programming model is presented for the determination of the optimal pollution levels for the electricity industry in Spain for carbon dioxide, sulphur dioxide, nitrous oxides, and radioactive waste. The preferences of several sectors of society are incorporated explicitly into the model, so that the solution obtained represents the optimal pollution level from a social point of view. Results show that cost minimisation is still the main objective for society, but the simultaneous consideration of the rest of the criteria achieves large pollution reductions at a low cost increment. (Author)

  5. Adaptive stimulus optimization and model-based experiments for sensory systems neuroscience

    Directory of Open Access Journals (Sweden)

    Christopher eDiMattina

    2013-06-01

    Full Text Available In this paper we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical textit{optimal stimulus} paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the textit{iso-response} paradigm which finds sets of stimuli giving rise to constant responses, and the textit{system identification} paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of nonlinear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify nonlinear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and towards a new paradigm of real-time model estimation and comparison.

  6. Optimal harvesting for a predator-prey agent-based model using difference equations.

    Science.gov (United States)

    Oremland, Matthew; Laubenbacher, Reinhard

    2015-03-01

    In this paper, a method known as Pareto optimization is applied in the solution of a multi-objective optimization problem. The system in question is an agent-based model (ABM) wherein global dynamics emerge from local interactions. A system of discrete mathematical equations is formulated in order to capture the dynamics of the ABM; while the original model is built up analytically from the rules of the model, the paper shows how minor changes to the ABM rule set can have a substantial effect on model dynamics. To address this issue, we introduce parameters into the equation model that track such changes. The equation model is amenable to mathematical theory—we show how stability analysis can be performed and validated using ABM data. We then reduce the equation model to a simpler version and implement changes to allow controls from the ABM to be tested using the equations. Cohen's weighted κ is proposed as a measure of similarity between the equation model and the ABM, particularly with respect to the optimization problem. The reduced equation model is used to solve a multi-objective optimization problem via a technique known as Pareto optimization, a heuristic evolutionary algorithm. Results show that the equation model is a good fit for ABM data; Pareto optimization provides a suite of solutions to the multi-objective optimization problem that can be implemented directly in the ABM.

  7. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters

    Science.gov (United States)

    Wang, Zhihao; Yi, Jing

    2016-01-01

    For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result. PMID:28042291

  8. An optimal control model of crop thinning in viticulture

    OpenAIRE

    Schamel Guenter H.; Schubert Stefan F.

    2016-01-01

    We develop an economic model of cluster thinning in viticulture to control for grape quantity harvested and grape quality, applying a simple optimal control model with the aim to raise grape quality and related economic profits. The model maximizes vineyard owner profits and allows to discuss two relevant scenarios using a phase diagram analysis: (1) when the initial grape quantity is sufficiently small, thinning grapes will not be optimal and (2) when the initial grape quantity is high enoug...

  9. On a mean field game optimal control approach modeling fast exit scenarios in human crowds

    KAUST Repository

    Burger, Martin; Di Francesco, Marco; Markowich, Peter A.; Wolfram, Marie Therese

    2013-01-01

    The understanding of fast exit and evacuation situations in crowd motion research has received a lot of scientific interest in the last decades. Security issues in larger facilities, like shopping malls, sports centers, or festivals necessitate a better understanding of the major driving forces in crowd dynamics. In this paper we present an optimal control approach modeling fast exit scenarios in pedestrian crowds. The model is formulated in the framework of mean field games and based on a parabolic optimal control problem. We consider the case of a large human crowd trying to exit a room as fast as possible. The motion of every pedestrian is determined by minimizing a cost functional, which depends on his/her position and velocity, the overall density of people, and the time to exit. This microscopic setup leads in a mean-field formulation to a nonlinear macroscopic optimal control problem, which raises challenging questions for the analysis and numerical simulations.We discuss different aspects of the mathematical modeling and illustrate them with various computational results. ©2013 IEEE.

  10. On a mean field game optimal control approach modeling fast exit scenarios in human crowds

    KAUST Repository

    Burger, Martin

    2013-12-01

    The understanding of fast exit and evacuation situations in crowd motion research has received a lot of scientific interest in the last decades. Security issues in larger facilities, like shopping malls, sports centers, or festivals necessitate a better understanding of the major driving forces in crowd dynamics. In this paper we present an optimal control approach modeling fast exit scenarios in pedestrian crowds. The model is formulated in the framework of mean field games and based on a parabolic optimal control problem. We consider the case of a large human crowd trying to exit a room as fast as possible. The motion of every pedestrian is determined by minimizing a cost functional, which depends on his/her position and velocity, the overall density of people, and the time to exit. This microscopic setup leads in a mean-field formulation to a nonlinear macroscopic optimal control problem, which raises challenging questions for the analysis and numerical simulations.We discuss different aspects of the mathematical modeling and illustrate them with various computational results. ©2013 IEEE.

  11. Studies on multivariate autoregressive analysis using synthesized reactor noise-like data for optimal modelling

    Energy Technology Data Exchange (ETDEWEB)

    Ciftcioglu, O.; Hoogenboom, J.E.; Dam, H. van

    1988-01-01

    Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality.

  12. Studies on multivariate autoregressive analysis using synthesized reactor noise-like data for optimal modelling

    International Nuclear Information System (INIS)

    Ciftcioglu, O.

    1988-01-01

    Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality. (author)

  13. Determination of hydraulic properties of unsaturated soil via inverse modeling

    International Nuclear Information System (INIS)

    Kodesova, R.

    2004-01-01

    The method for determining the hydraulic properties of unsaturated soil with inverse modeling is presented. A modified cone penetrometer has been designed to inject water into the soil through a screen, and measure the progress of the wetting front with two tensiometer rings positioned above the screen. Cumulative inflow and pressure head readings are analyzed to obtain estimates of the hydraulic parameters describing K(h) and θ(h). Optimization results for tests at one side are used to demonstrate the possibility to evaluate either the wetting branches of the soil hydraulic properties, or the wetting and drying curves simultaneously, via analysis of different parts of the experiment. The optimization results are compared to the results of standard laboratory and field methods. (author)

  14. Fuzzy Stochastic Optimization Theory, Models and Applications

    CERN Document Server

    Wang, Shuming

    2012-01-01

    Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies.   The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins...

  15. Optimal inventory management and order book modeling

    KAUST Repository

    Baradel, Nicolas

    2018-02-16

    We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) firms, and institutional brokers (IB). Given a prior dynamic of the order book, similar to the one considered in the Queue-Reactive models [14, 20, 21], the MM and the HFT define their trading strategy by optimizing the expected utility of terminal wealth, while the IB has a prescheduled task to sell or buy many shares of the considered asset. We derive the variational partial differential equations that characterize the value functions of the MM and HFT and explain how almost optimal control can be deduced from them. We then provide a first illustration of the interactions that can take place between these different market participants by simulating the dynamic of an order book in which each of them plays his own (optimal) strategy.

  16. Optimization of damping in the passive automotive suspension system with using two quarter-car models

    Science.gov (United States)

    Lozia, Z.; Zdanowicz, P.

    2016-09-01

    The paper presents the optimization of damping in the passive suspension system of a motor vehicle moving rectilinearly with a constant speed on a road with rough surface of random irregularities, described according to the ISO classification. Two quarter-car 2DoF models, linear and non-linear, were used; in the latter, nonlinearities of spring characteristics of the suspension system and pneumatic tyres, sliding friction in the suspension system, and wheel lift-off were taken into account. The smoothing properties of vehicle tyres were represented in both models. The calculations were carried out for three roads of different quality, with simulating four vehicle speeds. Statistical measures of vertical vehicle body vibrations and of changes in the vertical tyre/road contact force were used as the criteria of system optimization and model comparison. The design suspension displacement limit was also taken into account. The optimum suspension damping coefficient was determined and the impact of undesirable sliding friction in the suspension system on the calculation results was estimated. The results obtained make it possible to evaluate the impact of the structure and complexity of the model used on the results of the optimization.

  17. A system-theory-based model for monthly river runoff forecasting: model calibration and optimization

    Directory of Open Access Journals (Sweden)

    Wu Jianhua

    2014-03-01

    Full Text Available River runoff is not only a crucial part of the global water cycle, but it is also an important source for hydropower and an essential element of water balance. This study presents a system-theory-based model for river runoff forecasting taking the Hailiutu River as a case study. The forecasting model, designed for the Hailiutu watershed, was calibrated and verified by long-term precipitation observation data and groundwater exploitation data from the study area. Additionally, frequency analysis, taken as an optimization technique, was applied to improve prediction accuracy. Following model optimization, the overall relative prediction errors are below 10%. The system-theory-based prediction model is applicable to river runoff forecasting, and following optimization by frequency analysis, the prediction error is acceptable.

  18. Ground Vehicle System Integration (GVSI) and Design Optimization Model

    National Research Council Canada - National Science Library

    Horton, William

    1996-01-01

    This report documents the Ground Vehicle System Integration (GVSI) and Design Optimization Model GVSI is a top-level analysis tool designed to support engineering tradeoff studies and vehicle design optimization efforts...

  19. Reduced order modeling and parameter identification of a building energy system model through an optimization routine

    International Nuclear Information System (INIS)

    Harish, V.S.K.V.; Kumar, Arun

    2016-01-01

    Highlights: • A BES model based on 1st principles is developed and solved numerically. • Parameters of lumped capacitance model are fitted using the proposed optimization routine. • Validations are showed for different types of building construction elements. • Step response excitations for outdoor air temperature and relative humidity are analyzed. - Abstract: Different control techniques together with intelligent building technology (Building Automation Systems) are used to improve energy efficiency of buildings. In almost all control projects, it is crucial to have building energy models with high computational efficiency in order to design and tune the controllers and simulate their performance. In this paper, a set of partial differential equations are formulated accounting for energy flow within the building space. These equations are then solved as conventional finite difference equations using Crank–Nicholson scheme. Such a model of a higher order is regarded as a benchmark model. An optimization algorithm has been developed, depicted through a flowchart, which minimizes the sum squared error between the step responses of the numerical and the optimal model. Optimal model of the construction element is nothing but a RC-network model with the values of Rs and Cs estimated using the non-linear time invariant constrained optimization routine. The model is validated with comparing the step responses with other two RC-network models whose parameter values are selected based on a certain criteria. Validations are showed for different types of building construction elements viz., low, medium and heavy thermal capacity elements. Simulation results show that the optimal model closely follow the step responses of the numerical model as compared to the responses of other two models.

  20. Optimization of the indirect at neutron activation technique for the determination of boron in aqueous solutions

    International Nuclear Information System (INIS)

    Luz, L.C.Q.P. da.

    1984-01-01

    The purpose of this work was the development of an instrumental method for the optimization of the indirect neutron activation analysis of boron in aqueous solutions. The optimization took into account the analytical parameters under laboratory conditions: activation carried out with a 241 Am/Be neutron source and detection of the activity induced in vanadium with two NaI(Tl) gamma spectrometers. A calibration curve was thus obtained for a concentration range of 0 to 5000 ppm B. Later on, experimental models were built in order to study the feasibility of automation. The analysis of boron was finally performed, under the previously established conditions, with an automated system comprising the operations of transport, irradiation and counting. An improvement in the quality of the analysis was observed, with boron concentrations as low as 5 ppm being determined with a precision level better than 0.4%. The experimental model features all basic design elements for an automated device for the analysis of boron in agueous solutions wherever this is required, as in the operation of nuclear reactors. (Author) [pt

  1. Integrated intravital microscopy and mathematical modeling to optimize nanotherapeutics delivery to tumors

    Directory of Open Access Journals (Sweden)

    Anne L. van de Ven

    2012-03-01

    Full Text Available Inefficient vascularization hinders the optimal transport of cell nutrients, oxygen, and drugs to cancer cells in solid tumors. Gradients of these substances maintain a heterogeneous cell-scale microenvironment through which drugs and their carriers must travel, significantly limiting optimal drug exposure. In this study, we integrate intravital microscopy with a mathematical model of cancer to evaluate the behavior of nanoparticle-based drug delivery systems designed to circumvent biophysical barriers. We simulate the effect of doxorubicin delivered via porous 1000 x 400 nm plateloid silicon particles to a solid tumor characterized by a realistic vasculature, and vary the parameters to determine how much drug per particle and how many particles need to be released within the vasculature in order to achieve remission of the tumor. We envision that this work will contribute to the development of quantitative measures of nanoparticle design and drug loading in order to optimize cancer treatment via nanotherapeutics.

  2. CREATION OF OPTIMIZATION MODEL OF STEAM BOILER RECUPERATIVE AIR HEATER

    Directory of Open Access Journals (Sweden)

    N. B. Carnickiy

    2006-01-01

    Full Text Available The paper proposes to use a mathematical modeling as one of the ways intended to improve quality of recuperative air heater design (RAH without significant additional costs, connected with the change of design materials or fuel type. The described conceptual mathematical AHP optimization model of RAH consists of optimized and constant parameters, technical limitations and optimality criteria.The paper considers a methodology for search of design and regime parameters of an air heater which is based on the methods of multi-criteria optimization. Conclusions for expediency of the given approach usage are made in the paper.

  3. Multi-time scale Climate Informed Stochastic Hybrid Simulation-Optimization Model (McISH model) for Multi-Purpose Reservoir System

    Science.gov (United States)

    Lu, M.; Lall, U.

    2013-12-01

    In order to mitigate the impacts of climate change, proactive management strategies to operate reservoirs and dams are needed. A multi-time scale climate informed stochastic model is developed to optimize the operations for a multi-purpose single reservoir by simulating decadal, interannual, seasonal and sub-seasonal variability. We apply the model to a setting motivated by the largest multi-purpose dam in N. India, the Bhakhra reservoir on the Sutlej River, a tributary of the Indus. This leads to a focus on timing and amplitude of the flows for the monsoon and snowmelt periods. The flow simulations are constrained by multiple sources of historical data and GCM future projections, that are being developed through a NSF funded project titled 'Decadal Prediction and Stochastic Simulation of Hydroclimate Over Monsoon Asia'. The model presented is a multilevel, nonlinear programming model that aims to optimize the reservoir operating policy on a decadal horizon and the operation strategy on an updated annual basis. The model is hierarchical, in terms of having a structure that two optimization models designated for different time scales are nested as a matryoshka doll. The two optimization models have similar mathematical formulations with some modifications to meet the constraints within that time frame. The first level of the model is designated to provide optimization solution for policy makers to determine contracted annual releases to different uses with a prescribed reliability; the second level is a within-the-period (e.g., year) operation optimization scheme that allocates the contracted annual releases on a subperiod (e.g. monthly) basis, with additional benefit for extra release and penalty for failure. The model maximizes the net benefit of irrigation, hydropower generation and flood control in each of the periods. The model design thus facilitates the consistent application of weather and climate forecasts to improve operations of reservoir systems. The

  4. A Novel Parametric Modeling Method and Optimal Design for Savonius Wind Turbines

    Directory of Open Access Journals (Sweden)

    Baoshou Zhang

    2017-03-01

    Full Text Available Under the inspiration of polar coordinates, a novel parametric modeling and optimization method for Savonius wind turbines was proposed to obtain the highest power output, in which a quadratic polynomial curve was bent to describe a blade. Only two design parameters are needed for the shape-complicated blade. Therefore, this novel method reduces sampling scale. A series of transient simulations was run to get the optimal performance coefficient (power coefficient C p for different modified turbines based on computational fluid dynamics (CFD method. Then, a global response surface model and a more precise local response surface model were created according to Kriging Method. These models defined the relationship between optimization objective Cp and design parameters. Particle swarm optimization (PSO algorithm was applied to find the optimal design based on these response surface models. Finally, the optimal Savonius blade shaped like a “hook” was obtained. Cm (torque coefficient, Cp and flow structure were compared for the optimal design and the classical design. The results demonstrate that the optimal Savonius turbine has excellent comprehensive performance. The power coefficient Cp is significantly increased from 0.247 to 0.262 (6% higher. The weight of the optimal blade is reduced by 17.9%.

  5. Framework for determining airport daily departure and arrival delay thresholds: statistical modelling approach.

    Science.gov (United States)

    Wesonga, Ronald; Nabugoomu, Fabian

    2016-01-01

    The study derives a framework for assessing airport efficiency through evaluating optimal arrival and departure delay thresholds. Assumptions of airport efficiency measurements, though based upon minimum numeric values such as 15 min of turnaround time, cannot be extrapolated to determine proportions of delay-days of an airport. This study explored the concept of delay threshold to determine the proportion of delay-days as an expansion of the theory of delay and our previous work. Data-driven approach using statistical modelling was employed to a limited set of determinants of daily delay at an airport. For the purpose of testing the efficacy of the threshold levels, operational data for Entebbe International Airport were used as a case study. Findings show differences in the proportions of delay at departure (μ = 0.499; 95 % CI = 0.023) and arrival (μ = 0.363; 95 % CI = 0.022). Multivariate logistic model confirmed an optimal daily departure and arrival delay threshold of 60 % for the airport given the four probable thresholds {50, 60, 70, 80}. The decision for the threshold value was based on the number of significant determinants, the goodness of fit statistics based on the Wald test and the area under the receiver operating curves. These findings propose a modelling framework to generate relevant information for the Air Traffic Management relevant in planning and measurement of airport operational efficiency.

  6. Combustion Model and Control Parameter Optimization Methods for Single Cylinder Diesel Engine

    Directory of Open Access Journals (Sweden)

    Bambang Wahono

    2014-01-01

    Full Text Available This research presents a method to construct a combustion model and a method to optimize some control parameters of diesel engine in order to develop a model-based control system. The construction purpose of the model is to appropriately manage some control parameters to obtain the values of fuel consumption and emission as the engine output objectives. Stepwise method considering multicollinearity was applied to construct combustion model with the polynomial model. Using the experimental data of a single cylinder diesel engine, the model of power, BSFC, NOx, and soot on multiple injection diesel engines was built. The proposed method succesfully developed the model that describes control parameters in relation to the engine outputs. Although many control devices can be mounted to diesel engine, optimization technique is required to utilize this method in finding optimal engine operating conditions efficiently beside the existing development of individual emission control methods. Particle swarm optimization (PSO was used to calculate control parameters to optimize fuel consumption and emission based on the model. The proposed method is able to calculate control parameters efficiently to optimize evaluation item based on the model. Finally, the model which added PSO then was compiled in a microcontroller.

  7. Land Surface Model and Particle Swarm Optimization Algorithm Based on the Model-Optimization Method for Improving Soil Moisture Simulation in a Semi-Arid Region.

    Science.gov (United States)

    Yang, Qidong; Zuo, Hongchao; Li, Weidong

    2016-01-01

    Improving the capability of land-surface process models to simulate soil moisture assists in better understanding the atmosphere-land interaction. In semi-arid regions, due to limited near-surface observational data and large errors in large-scale parameters obtained by the remote sensing method, there exist uncertainties in land surface parameters, which can cause large offsets between the simulated results of land-surface process models and the observational data for the soil moisture. In this study, observational data from the Semi-Arid Climate Observatory and Laboratory (SACOL) station in the semi-arid loess plateau of China were divided into three datasets: summer, autumn, and summer-autumn. By combing the particle swarm optimization (PSO) algorithm and the land-surface process model SHAW (Simultaneous Heat and Water), the soil and vegetation parameters that are related to the soil moisture but difficult to obtain by observations are optimized using three datasets. On this basis, the SHAW model was run with the optimized parameters to simulate the characteristics of the land-surface process in the semi-arid loess plateau. Simultaneously, the default SHAW model was run with the same atmospheric forcing as a comparison test. Simulation results revealed the following: parameters optimized by the particle swarm optimization algorithm in all simulation tests improved simulations of the soil moisture and latent heat flux; differences between simulated results and observational data are clearly reduced, but simulation tests involving the adoption of optimized parameters cannot simultaneously improve the simulation results for the net radiation, sensible heat flux, and soil temperature. Optimized soil and vegetation parameters based on different datasets have the same order of magnitude but are not identical; soil parameters only vary to a small degree, but the variation range of vegetation parameters is large.

  8. Modelling and Optimizing Mathematics Learning in Children

    Science.gov (United States)

    Käser, Tanja; Busetto, Alberto Giovanni; Solenthaler, Barbara; Baschera, Gian-Marco; Kohn, Juliane; Kucian, Karin; von Aster, Michael; Gross, Markus

    2013-01-01

    This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalculia or difficulties in learning mathematics. The student model consists of a dynamic…

  9. Desiccant wheel thermal performance modeling for indoor humidity optimal control

    International Nuclear Information System (INIS)

    Wang, Nan; Zhang, Jiangfeng; Xia, Xiaohua

    2013-01-01

    Highlights: • An optimal humidity control model is formulated to control the indoor humidity. • MPC strategy is used to implement the optimal operation solution. • Practical applications of the MPC strategy is illustrated by the case study. - Abstract: Thermal comfort is an important concern in the energy efficiency improvement of commercial buildings. Thermal comfort research focuses mostly on temperature control, but humidity control is an important aspect to maintain indoor comfort too. In this paper, an optimal humidity control model (OHCM) is presented. Model predictive control (MPC) strategy is applied to implement the optimal operation of the desiccant wheel during working hours of a commercial building. The OHCM is revised to apply the MPC strategy. A case is studied to illustrate the practical applications of the MPC strategy

  10. Existence and characterization of optimal control in mathematics model of diabetics population

    Science.gov (United States)

    Permatasari, A. H.; Tjahjana, R. H.; Udjiani, T.

    2018-03-01

    Diabetes is a chronic disease with a huge burden affecting individuals and the whole society. In this paper, we constructed the optimal control mathematical model by applying a strategy to control the development of diabetic population. The constructed mathematical model considers the dynamics of disabled people due to diabetes. Moreover, an optimal control approach is proposed in order to reduce the burden of pre-diabetes. Implementation of control is done by preventing the pre-diabetes develop into diabetics with and without complications. The existence of optimal control and characterization of optimal control is discussed in this paper. Optimal control is characterized by applying the Pontryagin minimum principle. The results indicate that there is an optimal control in optimization problem in mathematics model of diabetic population. The effect of the optimal control variable (prevention) is strongly affected by the number of healthy people.

  11. Profile-driven regression for modeling and runtime optimization of mobile networks

    DEFF Research Database (Denmark)

    McClary, Dan; Syrotiuk, Violet; Kulahci, Murat

    2010-01-01

    Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...

  12. Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models.

    Science.gov (United States)

    Pozo, Carlos; Marín-Sanguino, Alberto; Alves, Rui; Guillén-Gosálbez, Gonzalo; Jiménez, Laureano; Sorribas, Albert

    2011-08-25

    Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

  13. Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

    Directory of Open Access Journals (Sweden)

    Sorribas Albert

    2011-08-01

    Full Text Available Abstract Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.

  14. A tool for efficient, model-independent management optimization under uncertainty

    Science.gov (United States)

    White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

    2018-01-01

    To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

  15. Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Aleman, Dionne M [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King' s College Road, Toronto, ON M5S 3G8 (Canada); Glaser, Daniel [Division of Optimization and Systems Theory, Department of Mathematics, Royal Institute of Technology, Stockholm (Sweden); Romeijn, H Edwin [Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109-2117 (United States); Dempsey, James F, E-mail: aleman@mie.utoronto.c, E-mail: romeijn@umich.ed, E-mail: jfdempsey@viewray.co [ViewRay, Inc. 2 Thermo Fisher Way, Village of Oakwood, OH 44146 (United States)

    2010-09-21

    One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications.

  16. Interior point algorithms: guaranteed optimality for fluence map optimization in IMRT

    International Nuclear Information System (INIS)

    Aleman, Dionne M; Glaser, Daniel; Romeijn, H Edwin; Dempsey, James F

    2010-01-01

    One of the most widely studied problems of the intensity-modulated radiation therapy (IMRT) treatment planning problem is the fluence map optimization (FMO) problem, the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. For a given set of beams, the fluences of the beamlets can drastically affect the quality of the treatment plan, and thus it is critical to obtain good fluence maps for radiation delivery. Although several approaches have been shown to yield good solutions to the FMO problem, these solutions are not guaranteed to be optimal. This shortcoming can be attributed to either optimization model complexity or properties of the algorithms used to solve the optimization model. We present a convex FMO formulation and an interior point algorithm that yields an optimal treatment plan in seconds, making it a viable option for clinical applications.

  17. Multiobjective optimization of an extremal evolution model

    International Nuclear Information System (INIS)

    Elettreby, M.F.

    2004-09-01

    We propose a two-dimensional model for a co-evolving ecosystem that generalizes the extremal coupled map lattice model. The model takes into account the concept of multiobjective optimization. We find that the system self-organizes into a critical state. The distributions of the distances between subsequent mutations as well as the distribution of avalanches sizes follow power law. (author)

  18. Optimal Application Timing of Pest Control Tactics in Nonautonomous Pest Growth Model

    OpenAIRE

    Zhang, Shujuan; Liang, Juhua; Tang, Sanyi

    2014-01-01

    Considering the effects of the living environment on growth of populations, it is unrealistic to assume that the growth rates of predator and prey are all constants in the models with integrated pest management (IPM) strategies. Therefore, a nonautonomous predator-prey system with impulsive effect is developed and investigated in the present work. In order to determine the optimal application timing of IPM tactics, the threshold value which guarantees the stability of pest-free periodic solut...

  19. Double-Bottom Chaotic Map Particle Swarm Optimization Based on Chi-Square Test to Determine Gene-Gene Interactions

    Science.gov (United States)

    Yang, Cheng-Hong; Chang, Hsueh-Wei

    2014-01-01

    Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer. PMID:24895547

  20. Millimeter-wave small-signal modeling with optimizing sensitive-parameters for metamorphic high electron mobility transistors

    International Nuclear Information System (INIS)

    Moon, S-W; Baek, Y-H; Han, M; Rhee, J-K; Kim, S-D; Oh, J-H

    2010-01-01

    In this paper, we present a simple and reliable technique for determining the small-signal equivalent circuit model parameters of the 0.1 µm metamorphic high electron mobility transistors (MHEMTs) in a millimeter-wave frequency range. The initial eight extrinsic parameters of the MHEMT are extracted using two S-parameter (scattering parameter) sets measured under the pinched-off and zero-biased cold field-effect transistor conditions by avoiding the forward gate biasing. Furthermore, highly calibration-sensitive values of the R s , L s and C pd are optimized by using a gradient optimization method to improve the modeling accuracy. The accuracy enhancement of this procedure is successfully verified with an excellent correlation between the measured and calculated S-parameters up to 65 GHz

  1. Discounted cost model for condition-based maintenance optimization

    International Nuclear Information System (INIS)

    Weide, J.A.M. van der; Pandey, M.D.; Noortwijk, J.M. van

    2010-01-01

    This paper presents methods to evaluate the reliability and optimize the maintenance of engineering systems that are damaged by shocks or transients arriving randomly in time and overall degradation is modeled as a cumulative stochastic point process. The paper presents a conceptually clear and comprehensive derivation of formulas for computing the discounted cost associated with a maintenance policy combining both condition-based and age-based criteria for preventive maintenance. The proposed discounted cost model provides a more realistic basis for optimizing the maintenance policies than those based on the asymptotic, non-discounted cost rate criterion.

  2. Optimal Cluster Mill Pass Scheduling With an Accurate and Rapid New Strip Crown Model

    International Nuclear Information System (INIS)

    Malik, Arif S.; Grandhi, Ramana V.; Zipf, Mark E.

    2007-01-01

    Besides the requirement to roll coiled sheet at high levels of productivity, the optimal pass scheduling of cluster-type reversing cold mills presents the added challenge of assigning mill parameters that facilitate the best possible strip flatness. The pressures of intense global competition, and the requirements for increasingly thinner, higher quality specialty sheet products that are more difficult to roll, continue to force metal producers to commission innovative flatness-control technologies. This means that during the on-line computerized set-up of rolling mills, the mathematical model should not only determine the minimum total number of passes and maximum rolling speed, it should simultaneously optimize the pass-schedule so that desired flatness is assured, either by manual or automated means. In many cases today, however, on-line prediction of strip crown and corresponding flatness for the complex cluster-type rolling mills is typically addressed either by trial and error, by approximate deflection models for equivalent vertical roll-stacks, or by non-physical pattern recognition style models. The abundance of the aforementioned methods is largely due to the complexity of cluster-type mill configurations and the lack of deflection models with sufficient accuracy and speed for on-line use. Without adequate assignment of the pass-schedule set-up parameters, it may be difficult or impossible to achieve the required strip flatness. In this paper, we demonstrate optimization of cluster mill pass-schedules using a new accurate and rapid strip crown model. This pass-schedule optimization includes computations of the predicted strip thickness profile to validate mathematical constraints. In contrast to many of the existing methods for on-line prediction of strip crown and flatness on cluster mills, the demonstrated method requires minimal prior tuning and no extensive training with collected mill data. To rapidly and accurately solve the multi-contact problem

  3. Accuracy Analysis and Parameters Optimization in Urban Flood Simulation by PEST Model

    Science.gov (United States)

    Keum, H.; Han, K.; Kim, H.; Ha, C.

    2017-12-01

    The risk of urban flooding has been increasing due to heavy rainfall, flash flooding and rapid urbanization. Rainwater pumping stations, underground reservoirs are used to actively take measures against flooding, however, flood damage from lowlands continues to occur. Inundation in urban areas has resulted in overflow of sewer. Therefore, it is important to implement a network system that is intricately entangled within a city, similar to the actual physical situation and accurate terrain due to the effects on buildings and roads for accurate two-dimensional flood analysis. The purpose of this study is to propose an optimal scenario construction procedure watershed partitioning and parameterization for urban runoff analysis and pipe network analysis, and to increase the accuracy of flooded area prediction through coupled model. The establishment of optimal scenario procedure was verified by applying it to actual drainage in Seoul. In this study, optimization was performed by using four parameters such as Manning's roughness coefficient for conduits, watershed width, Manning's roughness coefficient for impervious area, Manning's roughness coefficient for pervious area. The calibration range of the parameters was determined using the SWMM manual and the ranges used in the previous studies, and the parameters were estimated using the automatic calibration method PEST. The correlation coefficient showed a high correlation coefficient for the scenarios using PEST. The RPE and RMSE also showed high accuracy for the scenarios using PEST. In the case of RPE, error was in the range of 13.9-28.9% in the no-parameter estimation scenarios, but in the scenario using the PEST, the error range was reduced to 6.8-25.7%. Based on the results of this study, it can be concluded that more accurate flood analysis is possible when the optimum scenario is selected by determining the appropriate reference conduit for future urban flooding analysis and if the results is applied to various

  4. Optimization Research of Generation Investment Based on Linear Programming Model

    Science.gov (United States)

    Wu, Juan; Ge, Xueqian

    Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

  5. Optimal control of an invasive species using a reaction-diffusion model and linear programming

    Science.gov (United States)

    Bonneau, Mathieu; Johnson, Fred A.; Smith, Brian J.; Romagosa, Christina M.; Martin, Julien; Mazzotti, Frank J.

    2017-01-01

    Managing an invasive species is particularly challenging as little is generally known about the species’ biological characteristics in its new habitat. In practice, removal of individuals often starts before the species is studied to provide the information that will later improve control. Therefore, the locations and the amount of control have to be determined in the face of great uncertainty about the species characteristics and with a limited amount of resources. We propose framing spatial control as a linear programming optimization problem. This formulation, paired with a discrete reaction-diffusion model, permits calculation of an optimal control strategy that minimizes the remaining number of invaders for a fixed cost or that minimizes the control cost for containment or protecting specific areas from invasion. We propose computing the optimal strategy for a range of possible model parameters, representing current uncertainty on the possible invasion scenarios. Then, a best strategy can be identified depending on the risk attitude of the decision-maker. We use this framework to study the spatial control of the Argentine black and white tegus (Salvator merianae) in South Florida. There is uncertainty about tegu demography and we considered several combinations of model parameters, exhibiting various dynamics of invasion. For a fixed one-year budget, we show that the risk-averse strategy, which optimizes the worst-case scenario of tegus’ dynamics, and the risk-neutral strategy, which optimizes the expected scenario, both concentrated control close to the point of introduction. A risk-seeking strategy, which optimizes the best-case scenario, focuses more on models where eradication of the species in a cell is possible and consists of spreading control as much as possible. For the establishment of a containment area, assuming an exponential growth we show that with current control methods it might not be possible to implement such a strategy for some of the

  6. Research on NC laser combined cutting optimization model of sheet metal parts

    Science.gov (United States)

    Wu, Z. Y.; Zhang, Y. L.; Li, L.; Wu, L. H.; Liu, N. B.

    2017-09-01

    The optimization problem for NC laser combined cutting of sheet metal parts was taken as the research object in this paper. The problem included two contents: combined packing optimization and combined cutting path optimization. In the problem of combined packing optimization, the method of “genetic algorithm + gravity center NFP + geometric transformation” was used to optimize the packing of sheet metal parts. In the problem of combined cutting path optimization, the mathematical model of cutting path optimization was established based on the parts cutting constraint rules of internal contour priority and cross cutting. The model played an important role in the optimization calculation of NC laser combined cutting.

  7. Optimal interconnection and renewable targets for north-west Europe

    International Nuclear Information System (INIS)

    Lynch, Muireann Á.; Tol, Richard S.J.; O'Malley, Mark J.

    2012-01-01

    We present a mixed-integer, linear programming model for determining optimal interconnection for a given level of renewable generation using a cost minimisation approach. Optimal interconnection and capacity investment decisions are determined under various targets for renewable penetration. The model is applied to a test system for eight regions in Northern Europe. It is found that considerations on the supply side dominate demand side considerations when determining optimal interconnection investment: interconnection is found to decrease generation capacity investment and total costs only when there is a target for renewable generation. Higher wind integration costs see a concentration of wind in high-wind regions with interconnection to other regions. - Highlights: ► We use mixed-integer linear programming to determine optimal interconnection locations for given renewable targets. ► The model is applied to a test system for eight regions in Northern Europe. ► Interconnection reduces costs only when there is a renewable target. ► Wind integration costs affect the interconnection portfolio.

  8. A Fuzzy Optimization Model for High-Speed Railway Timetable Rescheduling

    Directory of Open Access Journals (Sweden)

    Li Wang

    2012-01-01

    Full Text Available A fuzzy optimization model based on improved symmetric tolerance approach is introduced, which allows for rescheduling high-speed railway timetable under unexpected interferences. The model nests different parameters of the soft constraints with uncertainty margin to describe their importance to the optimization purpose and treats the objective in the same manner. Thus a new optimal instrument is expected to achieve a new timetable subject to little slack of constraints. The section between Nanjing and Shanghai, which is the busiest, of Beijing-Shanghai high-speed rail line in China is used as the simulated measurement. The fuzzy optimization model provides an accurate approximation on train running time and headway time, and hence the results suggest that the number of seriously impacted trains and total delay time can be reduced significantly subject to little cost and risk.

  9. An optimal control model of crop thinning in viticulture

    Directory of Open Access Journals (Sweden)

    Schamel Guenter H.

    2016-01-01

    Full Text Available We develop an economic model of cluster thinning in viticulture to control for grape quantity harvested and grape quality, applying a simple optimal control model with the aim to raise grape quality and related economic profits. The model maximizes vineyard owner profits and allows to discuss two relevant scenarios using a phase diagram analysis: (1 when the initial grape quantity is sufficiently small, thinning grapes will not be optimal and (2 when the initial grape quantity is high enough, it is optimal to thin grapes from the beginning of the relevant planning horizon and to reduce the quantity over time until the stock of grapes arrives at its optimum. Depending on the model's parameters, the “stopping time” for thinning grapes is reached sooner or later. After the stopping time, grape quantity evolves solely according to natural decay. The results relate to observed dynamics in viticulture and for other horticultural crops.

  10. Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory

    Science.gov (United States)

    Dees, Patrick D.; Zwack, Matthew R.; Edwards, Stephen; Steffens, Michael

    2016-01-01

    into Conceptual and Pre-Conceptual design, knowledge of the effects originating from changes to the vehicle must be calculated. In order to do this, a model capable of quantitatively describing any vehicle within the entire design space under consideration must be constructed. This model must be based upon analysis of acceptable fidelity, which in this work comes from POST. Design space interrogation can be achieved with surrogate modeling, a parametric, polynomial equation representing a tool. A surrogate model must be informed by data from the tool with enough points to represent the solution space for the chosen number of variables with an acceptable level of error. Therefore, Design Of Experiments (DOE) is used to select points within the design space to maximize information gained on the design space while minimizing number of data points required. To represent a design space with a non-trivial number of variable parameters the number of points required still represent an amount of work which would take an inordinate amount of time via the current paradigm of manual analysis, and so an automated method was developed. The best practices of expert trajectory analysts working within NASA Marshall's Advanced Concepts Office (ACO) were implemented within a tool called multiPOST. These practices include how to use the output data from a previous run of POST to inform the next, determining whether a trajectory solution is feasible from a real-world perspective, and how to handle program execution errors. The tool was then augmented with multiprocessing capability to enable analysis on multiple trajectories simultaneously, allowing throughput to scale with available computational resources. In this update to the previous work the authors discuss issues with the method and solutions.

  11. A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    2015-12-01

    Full Text Available This article proposes a new model to address the design problem of a sustainable regional logistics network with uncertainty in future logistics demand. In the proposed model, the future logistics demand is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to logistics service capacity and environmental impacts is incorporated to consider the sustainability of logistics network design. The proposed model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of future logistics demand aims to minimize a risk-averse objective by determining the optimal location and size of logistics parks with CO2 emission taxes consideration. The second stage after the uncertain logistics demand has been determined is a scenario-based stochastic logistics service route choices equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, genetic algorithm, and Gauss–Seidel decomposition approach, is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that total social welfare of the logistics system depends very much on the level of uncertainty in future logistics demand, capital budget for logistics parks, and confidence levels of the chance constraints.

  12. A simplified lumped model for the optimization of post-buckled beam architecture wideband generator

    Science.gov (United States)

    Liu, Weiqun; Formosa, Fabien; Badel, Adrien; Hu, Guangdi

    2017-11-01

    Buckled beams structures are a classical kind of bistable energy harvesters which attract more and more interests because of their capability to scavenge energy over a large frequency band in comparison with linear generator. The usual modeling approach uses the Galerkin mode discretization method with relatively high complexity, while the simplification with a single-mode solution lacks accuracy. It stems on the optimization of the energy potential features to finally define the physical and geometrical parameters. Therefore, in this paper, a simple lumped model is proposed with explicit relationship between the potential shape and parameters to allow efficient design of bistable beams based generator. The accuracy of the approximation model is studied with the effectiveness of application analyzed. Moreover, an important fact, that the bending stiffness has little influence on the potential shape with low buckling level and the sectional area determined, is found. This feature extends the applicable range of the model by utilizing the design of high moment of inertia. Numerical investigations demonstrate that the proposed model is a simple and reliable tool for design. An optimization example of using the proposed model is demonstrated with satisfactory performance.

  13. Production-log base model for carbonate permeability distribution and steam flood optimization

    Energy Technology Data Exchange (ETDEWEB)

    Ahamed, S.F.; Choudhry, M.A.; Abdulbaqi, J.B. [Kuwait Gulf Oil Co. (Kuwait)

    2008-10-15

    This paper presented a model for the effective management of primary and thermal oil recovery operations in the Wafra Field in Kuwait, where a small huff and puff project was carried out in 1998 to determine if steam injection was a feasible recovery option for the field. The Eocene heavy oil reservoirs of the Wafra Field are carbonate rock admixtures with gypsum and anhydrite. They are the shallowest of the field's productive horizons and exhibit a high degree of fluid flow heterogeneity. The assessment of vertical and lateral permeability variation is a key factor for success of the reservoir development plan. Steam injection began in 2006 in a small scale test (SST) to determine if the innovative technology could produce steam from effluent water and to test the viability of steam injection in carbonate reservoirs. Following the success of the SST, a large scale pilot (LSP) is schedule to start in 2009. It can be used for completion strategies of injectors and producers in steam injection. The model showed that the productivity of the Eocene wells could be correlated with common available logs to develop a log based-permeability model. A series of cross plots for the perforated intervals of high and low productivity wells were constructed to develop a relationship between well productivity and location of log parameters on the plots. A relationship between rock quality, productivity and conventional log parameters was established. It was concluded that the vertical permeability and interwell continuity in the Eocene wells can be used to optimize new well placement for horizontal and vertical infill drilling. The model is also an effective tool to predict the steam injectivity profile to understand the anomalies related to temperature-depth distribution. The model can be used to improve the efficiency of formation heating by optimizing the steam flood process and steam pattern well completion. 16 refs.

  14. Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data.

    Science.gov (United States)

    García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

    2016-01-28

    Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC-MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc . Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC-MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

  15. Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

    Directory of Open Access Journals (Sweden)

    Paulino José García Nieto

    2016-01-01

    Full Text Available Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC in combination with multivariate adaptive regression splines (MARS technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed.

  16. Hybrid ABC Optimized MARS-Based Modeling of the Milling Tool Wear from Milling Run Experimental Data

    Science.gov (United States)

    García Nieto, Paulino José; García-Gonzalo, Esperanza; Ordóñez Galán, Celestino; Bernardo Sánchez, Antonio

    2016-01-01

    Milling cutters are important cutting tools used in milling machines to perform milling operations, which are prone to wear and subsequent failure. In this paper, a practical new hybrid model to predict the milling tool wear in a regular cut, as well as entry cut and exit cut, of a milling tool is proposed. The model was based on the optimization tool termed artificial bee colony (ABC) in combination with multivariate adaptive regression splines (MARS) technique. This optimization mechanism involved the parameter setting in the MARS training procedure, which significantly influences the regression accuracy. Therefore, an ABC–MARS-based model was successfully used here to predict the milling tool flank wear (output variable) as a function of the following input variables: the time duration of experiment, depth of cut, feed, type of material, etc. Regression with optimal hyperparameters was performed and a determination coefficient of 0.94 was obtained. The ABC–MARS-based model's goodness of fit to experimental data confirmed the good performance of this model. This new model also allowed us to ascertain the most influential parameters on the milling tool flank wear with a view to proposing milling machine's improvements. Finally, conclusions of this study are exposed. PMID:28787882

  17. Energy savings by optimization of drying processes through use of models and effective control; Energibesparelser ved optimering af toerreprocesser gennem anvendelse af modeller og effektiv regulering

    Energy Technology Data Exchange (ETDEWEB)

    Didriksen, H.; Sandvig Nielsen, J.; Weel Hansen, M.

    2001-06-01

    The aim of the project is to present a procedure to optimize existing drying processes. The optimization deals with energy consumption, capacity utilization and product quality. Other factors can also be included in the optimization, e.g. minimization of volume of discharged air. The optimization of existing drying processes will use calculation tool based on a mathematical simulation model for the process to calculate the most optimum operation situation on the basis of given conditions. In the project mathematical models have been developed precisely with this aim. The calculation tools have been developed with a user interface so that the tools can be used by technical staff in industrial companies and by consultants. The project also illustrates control of drying processes. Based on the developed models, the effect of using different types of control strategies by means of model simulations is illustrated. Three types of drying processes are treated: drum dryers, disc dryers and drying chambers. The work with the development of the simulation models has been very central in the project, as these have to be the basis for the optimization of the processes. The work is based on a large amount of information from academical literature and knowledge and experience about modelling thermal processes at dk-TEKNIK. The models constitute the core in the simulation programmes. The models describe the most important physical effects in connection with mass and energy transfer and transport under the drying for the three treated drying technologies. (EHS)

  18. Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid.

    Science.gov (United States)

    Arefi-Oskoui, Samira; Khataee, Alireza; Vatanpour, Vahid

    2017-07-10

    In this research, MgAl-CO 3 2- nanolayered double hydroxide (NLDH) was synthesized through a facile coprecipitation method, followed by a hydrothermal treatment. The prepared NLDHs were used as a hydrophilic nanofiller for improving the performance of the PVDF-based ultrafiltration membranes. The main objective of this research was to obtain the optimized formula of NLDH/PVDF nanocomposite membrane presenting the best performance using computational techniques as a cost-effective method. For this aim, an artificial neural network (ANN) model was developed for modeling and expressing the relationship between the performance of the nanocomposite membrane (pure water flux, protein flux and flux recovery ratio) and the affecting parameters including the NLDH, PVP 29000 and polymer concentrations. The effects of the mentioned parameters and the interaction between the parameters were investigated using the contour plot predicted with the developed model. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle techniques were applied to characterize the nanocomposite membranes and to interpret the predictions of the ANN model. The developed ANN model was introduced to genetic algorithm (GA) as a bioinspired optimizer to determine the optimum values of input parameters leading to high pure water flux, protein flux, and flux recovery ratio. The optimum values for NLDH, PVP 29000 and the PVDF concentration were determined to be 0.54, 1, and 18 wt %, respectively. The performance of the nanocomposite membrane prepared using the optimum values proposed by GA was investigated experimentally, in which the results were in good agreement with the values predicted by ANN model with error lower than 6%. This good agreement confirmed that the nanocomposite membranes prformance could be successfully modeled and optimized by ANN-GA system.

  19. SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

    International Nuclear Information System (INIS)

    Zhou, Z; Folkert, M; Wang, J

    2016-01-01

    Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

  20. SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

    2016-06-15

    Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

  1. Determining the optimal number of Kanban in multi-products supply chain system

    Science.gov (United States)

    Widyadana, G. A.; Wee, H. M.; Chang, Jer-Yuan

    2010-02-01

    Kanban, a key element of just-in-time system, is a re-order card or signboard giving instruction or triggering the pull system to manufacture or supply a component based on actual usage of material. There are two types of Kanban: production Kanban and withdrawal Kanban. This study uses optimal and meta-heuristic methods to determine the Kanban quantity and withdrawal lot sizes in a supply chain system. Although the mix integer programming method gives an optimal solution, it is not time efficient. For this reason, the meta-heuristic methods are suggested. In this study, a genetic algorithm (GA) and a hybrid of genetic algorithm and simulated annealing (GASA) are used. The study compares the performance of GA and GASA with that of the optimal method using MIP. The given problems show that both GA and GASA result in a near optimal solution, and they outdo the optimal method in term of run time. In addition, the GASA heuristic method gives a better performance than the GA heuristic method.

  2. Aggregate meta-models for evolutionary multiobjective and many-objective optimization

    Czech Academy of Sciences Publication Activity Database

    Pilát, Martin; Neruda, Roman

    Roč. 116, 20 September (2013), s. 392-402 ISSN 0925-2312 R&D Projects: GA ČR GAP202/11/1368 Institutional support: RVO:67985807 Keywords : evolutionary algorithms * multiobjective optimization * many-objective optimization * surrogate models * meta-models * memetic algorithm Subject RIV: IN - Informatics, Computer Science Impact factor: 2.005, year: 2013

  3. Optimizing model. 1. Insemination, replacement, seasonal production and cash flow.

    NARCIS (Netherlands)

    Delorenzo, M.A.; Spreen, T.H.; Bryan, G.R.; Beede, D.K.; Arendonk, van J.A.M.

    1992-01-01

    Dynamic programming to solve the Markov decision process problem of optimal insemination and replacement decisions was adapted to address large dairy herd management decision problems in the US. Expected net present values of cow states (151,200) were used to determine the optimal policy. States

  4. A tutorial on fundamental model structures for railway timetable optimization

    DEFF Research Database (Denmark)

    Harrod, Steven

    2012-01-01

    This guide explains the role of railway timetables relative to all other railway scheduling activities, and then presents four fundamental timetable formulations suitable for optimization. Timetabling models may be classified according to whether they explicitly model the track structure, and whe......This guide explains the role of railway timetables relative to all other railway scheduling activities, and then presents four fundamental timetable formulations suitable for optimization. Timetabling models may be classified according to whether they explicitly model the track structure...

  5. Topology optimization of a pseudo 3D thermofluid heat sink model

    DEFF Research Database (Denmark)

    Haertel, Jan H. K.; Engelbrecht, Kurt; Lazarov, Boyan S.

    2018-01-01

    sink and a fixed heat production rate in the base plate. Optimized designs are presented and the resulting fin geometry is discussed from a thermal engineering point of view and compared to fin shapes resulting from a pressure drop minimization objective. Parametric studies are conducted to analyze......This paper investigates the application of density-based topology optimization to the design of air-cooled forced convection heat sinks. To reduce the computational burden that is associated with a full 3D optimization, a pseudo 3D optimization model comprising a 2D modeled conducting metal base...... layer and a thermally coupled 2D modeled thermofluid design layer is used. Symmetry conditions perpendicular to the flow direction are applied to generate periodic heat sink designs. The optimization objective is to minimize the heat sink heat transfer resistance for a fixed pressure drop over the heat...

  6. Modeling and optimization of dough recipe for breadsticks

    Science.gov (United States)

    Krivosheev, A. Yu; Ponomareva, E. I.; Zhuravlev, A. A.; Lukina, S. I.; Alekhina, N. N.

    2018-05-01

    During the work, the authors studied the combined effect of non-traditional raw materials on indicators of quality breadsticks, mathematical methods of experiment planning were applied. The main factors chosen were the dosages of flaxseed flour and grape seed oil. The output parameters were the swelling factor of the products and their strength. Optimization of the formulation composition of the dough for bread sticks was carried out by experimental- statistical methods. As a result of the experiment, mathematical models were constructed in the form of regression equations, adequately describing the process of studies. The statistical processing of the experimental data was carried out by the criteria of Student, Cochran and Fisher (with a confidence probability of 0.95). A mathematical interpretation of the regression equations was given. Optimization of the formulation of the dough for bread sticks was carried out by the method of uncertain Lagrange multipliers. The rational values of the factors were determined: the dosage of flaxseed flour - 14.22% and grape seed oil - 7.8%, ensuring the production of products with the best combination of swelling ratio and strength. On the basis of the data obtained, a recipe and a method for the production of breadsticks "Idea" were proposed (TU (Russian Technical Specifications) 9117-443-02068106-2017).

  7. Optimal model-free prediction from multivariate time series

    Science.gov (United States)

    Runge, Jakob; Donner, Reik V.; Kurths, Jürgen

    2015-05-01

    Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostly to the univariate case. Therefore, selection strategies are needed that harness the available information as efficiently as possible. Since often the right combination of predictors matters, ideally all subsets of possible predictors should be tested for their predictive power, but the exponentially growing number of combinations makes such an approach computationally prohibitive. Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal preselection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable. The information-theoretic optimality is derived and practical selection criteria are discussed. As demonstrated for multivariate nonlinear stochastic delay processes, the optimal scheme can even be less computationally expensive than commonly used suboptimal schemes like forward selection. The method suggests a general framework to apply the optimal model-free approach to select variables and subsequently fit a model to further improve a prediction or learn statistical dependencies. The performance of this framework is illustrated on a climatological index of El Niño Southern Oscillation.

  8. Perturbing engine performance measurements to determine optimal engine control settings

    Science.gov (United States)

    Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna

    2014-12-30

    Methods and systems for optimizing a performance of a vehicle engine are provided. The method includes determining an initial value for a first engine control parameter based on one or more detected operating conditions of the vehicle engine, determining a value of an engine performance variable, and artificially perturbing the determined value of the engine performance variable. The initial value for the first engine control parameter is then adjusted based on the perturbed engine performance variable causing the engine performance variable to approach a target engine performance variable. Operation of the vehicle engine is controlled based on the adjusted initial value for the first engine control parameter. These acts are repeated until the engine performance variable approaches the target engine performance variable.

  9. Behavioral optimization models for multicriteria portfolio selection

    Directory of Open Access Journals (Sweden)

    Mehlawat Mukesh Kumar

    2013-01-01

    Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

  10. Portfolio optimization for index tracking modelling in Malaysia stock market

    Science.gov (United States)

    Siew, Lam Weng; Jaaman, Saiful Hafizah; Ismail, Hamizun

    2016-06-01

    Index tracking is an investment strategy in portfolio management which aims to construct an optimal portfolio to generate similar mean return with the stock market index mean return without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using the optimization model which adopts regression approach in tracking the benchmark stock market index return. In this study, the data consists of weekly price of stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2013. The results of this study show that the optimal portfolio is able to track FBMKLCI Index at minimum tracking error of 1.0027% with 0.0290% excess mean return over the mean return of FBMKLCI Index. The significance of this study is to construct the optimal portfolio using optimization model which adopts regression approach in tracking the stock market index without purchasing all index components.

  11. A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

    Science.gov (United States)

    Tong, Cao; Gong, Haili

    2018-03-01

    This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.

  12. Model-based optimization strategy of chiller driven liquid desiccant dehumidifier with genetic algorithm

    International Nuclear Information System (INIS)

    Wang, Xinli; Cai, Wenjian; Lu, Jiangang; Sun, Youxian; Zhao, Lei

    2015-01-01

    This study presents a model-based optimization strategy for an actual chiller driven dehumidifier of liquid desiccant dehumidification system operating with lithium chloride solution. By analyzing the characteristics of the components, energy predictive models for the components in the dehumidifier are developed. To minimize the energy usage while maintaining the outlet air conditions at the pre-specified set-points, an optimization problem is formulated with an objective function, the constraints of mechanical limitations and components interactions. Model-based optimization strategy using genetic algorithm is proposed to obtain the optimal set-points for desiccant solution temperature and flow rate, to minimize the energy usage in the dehumidifier. Experimental studies on an actual system are carried out to compare energy consumption between the proposed optimization and the conventional strategies. The results demonstrate that energy consumption using the proposed optimization strategy can be reduced by 12.2% in the dehumidifier operation. - Highlights: • Present a model-based optimization strategy for energy saving in LDDS. • Energy predictive models for components in dehumidifier are developed. • The Optimization strategy are applied and tested in an actual LDDS. • Optimization strategy can achieve energy savings by 12% during operation

  13. [Multi-mathematical modelings for compatibility optimization of Jiangzhi granules].

    Science.gov (United States)

    Yang, Ming; Zhang, Li; Ge, Yingli; Lu, Yanliu; Ji, Guang

    2011-12-01

    To investigate into the method of "multi activity index evaluation and combination optimized of mult-component" for Chinese herbal formulas. According to the scheme of uniform experimental design, efficacy experiment, multi index evaluation, least absolute shrinkage, selection operator (LASSO) modeling, evolutionary optimization algorithm, validation experiment, we optimized the combination of Jiangzhi granules based on the activity indexes of blood serum ALT, ALT, AST, TG, TC, HDL, LDL and TG level of liver tissues, ratio of liver tissue to body. Analytic hierarchy process (AHP) combining with criteria importance through intercriteria correlation (CRITIC) for multi activity index evaluation was more reasonable and objective, it reflected the information of activity index's order and objective sample data. LASSO algorithm modeling could accurately reflect the relationship between different combination of Jiangzhi granule and the activity comprehensive indexes. The optimized combination of Jiangzhi granule showed better values of the activity comprehensive indexed than the original formula after the validation experiment. AHP combining with CRITIC can be used for multi activity index evaluation and LASSO algorithm, it is suitable for combination optimized of Chinese herbal formulas.

  14. Modeling and optimization of cloud-ready and content-oriented networks

    CERN Document Server

    Walkowiak, Krzysztof

    2016-01-01

    This book focuses on modeling and optimization of cloud-ready and content-oriented networks in the context of different layers and accounts for specific constraints following from protocols and technologies used in a particular layer. It addresses a wide range of additional constraints important in contemporary networks, including various types of network flows, survivability issues, multi-layer networking, and resource location. The book presents recent existing and new results in a comprehensive and cohesive way. The contents of the book are organized in five chapters, which are mostly self-contained. Chapter 1 briefly presents information on cloud computing and content-oriented services, and introduces basic notions and concepts of network modeling and optimization. Chapter 2 covers various optimization problems that arise in the context of connection-oriented networks. Chapter 3 focuses on modeling and optimization of Elastic Optical Networks. Chapter 4 is devoted to overlay networks. The book concludes w...

  15. Learning optimal quantum models is NP-hard

    Science.gov (United States)

    Stark, Cyril J.

    2018-02-01

    Physical modeling translates measured data into a physical model. Physical modeling is a major objective in physics and is generally regarded as a creative process. How good are computers at solving this task? Here, we show that in the absence of physical heuristics, the inference of optimal quantum models cannot be computed efficiently (unless P=NP ). This result illuminates rigorous limits to the extent to which computers can be used to further our understanding of nature.

  16. Application of optimization modeling to groundwater remediation at US Department of Energy facilities

    International Nuclear Information System (INIS)

    Bakr, A.A.; Dal Santo, D.J.; Smalley, R.C.; Phillips, E.C.

    1988-01-01

    This paper outlines and explores the fundamentals of the current strategies for groundwater hydraulic and quality management modeling and presents a scheme for the application of such strategies to DOE facilities. The discussion focuses on the DOE-Savannah River Operations (DOE-SR) facility. Remediation of contaminated groundwater at active and abandoned waste disposal sites has become a major element of environmental programs. Traditional groundwater remediation programs (e.g., pumping and treatment) may not represent optimal water quality management strategies at sites to be remediated. Complex, interrelated environmental (geologic/geohydrologic), institutional, engineering, and economic conditions at a site may require a more comprehensive management strategy. Groundwater management models based on the principles of operations research have been developed and used to determine optimal management strategies for water resources needs and for hypothetical remediation programs. Strategies for groundwater remediation programs have ranged from the simple removal of groundwater to complex, hydraulic gradient control programs involving groundwater removal, treatment, and recharge

  17. Solving Optimal Control Problem of Monodomain Model Using Hybrid Conjugate Gradient Methods

    Directory of Open Access Journals (Sweden)

    Kin Wei Ng

    2012-01-01

    Full Text Available We present the numerical solutions for the PDE-constrained optimization problem arising in cardiac electrophysiology, that is, the optimal control problem of monodomain model. The optimal control problem of monodomain model is a nonlinear optimization problem that is constrained by the monodomain model. The monodomain model consists of a parabolic partial differential equation coupled to a system of nonlinear ordinary differential equations, which has been widely used for simulating cardiac electrical activity. Our control objective is to dampen the excitation wavefront using optimal applied extracellular current. Two hybrid conjugate gradient methods are employed for computing the optimal applied extracellular current, namely, the Hestenes-Stiefel-Dai-Yuan (HS-DY method and the Liu-Storey-Conjugate-Descent (LS-CD method. Our experiment results show that the excitation wavefronts are successfully dampened out when these methods are used. Our experiment results also show that the hybrid conjugate gradient methods are superior to the classical conjugate gradient methods when Armijo line search is used.

  18. Optimizing refiner operation with statistical modelling

    Energy Technology Data Exchange (ETDEWEB)

    Broderick, G [Noranda Research Centre, Pointe Claire, PQ (Canada)

    1997-02-01

    The impact of refining conditions on the energy efficiency of the process and on the handsheet quality of a chemi-mechanical pulp was studied as part of a series of pilot scale refining trials. Statistical models of refiner performance were constructed from these results and non-linear optimization of process conditions were conducted. Optimization results indicated that increasing the ratio of specific energy applied in the first stage led to a reduction of some 15 per cent in the total energy requirement. The strategy can also be used to obtain significant increases in pulp quality for a given energy input. 20 refs., 6 tabs.

  19. An Optimization Model and Modified Harmony Search Algorithm for Microgrid Planning with ESS

    Directory of Open Access Journals (Sweden)

    Yang Jiao

    2017-01-01

    Full Text Available To solve problems such as the high cost of microgrids (MGs, balance between supply and demand, stability of system operation, and optimizing the MG planning model, the energy storage system (ESS and harmony search algorithm (HSA are proposed. First, the conventional MG planning optimization model is constructed and the constraint conditions are defined: the supply and demand balance and reserve requirements. Second, an ESS is integrated into the optimal model of MG planning. The model with an ESS can solve and identify parameters such as the optimal power, optimal capacity, and optimal installation year. Third, the convergence speed and robustness of the ESS are optimized and improved. A case study comprising three different cases concludes the paper. The results show that the modified HSA (MHSA can effectively improve the stability and economy of MG operation with an ESS.

  20. Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm

    International Nuclear Information System (INIS)

    Allam, Dalia; Yousri, D.A.; Eteiba, M.B.

    2016-01-01

    Highlights: • More detailed models are proposed to emulate the multi-crystalline solar cell/module. • Moth-Flame Optimizer (MFO) is proposed for the parameter extraction process. • The performance of MFO technique is compared with the recent optimization algorithms. • MFO algorithm converges to the optimal solution more rapidly and more accurately. • MFO algorithm accomplished with three diode model achieves the most accurate model. - Abstract: As a result of the wide prevalence of using the multi-crystalline silicon solar cells, an accurate mathematical model for these cells has become an important issue. Therefore, a three diode model is proposed as a more precise model to meet the relatively complicated physical behavior of the multi-crystalline silicon solar cells. The performance of this model is compared to the performance of both the double diode and the modified double diode models of the same cell/module. Therefore, there is a persistent need to keep searching for a more accurate optimization algorithm to estimate the more complicated models’ parameters. Hence, a proper optimization algorithm which is called Moth-Flame Optimizer (MFO), is proposed as a new optimization algorithm for the parameter extraction process of the three tested models based on data measured at laboratory and other data reported at previous literature. To verify the performance of the suggested technique, its results are compared with the results of the most recent and powerful techniques in the literature such as Hybrid Evolutionary (DEIM) and Flower Pollination (FPA) algorithms. Furthermore, evaluation analysis is performed for the three algorithms of the selected models at different environmental conditions. The results show that, MFO algorithm achieves the least Root Mean Square Error (RMSE), Mean Bias Error (MBE), Absolute Error at the Maximum Power Point (AEMPP) and best Coefficient of Determination. In addition, MFO is reaching to the optimal solution with the

  1. Optimization of Operations Resources via Discrete Event Simulation Modeling

    Science.gov (United States)

    Joshi, B.; Morris, D.; White, N.; Unal, R.

    1996-01-01

    The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.

  2. Low Complexity Models to improve Incomplete Sensitivities for Shape Optimization

    Science.gov (United States)

    Stanciu, Mugurel; Mohammadi, Bijan; Moreau, Stéphane

    2003-01-01

    The present global platform for simulation and design of multi-model configurations treat shape optimization problems in aerodynamics. Flow solvers are coupled with optimization algorithms based on CAD-free and CAD-connected frameworks. Newton methods together with incomplete expressions of gradients are used. Such incomplete sensitivities are improved using reduced models based on physical assumptions. The validity and the application of this approach in real-life problems are presented. The numerical examples concern shape optimization for an airfoil, a business jet and a car engine cooling axial fan.

  3. Optimization of photometric determination of U with arsenazo III for direct determination of U in steels, soils and waters

    International Nuclear Information System (INIS)

    Kosturiak, A.; Talanova, A.; Rurikova, D.; Kalavska, D.

    1984-01-01

    Conditions were optimized for the reaction of U(VI) with arsenazo III. Recommended as the optimal medium for photometric determination of uranium in the concentration range 0.5 to 50 μg U/ml was the glycine buffer with pH 1.2 to 2.2. The results of the suggested method have better reproducibility than those of the mineral acid procedure used so far. Complexone III should be added to mask the other cations accompanying uranium in steels, waters and rocks. (author)

  4. Nuclear-fuel-cycle optimization: methods and modelling techniques

    International Nuclear Information System (INIS)

    Silvennoinen, P.

    1982-01-01

    This book present methods applicable to analyzing fuel-cycle logistics and optimization as well as in evaluating the economics of different reactor strategies. After an introduction to the phases of a fuel cycle, uranium cost trends are assessed in a global perspective. Subsequent chapters deal with the fuel-cycle problems faced by a power utility. The fuel-cycle models cover the entire cycle from the supply of uranium to the disposition of spent fuel. The chapter headings are: Nuclear Fuel Cycle, Uranium Supply and Demand, Basic Model of the LWR (light water reactor) Fuel Cycle, Resolution of Uncertainties, Assessment of Proliferation Risks, Multigoal Optimization, Generalized Fuel-Cycle Models, Reactor Strategy Calculations, and Interface with Energy Strategies. 47 references, 34 figures, 25 tables

  5. Population Pharmacokinetics and Optimal Sampling Strategy for Model-Based Precision Dosing of Melphalan in Patients Undergoing Hematopoietic Stem Cell Transplantation.

    Science.gov (United States)

    Mizuno, Kana; Dong, Min; Fukuda, Tsuyoshi; Chandra, Sharat; Mehta, Parinda A; McConnell, Scott; Anaissie, Elias J; Vinks, Alexander A

    2018-05-01

    High-dose melphalan is an important component of conditioning regimens for patients undergoing hematopoietic stem cell transplantation. The current dosing strategy based on body surface area results in a high incidence of oral mucositis and gastrointestinal and liver toxicity. Pharmacokinetically guided dosing will individualize exposure and help minimize overexposure-related toxicity. The purpose of this study was to develop a population pharmacokinetic model and optimal sampling strategy. A population pharmacokinetic model was developed with NONMEM using 98 observations collected from 15 adult patients given the standard dose of 140 or 200 mg/m 2 by intravenous infusion. The determinant-optimal sampling strategy was explored with PopED software. Individual area under the curve estimates were generated by Bayesian estimation using full and the proposed sparse sampling data. The predictive performance of the optimal sampling strategy was evaluated based on bias and precision estimates. The feasibility of the optimal sampling strategy was tested using pharmacokinetic data from five pediatric patients. A two-compartment model best described the data. The final model included body weight and creatinine clearance as predictors of clearance. The determinant-optimal sampling strategies (and windows) were identified at 0.08 (0.08-0.19), 0.61 (0.33-0.90), 2.0 (1.3-2.7), and 4.0 (3.6-4.0) h post-infusion. An excellent correlation was observed between area under the curve estimates obtained with the full and the proposed four-sample strategy (R 2  = 0.98; p strategy promises to achieve the target area under the curve as part of precision dosing.

  6. Decision Support Model for Optimal Management of Coastal Gate

    Science.gov (United States)

    Ditthakit, Pakorn; Chittaladakorn, Suwatana

    2010-05-01

    The coastal areas are intensely settled by human beings owing to their fertility of natural resources. However, at present those areas are facing with water scarcity problems: inadequate water and poor water quality as a result of saltwater intrusion and inappropriate land-use management. To solve these problems, several measures have been exploited. The coastal gate construction is a structural measure widely performed in several countries. This manner requires the plan for suitably operating coastal gates. Coastal gate operation is a complicated task and usually concerns with the management of multiple purposes, which are generally conflicted one another. This paper delineates the methodology and used theories for developing decision support modeling for coastal gate operation scheduling. The developed model was based on coupling simulation and optimization model. The weighting optimization technique based on Differential Evolution (DE) was selected herein for solving multiple objective problems. The hydrodynamic and water quality models were repeatedly invoked during searching the optimal gate operations. In addition, two forecasting models:- Auto Regressive model (AR model) and Harmonic Analysis model (HA model) were applied for forecasting water levels and tide levels, respectively. To demonstrate the applicability of the developed model, it was applied to plan the operations for hypothetical system of Pak Phanang coastal gate system, located in Nakhon Si Thammarat province, southern part of Thailand. It was found that the proposed model could satisfyingly assist decision-makers for operating coastal gates under various environmental, ecological and hydraulic conditions.

  7. Optimizing Classroom Acoustics Using Computer Model Studies.

    Science.gov (United States)

    Reich, Rebecca; Bradley, John

    1998-01-01

    Investigates conditions relating to the maximum useful-to-detrimental sound ratios present in classrooms and determining the optimum conditions for speech intelligibility. Reveals that speech intelligibility is more strongly influenced by ambient noise levels and that the optimal location for sound absorbing material is on a classroom's upper…

  8. Optimization of A(2)O BNR processes using ASM and EAWAG Bio-P models: model performance.

    Science.gov (United States)

    El Shorbagy, Walid E; Radif, Nawras N; Droste, Ronald L

    2013-12-01

    This paper presents the performance of an optimization model for a biological nutrient removal (BNR) system using the anaerobic-anoxic-oxic (A(2)O) process. The formulated model simulates removal of organics, nitrogen, and phosphorus using a reduced International Water Association (IWA) Activated Sludge Model #3 (ASM3) model and a Swiss Federal Institute for Environmental Science and Technology (EAWAG) Bio-P module. Optimal sizing is attained considering capital and operational costs. Process performance is evaluated against the effect of influent conditions, effluent limits, and selected parameters of various optimal solutions with the following results: an increase of influent temperature from 10 degrees C to 25 degrees C decreases the annual cost by about 8.5%, an increase of influent flow from 500 to 2500 m(3)/h triples the annual cost, the A(2)O BNR system is more sensitive to variations in influent ammonia than phosphorus concentration and the maximum growth rate of autotrophic biomass was the most sensitive kinetic parameter in the optimization model.

  9. Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.

    Science.gov (United States)

    Patri, Jean-François; Diard, Julien; Perrier, Pascal

    2015-12-01

    The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.

  10. Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation

    International Nuclear Information System (INIS)

    Jiang, He; Dong, Yao; Wang, Jianzhou; Li, Yuqin

    2015-01-01

    Highlights: • CS-hard-ridge-RBF and DE-hard-ridge-RBF are proposed to forecast solar radiation. • Pearson and Apriori algorithm are used to analyze correlations between the data. • Hard-ridge penalty is added to reduce the number of nodes in the hidden layer. • CS algorithm and DE algorithm are used to determine the optimal parameters. • Proposed two models have higher forecasting accuracy than RBF and hard-ridge-RBF. - Abstract: Due to the scarcity of equipment and the high costs of maintenance, far fewer observations of solar radiation are made than observations of temperature, precipitation and other weather factors. Therefore, it is increasingly important to study several relevant meteorological factors to accurately forecast solar radiation. For this research, monthly average global solar radiation and 12 meteorological parameters from 1998 to 2010 at four sites in the United States were collected. Pearson correlation coefficients and Apriori association rules were successfully used to analyze correlations between the data, which provided a basis for these relative parameters as input variables. Two effective and innovative methods were developed to forecast monthly average global solar radiation by converting a RBF neural network into a multiple linear regression problem, adding a hard-ridge penalty to reduce the number of nodes in the hidden layer, and applying intelligent optimization algorithms, such as the cuckoo search algorithm (CS) and differential evolution (DE), to determine the optimal center and scale parameters. The experimental results show that the proposed models produce much more accurate forecasts than other models

  11. Simulation and optimization of a coking wastewater biological treatment process by activated sludge models (ASM).

    Science.gov (United States)

    Wu, Xiaohui; Yang, Yang; Wu, Gaoming; Mao, Juan; Zhou, Tao

    2016-01-01

    Applications of activated sludge models (ASM) in simulating industrial biological wastewater treatment plants (WWTPs) are still difficult due to refractory and complex components in influents as well as diversity in activated sludges. In this study, an ASM3 modeling study was conducted to simulate and optimize a practical coking wastewater treatment plant (CWTP). First, respirometric characterizations of the coking wastewater and CWTP biomasses were conducted to determine the specific kinetic and stoichiometric model parameters for the consecutive aeration-anoxic-aeration (O-A/O) biological process. All ASM3 parameters have been further estimated and calibrated, through cross validation by the model dynamic simulation procedure. Consequently, an ASM3 model was successfully established to accurately simulate the CWTP performances in removing COD and NH4-N. An optimized CWTP operation condition could be proposed reducing the operation cost from 6.2 to 5.5 €/m(3) wastewater. This study is expected to provide a useful reference for mathematic simulations of practical industrial WWTPs. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Optimal inflation for the U.S.

    OpenAIRE

    Roberto M. Billi

    2007-01-01

    What is the correctly measured inflation rate that monetary policy should aim for in the long-run? This paper characterizes the optimal inflation rate for the U.S. economy in a New Keynesian sticky-price model with an occasionally binding zero lower bound on the nominal interest rate. Real-rate and mark-up shocks jointly determine the optimal inflation rate to be positive but not large. Even allowing for the possibility of extreme model misspecification, the optimal inflation rate is robustly...

  13. Global Optimization Ensemble Model for Classification Methods

    Science.gov (United States)

    Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

    2014-01-01

    Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

  14. Global Optimization Ensemble Model for Classification Methods

    Directory of Open Access Journals (Sweden)

    Hina Anwar

    2014-01-01

    Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

  15. A Particle Swarm Optimization Algorithm for Optimal Operating Parameters of VMI Systems in a Two-Echelon Supply Chain

    Science.gov (United States)

    Sue-Ann, Goh; Ponnambalam, S. G.

    This paper focuses on the operational issues of a Two-echelon Single-Vendor-Multiple-Buyers Supply chain (TSVMBSC) under vendor managed inventory (VMI) mode of operation. To determine the optimal sales quantity for each buyer in TSVMBC, a mathematical model is formulated. Based on the optimal sales quantity can be obtained and the optimal sales price that will determine the optimal channel profit and contract price between the vendor and buyer. All this parameters depends upon the understanding of the revenue sharing between the vendor and buyers. A Particle Swarm Optimization (PSO) is proposed for this problem. Solutions obtained from PSO is compared with the best known results reported in literature.

  16. Degradation modeling and operational optimization for improving the lifetime of high-temperature PEM (proton exchange membrane) fuel cells

    International Nuclear Information System (INIS)

    Kim, Jintae; Kim, Minjin; Kang, Taegon; Sohn, Young-Jun; Song, Taewon; Choi, Kyoung Hwan

    2014-01-01

    High-temperature PEMFCs (proton exchange membrane fuel cells) using PA (phosphoric acid)-doped PBI (polybenzimidazole) membranes have received attention as a potential solution to several of the issues with traditional low-temperature PEMFCs. However, the durability of high-temperature PEMFCs deteriorates rapidly with increasing temperature, although its performance improves. This characteristic makes it difficult to select the proper operating temperature to achieve its target lifetime. In this paper, to resolve this problem, models were developed to predict the performance and durability of the high-temperature PEMFC as a function of operating temperature. The optimal operating temperature was then determined for a variety of lifetimes. Theoretical model to estimate cell performance and empirical model to predict the degradation rate of cell performance were constructed, respectively. The prediction results of the developed models agreed well with the experimental data. From the simulation, we could obtain higher average cell performances by optimizing the operating temperature for the given target lifetime compared to the cell performance at some temperatures determined using an existing rule of thumb. It is expected that the proposed methodologies will lead to the more rapid commercialization of this technology in such applications as stationary and automotive fuel cell systems. - Highlights: • High-temperature PEMFCs (proton exchange membrane fuel cells). • Operational optimization for improving the lifetime. • Development of the degradation modeling for high-temperature PEMFCs

  17. Sootblowing optimization for improved boiler performance

    Science.gov (United States)

    James, John Robert; McDermott, John; Piche, Stephen; Pickard, Fred; Parikh, Neel J.

    2012-12-25

    A sootblowing control system that uses predictive models to bridge the gap between sootblower operation and boiler performance goals. The system uses predictive modeling and heuristics (rules) associated with different zones in a boiler to determine an optimal sequence of sootblower operations and achieve boiler performance targets. The system performs the sootblower optimization while observing any operational constraints placed on the sootblowers.

  18. Innovative model-based flow rate optimization for vanadium redox flow batteries

    Science.gov (United States)

    König, S.; Suriyah, M. R.; Leibfried, T.

    2016-11-01

    In this paper, an innovative approach is presented to optimize the flow rate of a 6-kW vanadium redox flow battery with realistic stack dimensions. Efficiency is derived using a multi-physics battery model and a newly proposed instantaneous efficiency determination technique. An optimization algorithm is applied to identify optimal flow rates for operation points defined by state-of-charge (SoC) and current. The proposed method is evaluated against the conventional approach of applying Faraday's first law of electrolysis, scaled to the so-called flow factor. To make a fair comparison, the flow factor is also optimized by simulating cycles with different charging/discharging currents. It is shown through the obtained results that the efficiency is increased by up to 1.2% points; in addition, discharge capacity is also increased by up to 1.0 kWh or 5.4%. Detailed loss analysis is carried out for the cycles with maximum and minimum charging/discharging currents. It is shown that the proposed method minimizes the sum of losses caused by concentration over-potential, pumping and diffusion. Furthermore, for the deployed Nafion 115 membrane, it is observed that diffusion losses increase with stack SoC. Therefore, to decrease stack SoC and lower diffusion losses, a higher flow rate during charging than during discharging is reasonable.

  19. Correlations in state space can cause sub-optimal adaptation of optimal feedback control models

    OpenAIRE

    Aprasoff, Jonathan; Donchin, Opher

    2011-01-01

    Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedb...

  20. A methodology for determining optimal durations for the use of contaminated crops as fodder following a nuclear accident using a dynamic food-chain model

    International Nuclear Information System (INIS)

    Hwang, Won Tae; Han, Moon Hee; Cho, Gyuseong

    2000-01-01

    A methodology for determining optimal durations for the use of contaminated crops as fodder was designed based on cost-benefit analysis. Illustrative results of the application of this methodology to pigs are presented for the hypothetical deposition of radionuclides on August 15 when a number of crops are fully developed in Korean agricultural conditions. For investigating the appropriateness of the use of contaminated crops as fodder, the net benefit from this action was compared with the imposition of a ban on human consumption of contaminated crops without alternative use. The time-dependent radionuclide concentrations in crops and pork after the deposition event were predicted from a dynamic food-chain model DYNACON. The net benefit from the actions was quantitatively evaluated in terms of cost equivalent of the doses incurred or averted and the monetary costs needed to implement the action. The optimal duration for the use of contaminated crops as fodder depended on a number of factors such as radionuclide, variety of crops fed as fodder and duration of the action. Such action was more cost effective for 137 Cs deposition than for 90 Sr or 131 I deposition. The use of contaminated crops as fodder can be an effective response to a public reluctance to consume contaminated crops

  1. Optimization Model for Headway of a Suburban Bus Route

    Directory of Open Access Journals (Sweden)

    Xiaohong Jiang

    2014-01-01

    Full Text Available Due to relatively low passenger demand, headways of suburban bus route are usually longer than those of urban bus route. Actually it is also difficult to balance the benefits between passengers and operators, subject to the service standards from the government. Hence the headway of a suburban bus route is usually determined on the empirical experience of transport planners. To cope with this problem, this paper proposes an optimization model for designing the headways of suburban bus routes by minimizing the operating and user costs. The user costs take into account both the waiting time cost and the crowding cost. The feasibility and validity of the proposed model are shown by applying it to the Route 206 in Jiangning district, Nanjing city of China. Weightages of passengers’ cost and operating cost are further discussed, considering different passenger flows. It is found that the headway and objective function are affected by the weightages largely.

  2. Using real options to determine optimal funding strategies for CO2 capture, transport and storage projects in the European Union

    International Nuclear Information System (INIS)

    Eckhause, Jeremy; Herold, Johannes

    2014-01-01

    Several projects in the European Union (EU) are currently under development to implement the carbon capture, transport and storage (CCS) technology on a large scale and may be subject to public funding under EU support initiatives. These CCS projects may develop any combination of three types of operating levels: pilot, demonstration and full-scale, representing progressing levels of electric power generation capability. Several projects have commenced at the demonstration level, with full-scale commercial levels planned for approximately 2020. Taking the perspective of a funding agency, we employ a real options framework for determining an optimal project selection and funding strategy for the development of full-scale CCS plants. Specifically, we formulate and solve a stochastic dynamic program (SDP) for obtaining optimal funding solutions in order to achieve at least one successfully operating full-scale CCS plant by a target year. The model demonstrates the improved risk reduction by employing such a multi-stage competition. We then extend the model to consider two sensitivities: (1) the flexibility to spend that budget among the time periods and (2) optimizing the budget, but specifying each time period's allocation a priori. State size and runtimes of the SDP model are provided. - Highlights: • Projects implementing three different CCS technology types are described. • We obtain projects’ transition probabilities and costs from expert interviews. • We use a multi-stage real options model to obtain optimal funding strategies. • Using this approach, actual decision-makers could reduce risks in CCS development

  3. Optimal modeling and forecasting of the energy consumption and production in China

    International Nuclear Information System (INIS)

    Xiong, Ping-ping; Dang, Yao-guo; Yao, Tian-xiang; Wang, Zheng-xin

    2014-01-01

    Energy is of fundamental importance to a nation's economy. Accurate prediction of the energy consumption and production in China can play a guiding role in making the energy consumption plan, and facilitate timely and effective decision making of energy policy. This article proposes a novel GM (gray model) (1,1) model based on optimizing initial condition according to the principle of new information priority. The optimized model and five other GM (1,1) models are applied in the modeling of China's energy consumption and production. Both the simulation and prediction accuracy of the models are compared and analyzed. We obtain the result that the optimized model has higher prediction accuracy than the other five models. Therefore, the presented optimized model is further utilized to predict China's energy consumption and production from 2013 to 2017. The result indicates that China's energy consumption and production will keep increasing and the gap between the energy production and consumption will also be increasing. Finally, we predict Iran's and Argentina's energy consumption to further prove the effectiveness of the proposed model. - Highlights: • We proposed a novel GM (1,1) model based on optimizing initial condition. • The prediction accuracy of the proposed model is better than the other models. • We used the proposed model to predict China's energy consumption and production. • The proposed model can be used to predict other countries' energy consumption

  4. Optimization models and techniques for implementation and pricing of electricity markets

    International Nuclear Information System (INIS)

    Madrigal Martinez, M.

    2001-01-01

    The operation and planning of vertically integrated electric power systems can be optimized using models that simulate solutions to problems. As the electric power industry is going through a period of restructuring, there is a need for new optimization tools. This thesis describes the importance of optimization tools and presents techniques for implementing them. It also presents methods for pricing primary electricity markets. Three modeling groups are studied. The first considers a simplified continuous and discrete model for power pool auctions. The second considers the unit commitment problem, and the third makes use of a new type of linear network-constrained clearing system model for daily markets for power and spinning reserve. The newly proposed model considers bids for supply and demand and bilateral contracts. It is a direct current model for the transmission network

  5. Multi-objective optimization for generating a weighted multi-model ensemble

    Science.gov (United States)

    Lee, H.

    2017-12-01

    Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

  6. An extended optimal replacement model for a deteriorating system with inspections

    International Nuclear Information System (INIS)

    Sheu, Shey-Huei; Tsai, Hsin-Nan; Wang, Fu-Kwun; Zhang, Zhe George

    2015-01-01

    This study considers a generalized replacement model for a deteriorating system in which failures can only be detected by inspection. The system is assumed to have two types of failures and is replaced at the occurrence of the Nth type I failure (minor failure), or the first type II failure (catastrophic failure), or at working age T, whichever occurs first. The probability of a type I or type II failure depends on the number of type I failures since the previous replacement. Such a system can be repaired after a type I failure, but is deteriorating stochastically. That is, the operating intervals are decreasing stochastically, whereas the durations of the repairs are increasing stochastically. Based on these assumptions, we determine the expected net cost rate and discuss various special cases of the model. Finally, we develop a computational algorithm for finding the optimal policy and present a numerical example to show the properties of the proposed model. - Highlight: • Replacement policy for system subject two types of failures or the system's working age. • Failures detected by inspections. • Decreasing operating times and increasing repair times. • Derive a cost function. • Determine the cost minimization policy

  7. A trust region approach with multivariate Padé model for optimal circuit design

    Science.gov (United States)

    Abdel-Malek, Hany L.; Ebid, Shaimaa E. K.; Mohamed, Ahmed S. A.

    2017-11-01

    Since the optimization process requires a significant number of consecutive function evaluations, it is recommended to replace the function by an easily evaluated approximation model during the optimization process. The model suggested in this article is based on a multivariate Padé approximation. This model is constructed using data points of ?, where ? is the number of parameters. The model is updated over a sequence of trust regions. This model avoids the slow convergence of linear models of ? and has features of quadratic models that need interpolation data points of ?. The proposed approach is tested by applying it to several benchmark problems. Yield optimization using such a direct method is applied to some practical circuit examples. Minimax solution leads to a suitable initial point to carry out the yield optimization process. The yield is optimized by the proposed derivative-free method for active and passive filter examples.

  8. Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

    Directory of Open Access Journals (Sweden)

    Sie Long Kek

    2015-01-01

    Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.

  9. Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

    Science.gov (United States)

    Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

    2017-01-01

    Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

  10. Optimization of single photon detection model based on GM-APD

    Science.gov (United States)

    Chen, Yu; Yang, Yi; Hao, Peiyu

    2017-11-01

    One hundred kilometers high precision laser ranging hopes the detector has very strong detection ability for very weak light. At present, Geiger-Mode of Avalanche Photodiode has more use. It has high sensitivity and high photoelectric conversion efficiency. Selecting and designing the detector parameters according to the system index is of great importance to the improvement of photon detection efficiency. Design optimization requires a good model. In this paper, we research the existing Poisson distribution model, and consider the important detector parameters of dark count rate, dead time, quantum efficiency and so on. We improve the optimization of detection model, select the appropriate parameters to achieve optimal photon detection efficiency. The simulation is carried out by using Matlab and compared with the actual test results. The rationality of the model is verified. It has certain reference value in engineering applications.

  11. Heat transfer optimization of SCO2 porous flow based on Brinkman model

    Directory of Open Access Journals (Sweden)

    Lin David T.W.

    2016-01-01

    Full Text Available The purpose of this study is to obtain the optimal operating condition in order to find the maximum supercritical CO2 heat extraction in the enhanced geothermal system (EGS. In this study, the heat transfer model conjugated with the Brinkman model is used to evaluate the thermal behavior in the reservoir of the EGS. This numerical model is validated by experiment. Optimization is processed based on the Nelder-Mead approach. The optimal operating conditions are proposed with different pressure, porosity. This study will build the optimal platform of heat source of geothermal power plant.

  12. Optimization and mathematical modeling in computer architecture

    CERN Document Server

    Sankaralingam, Karu; Nowatzki, Tony

    2013-01-01

    In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms t

  13. Modeling, Analysis, and Optimization Issues for Large Space Structures

    Science.gov (United States)

    Pinson, L. D. (Compiler); Amos, A. K. (Compiler); Venkayya, V. B. (Compiler)

    1983-01-01

    Topics concerning the modeling, analysis, and optimization of large space structures are discussed including structure-control interaction, structural and structural dynamics modeling, thermal analysis, testing, and design.

  14. Sparse optimization for inverse problems in atmospheric modelling

    Czech Academy of Sciences Publication Activity Database

    Adam, Lukáš; Branda, Martin

    2016-01-01

    Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf

  15. Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

    Science.gov (United States)

    Jin, Junchen

    2016-01-01

    The shunting schedule of electric multiple units depot (SSED) is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO) algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality. PMID:27436998

  16. Optimizing the Shunting Schedule of Electric Multiple Units Depot Using an Enhanced Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jiaxi Wang

    2016-01-01

    Full Text Available The shunting schedule of electric multiple units depot (SSED is one of the essential plans for high-speed train maintenance activities. This paper presents a 0-1 programming model to address the problem of determining an optimal SSED through automatic computing. The objective of the model is to minimize the number of shunting movements and the constraints include track occupation conflicts, shunting routes conflicts, time durations of maintenance processes, and shunting running time. An enhanced particle swarm optimization (EPSO algorithm is proposed to solve the optimization problem. Finally, an empirical study from Shanghai South EMU Depot is carried out to illustrate the model and EPSO algorithm. The optimization results indicate that the proposed method is valid for the SSED problem and that the EPSO algorithm outperforms the traditional PSO algorithm on the aspect of optimality.

  17. Deterministic operations research models and methods in linear optimization

    CERN Document Server

    Rader, David J

    2013-01-01

    Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear

  18. Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

    Science.gov (United States)

    Chande, Ruchi D; Wayne, Jennifer S

    2017-09-01

    Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

  19. Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Meiping Wang

    2016-01-01

    Full Text Available We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR model-optimized particle swarm optimization (PSO algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR, and artificial neural network (ANN through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.

  20. Solving bi-level optimization problems in engineering design using kriging models

    Science.gov (United States)

    Xia, Yi; Liu, Xiaojie; Du, Gang

    2018-05-01

    Stackelberg game-theoretic approaches are applied extensively in engineering design to handle distributed collaboration decisions. Bi-level genetic algorithms (BLGAs) and response surfaces have been used to solve the corresponding bi-level programming models. However, the computational costs for BLGAs often increase rapidly with the complexity of lower-level programs, and optimal solution functions sometimes cannot be approximated by response surfaces. This article proposes a new method, namely the optimal solution function approximation by kriging model (OSFAKM), in which kriging models are used to approximate the optimal solution functions. A detailed example demonstrates that OSFAKM can obtain better solutions than BLGAs and response surface-based methods, and at the same time reduce the workload of computation remarkably. Five benchmark problems and a case study of the optimal design of a thin-walled pressure vessel are also presented to illustrate the feasibility and potential of the proposed method for bi-level optimization in engineering design.

  1. Building of Reusable Reverse Logistics Model and its Optimization Considering the Decision of Backorder or Next Arrival of Goods

    Science.gov (United States)

    Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu; Lee, Hee-Hyol

    This paper deals with the building of the reusable reverse logistics model considering the decision of the backorder or the next arrival of goods. The optimization method to minimize the transportation cost and to minimize the volume of the backorder or the next arrival of goods occurred by the Just in Time delivery of the final delivery stage between the manufacturer and the processing center is proposed. Through the optimization algorithms using the priority-based genetic algorithm and the hybrid genetic algorithm, the sub-optimal delivery routes are determined. Based on the case study of a distilling and sale company in Busan in Korea, the new model of the reusable reverse logistics of empty bottles is built and the effectiveness of the proposed method is verified.

  2. An atomic model of brome mosaic virus using direct electron detection and real-space optimization

    Science.gov (United States)

    Wang, Zhao; Hryc, Corey F.; Bammes, Benjamin; Afonine, Pavel V.; Jakana, Joanita; Chen, Dong-Hua; Liu, Xiangan; Baker, Matthew L.; Kao, Cheng; Ludtke, Steven J.; Schmid, Michael F.; Adams, Paul D.; Chiu, Wah

    2014-09-01

    Advances in electron cryo-microscopy have enabled structure determination of macromolecules at near-atomic resolution. However, structure determination, even using de novo methods, remains susceptible to model bias and overfitting. Here we describe a complete workflow for data acquisition, image processing, all-atom modelling and validation of brome mosaic virus, an RNA virus. Data were collected with a direct electron detector in integrating mode and an exposure beyond the traditional radiation damage limit. The final density map has a resolution of 3.8 Å as assessed by two independent data sets and maps. We used the map to derive an all-atom model with a newly implemented real-space optimization protocol. The validity of the model was verified by its match with the density map and a previous model from X-ray crystallography, as well as the internal consistency of models from independent maps. This study demonstrates a practical approach to obtain a rigorously validated atomic resolution electron cryo-microscopy structure.

  3. In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data

    Science.gov (United States)

    Yi, Yeon-Sook

    2017-01-01

    This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model…

  4. Optimized determination of the radiological inventory during different phases of decommissioning

    International Nuclear Information System (INIS)

    Hillberg, Matthias; Beltz, Detlef; Karschnick, Oliver

    2012-01-01

    The decommissioning of nuclear facilities comprises a lot of activities such as decontamination, dismantling and demolition of equipment and structures. For these activities the aspects of health and safety of the operational personnel and of the general public as well as the minimization of radioactive waste have to be taken into account. An optimized, comprehensible and verifiable determination of the radiological inventory is essential for the decommissioning management with respect to safety, time, and costs. For example: right from the start of the post operational phase, the radiological characterization has to enable the decision whether to perform a system decontamination or not. Furthermore it is necessary, e.g. to determine the relevant nuclides and their composition (nuclide vector) for the release of material and for sustaining the radiological health and safety at work (e. g. minimizing the risk of incorporation). Our contribution will focus on the optimization of the radiological characterization with respect to the requisite extent and the best instant of time during the decommissioning process. For example: which additional information, besides the history of operation, is essential for an adequate amount of sampling and measurements needed in order to determine the relevant nuclides and their compositions? Furthermore, the characterization of buildings requires a kind of a graded approach during the decommissioning process. At the beginning of decommissioning, only a rough estimate of the expected radioactive waste due to the necessary decontamination of the building structures is sufficient. With ongoing decommissioning, a more precise radiological characterization of buildings is needed in order to guarantee an optimized, comprehensible and verifiable decontamination, dismantling and trouble-free clearance. These and other examples will be discussed on the background of and with reference to different decommissioning projects involving direct

  5. Applied probability models with optimization applications

    CERN Document Server

    Ross, Sheldon M

    1992-01-01

    Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.

  6. Hedging Rules for Water Supply Reservoir Based on the Model of Simulation and Optimization

    Directory of Open Access Journals (Sweden)

    Yi Ji

    2016-06-01

    Full Text Available This study proposes a hedging rule model which is composed of a two-period reservior operation model considering the damage depth and hedging rule parameter optimization model. The former solves hedging rules based on a given poriod’s water supply weighting factor and carryover storage target, while the latter optimization model is used to optimize the weighting factor and carryover storage target based on the hedging rules. The coupling model gives the optimal poriod’s water supply weighting factor and carryover storage target to guide release. The conclusions achieved from this study as follows: (1 the water supply weighting factor and carryover storage target have a direct impact on the three elements of the hedging rule; (2 parameters can guide reservoirs to supply water reasonably after optimization of the simulation and optimization model; and (3 in order to verify the utility of the hedging rule, the Heiquan reservoir is used as a case study and particle swarm optimization algorithm with a simulation model is adopted for optimizing the parameter. The results show that the proposed hedging rule can improve the operation performances of the water supply reservoir.

  7. Optimal pricing and lot-sizing policies for an economic production quantity model with non-instantaneous deteriorating items, permissible delay in payments, customer returns, and inflation

    DEFF Research Database (Denmark)

    Ghoreishi, Maryam; Mirzazadeh, Abolfazl; Nakhai Kamalabadi, Isa

    2014-01-01

    . The effects of time value of money are studied using the Discounted Cash Flow approach. The main objective is to determine the optimal selling price, the optimal length of the production period, and the optimal length of inventory cycle simultaneously such that the present value of total profit is maximized....... An efficient algorithm is presented to find the optimal solution of the developed model. Finally, a numerical example is extracted to solve the presented inventory model using our proposed algorithm, and the effects of the customer returns, inflation, and delay in payments are also discussed....

  8. Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network-Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Li

    2017-01-01

    Full Text Available The goal of this study is to improve thermal comfort and indoor air quality with the adaptive network-based fuzzy inference system (ANFIS model and improved particle swarm optimization (PSO algorithm. A method to optimize air conditioning parameters and installation distance is proposed. The methodology is demonstrated through a prototype case, which corresponds to a typical laboratory in colleges and universities. A laboratory model is established, and simulated flow field information is obtained with the CFD software. Subsequently, the ANFIS model is employed instead of the CFD model to predict indoor flow parameters, and the CFD database is utilized to train ANN input-output “metamodels” for the subsequent optimization. With the improved PSO algorithm and the stratified sequence method, the objective functions are optimized. The functions comprise PMV, PPD, and mean age of air. The optimal installation distance is determined with the hemisphere model. Results show that most of the staff obtain a satisfactory degree of thermal comfort and that the proposed method can significantly reduce the cost of building an experimental device. The proposed methodology can be used to determine appropriate air supply parameters and air conditioner installation position for a pleasant and healthy indoor environment.

  9. Optimization of large-scale heterogeneous system-of-systems models.

    Energy Technology Data Exchange (ETDEWEB)

    Parekh, Ojas; Watson, Jean-Paul; Phillips, Cynthia Ann; Siirola, John; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Lee, Herbert K. H. (University of California, Santa Cruz, Santa Cruz, CA); Hart, William Eugene; Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Woodruff, David L. (University of California, Davis, Davis, CA)

    2012-01-01

    Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.

  10. Acid hydrolysis of corn stover using hydrochloric acid: Kinetic modeling and statistical optimization

    Directory of Open Access Journals (Sweden)

    Sun Yong

    2014-01-01

    Full Text Available The hydrolysis of corn stover using hydrochloric acid was studied. The kinetic parameters of the mathematical models for predicting the yields of xylose, glucose, furfural and acetic acid were obtained, and the corresponding xylose generation activation energy of 100 kJ/mol was determined. The characterization of corn stover using with different techniques during hydrolysis indicated an effective removal of xylan and the slightly alteration on the structures of cellulose and lignin. A 23five levels Central Composite Design (CCD was used to develop a statistical model for the optimization of process variables including acid concentration, pretreatment temperature and time. The optimum conditions determined by this model were found to be 108ºC for 80 minutes with acid concentration of 5.8%. Under these conditions, the maximised results are the following: xylose 19.93 g/L, glucose 1.2 g/L, furfural 1.5 g/L, acetic acid 1.3 g/L. The validation of the model indicates a good agreement between the experimental results and the predicted values.

  11. Runtime Optimizations for Tree-Based Machine Learning Models

    NARCIS (Netherlands)

    N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

    2014-01-01

    htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

  12. Spatial multiobjective optimization of agricultural conservation practices using a SWAT model and an evolutionary algorithm.

    Science.gov (United States)

    Rabotyagov, Sergey; Campbell, Todd; Valcu, Adriana; Gassman, Philip; Jha, Manoj; Schilling, Keith; Wolter, Calvin; Kling, Catherine

    2012-12-09

    Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,(5,12,20)) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods(3,4,9,10,13-15,17-19,22,23,25). In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model(7) with a

  13. Developments in model-based optimization and control distributed control and industrial applications

    CERN Document Server

    Grancharova, Alexandra; Pereira, Fernando

    2015-01-01

    This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...

  14. Optimal Strategy and Business Models

    DEFF Research Database (Denmark)

    Johnson, Peter; Foss, Nicolai Juul

    2016-01-01

    This study picks up on earlier suggestions that control theory may further the study of strategy. Strategy can be formally interpreted as an idealized path optimizing heterogeneous resource deployment to produce maximum financial gain. Using standard matrix methods to describe the firm Hamiltonia...... variable of firm path, suggesting in turn that the firm's business model is the codification of the application of investment resources used to control the strategic path of value realization....

  15. Study on network traffic forecast model of SVR optimized by GAFSA

    International Nuclear Information System (INIS)

    Liu, Yuan; Wang, RuiXue

    2016-01-01

    There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.

  16. MODELLING, SIMULATING AND OPTIMIZING BOILERS

    DEFF Research Database (Denmark)

    Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels

    2004-01-01

    In the present work a framework for optimizing the design of boilers for dynamic operation has been developed. A cost function to be minimized during the optimization has been formulated and for the present design variables related to the Boiler Volume and the Boiler load Gradient (i.e. ring rate...... on the boiler) have been dened. Furthermore a number of constraints related to: minimum and maximum boiler load gradient, minimum boiler size, Shrinking and Swelling and Steam Space Load have been dened. For dening the constraints related to the required boiler volume a dynamic model for simulating the boiler...... performance has been developed. Outputs from the simulations are shrinking and swelling of water level in the drum during for example a start-up of the boiler, these gures combined with the requirements with respect to allowable water level uctuations in the drum denes the requirements with respect to drum...

  17. Optimization of the host–guest system within an OLED using different models of mobility

    Energy Technology Data Exchange (ETDEWEB)

    Adames Prada, Rosana, E-mail: rdadamesp@unal.edu.co; Ardila Vargas, Angel Miguel, E-mail: amardilav@unal.edu.co

    2014-12-15

    To optimize the host–guest system of organic light emitting devices (OLEDs), it were performed simulations by doping a small molecule organic semiconductor (acting as host) with the transition organometallic complex known as Ir(ppy){sub 2}(acac) (acting as a guest). The simulation was carried out using simOLED with different mobility models: the Pool–Frenkel Model (PFM), the Extended Gaussian Disorder Model (EGDM) and Extended Correlated Disorder Model (ECDM), with the aim to determine the magnitude of some parameters like temperature, applied electric field, film thickness and charge carrier density that improve the electrical and optical properties of OLEDs with a more simplified structure like ITO/CBP/CBP:Ir(ppy){sub 2}(acac)/TPBi/LiF/Al.

  18. Optimized production planning model for a multi-plant cultivation system under uncertainty

    Science.gov (United States)

    Ke, Shunkui; Guo, Doudou; Niu, Qingliang; Huang, Danfeng

    2015-02-01

    An inexact multi-constraint programming model under uncertainty was developed by incorporating a production plan algorithm into the crop production optimization framework under the multi-plant collaborative cultivation system. In the production plan, orders from the customers are assigned to a suitable plant under the constraints of plant capabilities and uncertainty parameters to maximize profit and achieve customer satisfaction. The developed model and solution method were applied to a case study of a multi-plant collaborative cultivation system to verify its applicability. As determined in the case analysis involving different orders from customers, the period of plant production planning and the interval between orders can significantly affect system benefits. Through the analysis of uncertain parameters, reliable and practical decisions can be generated using the suggested model of a multi-plant collaborative cultivation system.

  19. Innovative supply chain optimization models with multiple uncertainty factors

    DEFF Research Database (Denmark)

    Choi, Tsan Ming; Govindan, Kannan; Li, Xiang

    2017-01-01

    Uncertainty is an inherent factor that affects all dimensions of supply chain activities. In today’s business environment, initiatives to deal with one specific type of uncertainty might not be effective since other types of uncertainty factors and disruptions may be present. These factors relate...... to supply chain competition and coordination. Thus, to achieve a more efficient and effective supply chain requires the deployment of innovative optimization models and novel methods. This preface provides a concise review of critical research issues regarding innovative supply chain optimization models...

  20. Energy-saving management modelling and optimization for lead-acid battery formation process

    Science.gov (United States)

    Wang, T.; Chen, Z.; Xu, J. Y.; Wang, F. Y.; Liu, H. M.

    2017-11-01

    In this context, a typical lead-acid battery producing process is introduced. Based on the formation process, an efficiency management method is proposed. An optimization model with the objective to minimize the formation electricity cost in a single period is established. This optimization model considers several related constraints, together with two influencing factors including the transformation efficiency of IGBT charge-and-discharge machine and the time-of-use price. An example simulation is shown using PSO algorithm to solve this mathematic model, and the proposed optimization strategy is proved to be effective and learnable for energy-saving and efficiency optimization in battery producing industries.

  1. Optimization of hybrid model on hajj travel

    Science.gov (United States)

    Cahyandari, R.; Ariany, R. L.; Sukono

    2018-03-01

    Hajj travel insurance is an insurance product offered by the insurance company in preparing funds to perform the pilgrimage. This insurance product helps would-be pilgrims to set aside a fund of saving hajj with regularly, but also provides funds of profit sharing (mudharabah) and insurance protection. Scheme of insurance product fund management is largely using the hybrid model, which is the fund from would-be pilgrims will be divided into three account management, that is personal account, tabarru’, and ujrah. Scheme of hybrid model on hajj travel insurance was already discussed at the earlier paper with titled “The Hybrid Model Algorithm on Sharia Insurance”, taking the example case of Mitra Mabrur Plus product from Bumiputera company. On these advanced paper, will be made the previous optimization model design, with partition of benefit the tabarru’ account. Benefits such as compensation for 40 critical illness which initially only for participants of insurance only, on optimization is intended for participants of the insurance and his heir, also to benefit the hospital bills. Meanwhile, the benefits of death benefit is given if the participant is fixed die.

  2. Multi-objective three stage design optimization for island microgrids

    International Nuclear Information System (INIS)

    Sachs, Julia; Sawodny, Oliver

    2016-01-01

    Highlights: • An enhanced multi-objective three stage design optimization for microgrids is given. • Use of an optimal control problem for the calculation of the optimal operation. • The inclusion of a detailed battery model with CC/CV charging control. • The determination of a representative profile with optimized number of days. • The proposed method finds its direct application in a design tool for microgids. - Abstract: Hybrid off-grid energy systems enable a cost efficient and reliable energy supply to rural areas around the world. The main potential for a low cost operation and uninterrupted power supply lies in the optimal sizing and operation of such microgrids. In particular, sudden variations in load demand or in the power supply from renewables underline the need for an optimally sized system. This paper presents an efficient multi-objective model based optimization approach for the optimal sizing of all components and the determination of the best power electronic layout. The presented method is divided into three optimization problems to minimize economic and environmental objectives. This design optimization includes detailed components models and an optimized energy dispatch strategy which enables the optimal design of the energy system with respect to an adequate control for the specific configuration. To significantly reduce the computation time without loss of accuracy, the presented method contains the determination of a representative load profile using a k-means clustering method. The k-means algorithm itself is embedded in an optimization problem for the calculation of the optimal number of clusters. The benefits in term of reduced computation time, inclusion of optimal energy dispatch and optimization of power electronic architecture, of the presented optimization method are illustrated using a case study.

  3. Reliability Of A Novel Intracardiac Electrogram Method For AV And VV Delay Optimization And Comparability To Echocardiography Procedure For Determining Optimal Conduction Delays In CRT Patients

    Directory of Open Access Journals (Sweden)

    N Reinsch

    2009-03-01

    Full Text Available Background: Echocardiography is widely used to optimize CRT programming. A novel intracardiac electrogram method (IEGM was recently developed as an automated programmer-based method, designed to calculate optimal atrioventricular (AV and interventricular (VV delays and provide optimized delay values as an alternative to standard echocardiographic assessment.Objective: This study was aimed at determining the reliability of this new method. Furthermore the comparability of IEGM to existing echocardiographic parameters for determining optimal conduction delays was verified.Methods: Eleven patients (age 62.9± 8.7; 81% male; 73% ischemic, previously implanted with a cardiac resynchronisation therapy defibrillator (CRT-D underwent both echocardiographic and IEGM-based delay optimization.Results: Applying the IEGM method, concordance of three consecutively performed measurements was found in 3 (27% patients for AV delay and in 5 (45% patients for VV delay. Intra-individual variation between three measurements as assessed by the IEGM technique was up to 20 ms (AV: n=6; VV: n=4. E-wave, diastolic filling time and septal-to-lateral wall motion delay emerged as significantly different between the echo and IEGM optimization techniques (p < 0.05. The final AV delay setting was significantly different between both methods (echo: 126.4 ± 29.4 ms, IEGM: 183.6 ± 16.3 ms; p < 0.001; correlation: R = 0.573, p = 0.066. VV delay showed significant differences for optimized delays (echo: 46.4 ± 23.8 ms, IEGM: 10.9 ± 7.0 ms; p <0.01; correlation: R = -0.278, p = 0.407.Conclusion: The automated programmer-based IEGM-based method provides a simple and safe method to perform CRT optimization. However, the reliability of this method appears to be limited. Thus, it remains difficult for the examiner to determine the optimal hemodynamic settings. Additionally, as there was no correlation between the optimal AV- and VV-delays calculated by the IEGM method and the echo

  4. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Science.gov (United States)

    Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

    2016-01-01

    Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  5. An improved model for the oPtImal Measurement Probes Allocation tool

    International Nuclear Information System (INIS)

    Sterle, C.; Neto, A.C.; De Tommasi, G.

    2015-01-01

    Highlights: • The problem of optimally allocating the probes of a diagnostic system is tackled. • The problem is decomposed in two consecutive optimization problems. • Two original ILP models are proposed and sequentially solved to optimality. • The proposed ILP models improve and extend the previous work present in literature. • Real size instances have been optimally solved with very low computation time. - Abstract: The oPtImal Measurement Probes Allocation (PIMPA) tool has been recently proposed in [1] to maximize the reliability of a tokamak diagnostic system against the failure of one or more of the processing nodes. PIMPA is based on the solution of integer linear programming (ILP) problems, and it minimizes the effect of the failure of a data acquisition component. The first formulation of the PIMPA model did not support the concept of individual slots. This work presents an improved ILP model that addresses the above mentioned problem, by taking into account all the individual probes.

  6. An improved model for the oPtImal Measurement Probes Allocation tool

    Energy Technology Data Exchange (ETDEWEB)

    Sterle, C., E-mail: claudio.sterle@unina.it [Consorzio CREATE/Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy); Neto, A.C. [Fusion for Energy, 08019 Barcelona (Spain); De Tommasi, G. [Consorzio CREATE/Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy)

    2015-10-15

    Highlights: • The problem of optimally allocating the probes of a diagnostic system is tackled. • The problem is decomposed in two consecutive optimization problems. • Two original ILP models are proposed and sequentially solved to optimality. • The proposed ILP models improve and extend the previous work present in literature. • Real size instances have been optimally solved with very low computation time. - Abstract: The oPtImal Measurement Probes Allocation (PIMPA) tool has been recently proposed in [1] to maximize the reliability of a tokamak diagnostic system against the failure of one or more of the processing nodes. PIMPA is based on the solution of integer linear programming (ILP) problems, and it minimizes the effect of the failure of a data acquisition component. The first formulation of the PIMPA model did not support the concept of individual slots. This work presents an improved ILP model that addresses the above mentioned problem, by taking into account all the individual probes.

  7. Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

    Directory of Open Access Journals (Sweden)

    Nadia Said

    Full Text Available Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

  8. Parametric modeling and stagger angle optimization of an axial flow fan

    International Nuclear Information System (INIS)

    Li, M X; Zhang, C H; Liu, Y; Zheng, S Y

    2013-01-01

    Axial flow fans are widely used in every field of social production. Improving their efficiency is a sustained and urgent demand of domestic industry. The optimization of stagger angle is an important method to improve fan performance. Parametric modeling and calculation process automation are realized in this paper to improve optimization efficiency. Geometric modeling and mesh division are parameterized based on GAMBIT. Parameter setting and flow field calculation are completed in the batch mode of FLUENT. A control program is developed in Visual C++ to dominate the data exchange of mentioned software. It also extracts calculation results for optimization algorithm module (provided by Matlab) to generate directive optimization control parameters, which as feedback are transferred upwards to modeling module. The center line of the blade airfoil, based on CLARK y profile, is constructed by non-constant circulation and triangle discharge method. Stagger angles of six airfoil sections are optimized, to reduce the influence of inlet shock loss as well as gas leak in blade tip clearance and hub resistance at blade root. Finally an optimal solution is obtained, which meets the total pressure requirement under given conditions and improves total pressure efficiency by about 6%

  9. A Multi-level hierarchic Markov process with Bayesian updating for herd optimization and simulation in dairy cattle

    NARCIS (Netherlands)

    Demeter, R.M.; Kristensen, A.R.; Dijkstra, J.; Oude Lansink, A.G.J.M.; Meuwissen, M.P.M.; Arendonk, van J.A.M.

    2011-01-01

    Herd optimization models that determine economically optimal insemination and replacement decisions are valuable research tools to study various aspects of farming systems. The aim of this study was to develop a herd optimization and simulation model for dairy cattle. The model determines

  10. Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

    International Nuclear Information System (INIS)

    Yong, Li; Ying-Gan, Tang

    2010-01-01

    A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method

  11. Radio-over-fiber linearization with optimized genetic algorithm CPWL model.

    Science.gov (United States)

    Mateo, Carlos; Carro, Pedro L; García-Dúcar, Paloma; De Mingo, Jesús; Salinas, Íñigo

    2017-02-20

    This article proposes an optimized version of a canonical piece-wise-linear (CPWL) digital predistorter in order to enhance the linearity of a radio-over-fiber (RoF) LTE mobile fronthaul. In this work, we propose a threshold allocation optimization process carried out by a genetic algorithm (GA) in order to optimize the CPWL model (GA-CPWL). Firstly, experiments show how the CPWL model outperforms the classical memory polynomial DPD in an intensity modulation/direct detection (IM/DD) RoF link. Then, the GA-CPWL predistorter is compared with the CPWL model in several scenarios, in order to verify that the proposed DPD offers better performance in different optical transmission conditions. Experimental results reveal that with a proper threshold allocation, the GA-CPWL predistorter offers very promising outcomes.

  12. Optimization methods for logical inference

    CERN Document Server

    Chandru, Vijay

    2011-01-01

    Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in

  13. Optimal Advertising with Stochastic Demand

    OpenAIRE

    George E. Monahan

    1983-01-01

    A stochastic, sequential model is developed to determine optimal advertising expenditures as a function of product maturity and past advertising. Random demand for the product depends upon an aggregate measure of current and past advertising called "goodwill," and the position of the product in its life cycle measured by sales-to-date. Conditions on the parameters of the model are established that insure that it is optimal to advertise less as the product matures. Additional characteristics o...

  14. The physics of an optimal basketball free throw

    OpenAIRE

    Barzykina, Irina

    2017-01-01

    A physical model is developed, which suggests a pathway to determining the optimal release conditions for a basketball free throw. Theoretical framework is supported by Monte Carlo simulations and a series of free throws performed and analysed at Southbank International School. The model defines a smile-shaped success region in angle-velocity space where a free throw will score. A formula for the minimum throwing angle is derived analytically. The optimal throwing conditions are determined nu...

  15. Optimization of a Two-Fluid Hydrodynamic Model of Churn-Turbulent Flow

    Energy Technology Data Exchange (ETDEWEB)

    Donna Post Guillen

    2009-07-01

    A hydrodynamic model of two-phase, churn-turbulent flows is being developed using the computational multiphase fluid dynamics (CMFD) code, NPHASE-CMFD. The numerical solutions obtained by this model are compared with experimental data obtained at the TOPFLOW facility of the Institute of Safety Research at the Forschungszentrum Dresden-Rossendorf. The TOPFLOW data is a high quality experimental database of upward, co-current air-water flows in a vertical pipe suitable for validation of computational fluid dynamics (CFD) codes. A five-field CMFD model was developed for the continuous liquid phase and four bubble size groups using mechanistic closure models for the ensemble-averaged Navier-Stokes equations. Mechanistic models for the drag and non-drag interfacial forces are implemented to include the governing physics to describe the hydrodynamic forces controlling the gas distribution. The closure models provide the functional form of the interfacial forces, with user defined coefficients to adjust the force magnitude. An optimization strategy was devised for these coefficients using commercial design optimization software. This paper demonstrates an approach to optimizing CMFD model parameters using a design optimization approach. Computed radial void fraction profiles predicted by the NPHASE-CMFD code are compared to experimental data for four bubble size groups.

  16. Challenges in wind farm optimization

    DEFF Research Database (Denmark)

    Larsen, Gunner Chr.

    To achieve the optimal economic output from a wind farm over its lifetime, an optimal balance between capital costs, operation and maintenance costs, fatigue lifetime consumption of turbine components and power production is to be determined on a rational basis. This has implications both...... for the wind turbine modeling, where aeroelastic models are required, and for the wind farm flow field description, where in-stationary flow field modeling is needed to capture the complicated mixture of atmospheric boundary layer (ABL) flows and upstream emitted meandering wind turbine wakes, which together...... dictates the fatigue loading of the individual wind turbines. Within an optimization context, the basic challenge in describing the in-stationary wind farm flow field is computational speed. The Dynamic Wake Meandering (DWM) model includes the basic features of a CFD Large Eddy Simulation approach...

  17. Canonical Analysis Technique as an Approach to Determine Optimal Conditions for Lactic Acid Production by Lactobacillus helveticus ATCC 15009

    Directory of Open Access Journals (Sweden)

    Marcelo Teixeira Leite

    2012-01-01

    Full Text Available The response surface methodology and canonical analysis were employed to find the most suitable conditions for Lactobacillus helveticus to produce lactic acid from cheese whey in batch fermentation. The analyzed variables were temperature, pH, and the concentrations of lactose and yeast extract. The experiments were carried out according to a central composite design with three center points. An empiric equation that correlated the concentration of lactic acid with the independent variables was proposed. The optimal conditions determined by the canonical analysis of the fitted model were 40°C, pH 6.8, 82 g/L of lactose, and 23.36 g/L of yeast extract. At this point, the lactic acid concentration reached 59.38 g/L. A subsequent fermentation, carried out under optimal conditions, confirmed the product concentration predicted by the adjusted model. This concentration of lactic acid is the highest ever reported for Lactobacillus helveticus ATCC 15009 in batch process using cheese whey as substrate.

  18. Determining the optimal number of independent components for reproducible transcriptomic data analysis.

    Science.gov (United States)

    Kairov, Ulykbek; Cantini, Laura; Greco, Alessandro; Molkenov, Askhat; Czerwinska, Urszula; Barillot, Emmanuel; Zinovyev, Andrei

    2017-09-11

    Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data. Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets. We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

  19. A comparison of the economic benefits of centralized and distributed model predictive control strategies for optimal and sub-optimal mine dewatering system designs

    International Nuclear Information System (INIS)

    Romero, Alberto; Millar, Dean; Carvalho, Monica; Maestre, José M.; Camacho, Eduardo F.

    2015-01-01

    Mine dewatering can represent up to 5% of the total energy demand of a mine, and is one of the mine systems that aim to guarantee safe operating conditions. As mines go deeper, dewatering pumping heads become bigger, potentially involving several lift stages. Greater depth does not only mean greater dewatering cost, but more complex systems that require more sophisticated control systems, especially if mine operators wish to gain benefits from demand response incentives that are becoming a routine part of electricity tariffs. This work explores a two stage economic optimization procedure of an underground mine dewatering system, comprising two lifting stages, each one including a pump station and a water reservoir. First, the system design is optimized considering hourly characteristic dewatering demands for twelve days, one day representing each month of the year to account for seasonal dewatering demand variations. This design optimization minimizes the annualized cost of the system, and therefore includes the investment costs in underground reservoirs. Reservoir size, as well as an hourly pumping operation plan are calculated for specific operating environments, defined by characteristic hourly electricity prices and water inflows (seepage and water use from production activities), at best known through historical observations for the previous year. There is no guarantee that the system design will remain optimal when it faces the water inflows and market determined electricity prices of the year ahead, or subsequent years ahead, because these remain unknown at design time. Consequently, the dewatering optimized system design is adopted subsequently as part of a Model Predictive Control (MPC) strategy that adaptively maintains optimality during the operations phase. Centralized, distributed and non-centralized MPC strategies are explored. Results show that the system can be reliably controlled using any of these control strategies proposed. Under the operating

  20. The analysis of optimal singular controls for SEIR model of tuberculosis

    Science.gov (United States)

    Marpaung, Faridawaty; Rangkuti, Yulita M.; Sinaga, Marlina S.

    2014-12-01

    The optimally of singular control for SEIR model of Tuberculosis is analyzed. There are controls that correspond to time of the vaccination and treatment schedule. The optimally of singular control is obtained by differentiate a switching function of the model. The result shows that vaccination and treatment control are singular.

  1. Regional gray matter abnormalities in patients with schizophrenia determined with optimized voxel-based morphometry

    Science.gov (United States)

    Guo, XiaoJuan; Yao, Li; Jin, Zhen; Chen, Kewei

    2006-03-01

    This study examined regional gray matter abnormalities across the whole brain in 19 patients with schizophrenia (12 males and 7 females), comparing with 11 normal volunteers (7 males and 4 females). The customized brain templates were created in order to improve spatial normalization and segmentation. Then automated preprocessing of magnetic resonance imaging (MRI) data was conducted using optimized voxel-based morphometry (VBM). The statistical voxel based analysis was implemented in terms of two-sample t-test model. Compared with normal controls, regional gray matter concentration in patients with schizophrenia was significantly reduced in the bilateral superior temporal gyrus, bilateral middle frontal and inferior frontal gyrus, right insula, precentral and parahippocampal areas, left thalamus and hypothalamus as well as, however, significant increases in gray matter concentration were not observed across the whole brain in the patients. This study confirms and extends some earlier findings on gray matter abnormalities in schizophrenic patients. Previous behavior and fMRI researches on schizophrenia have suggested that cognitive capacity decreased and self-conscious weakened in schizophrenic patients. These regional gray matter abnormalities determined through structural MRI with optimized VBM may be potential anatomic underpinnings of schizophrenia.

  2. Web malware spread modelling and optimal control strategies

    Science.gov (United States)

    Liu, Wanping; Zhong, Shouming

    2017-02-01

    The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice.

  3. Determination of Optimal Initial Weights of an Artificial Neural Network by Using the Harmony Search Algorithm: Application to Breakwater Armor Stones

    Directory of Open Access Journals (Sweden)

    Anzy Lee

    2016-05-01

    Full Text Available In this study, an artificial neural network (ANN model is developed to predict the stability number of breakwater armor stones based on the experimental data reported by Van der Meer in 1988. The harmony search (HS algorithm is used to determine the near-global optimal initial weights in the training of the model. The stratified sampling is used to sample the training data. A total of 25 HS-ANN hybrid models are tested with different combinations of HS algorithm parameters. The HS-ANN models are compared with the conventional ANN model, which uses a Monte Carlo simulation to determine the initial weights. Each model is run 50 times and the statistical analyses are conducted for the model results. The present models using stratified sampling are shown to be more accurate than those of previous studies. The statistical analyses for the model results show that the HS-ANN model with proper values of HS algorithm parameters can give much better and more stable prediction than the conventional ANN model.

  4. Modelling the host-pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization.

    Science.gov (United States)

    Dühring, Sybille; Ewald, Jan; Germerodt, Sebastian; Kaleta, Christoph; Dandekar, Thomas; Schuster, Stefan

    2017-07-01

    The release of fungal cells following macrophage phagocytosis, called non-lytic expulsion, is reported for several fungal pathogens. On one hand, non-lytic expulsion may benefit the fungus in escaping the microbicidal environment of the phagosome. On the other hand, the macrophage could profit in terms of avoiding its own lysis and being able to undergo proliferation. To analyse the causes of non-lytic expulsion and the relevance of macrophage proliferation in the macrophage- Candida albicans interaction, we employ Evolutionary Game Theory and dynamic optimization in a sequential manner. We establish a game-theoretical model describing the different strategies of the two players after phagocytosis. Depending on the parameter values, we find four different Nash equilibria and determine the influence of the systems state of the host upon the game. As our Nash equilibria are a direct consequence of the model parameterization, we can depict several biological scenarios. A parameter region, where the host response is robust against the fungal infection, is determined. We further apply dynamic optimization to analyse whether macrophage mitosis is relevant in the host-pathogen interaction of macrophages and C. albicans For this, we study the population dynamics of the macrophage- C. albicans interactions and the corresponding optimal controls for the macrophages, indicating the best macrophage strategy of switching from proliferation to attacking fungal cells. © 2017 The Author(s).

  5. Sustainable logistics and transportation optimization models and algorithms

    CERN Document Server

    Gakis, Konstantinos; Pardalos, Panos

    2017-01-01

    Focused on the logistics and transportation operations within a supply chain, this book brings together the latest models, algorithms, and optimization possibilities. Logistics and transportation problems are examined within a sustainability perspective to offer a comprehensive assessment of environmental, social, ethical, and economic performance measures. Featured models, techniques, and algorithms may be used to construct policies on alternative transportation modes and technologies, green logistics, and incentives by the incorporation of environmental, economic, and social measures. Researchers, professionals, and graduate students in urban regional planning, logistics, transport systems, optimization, supply chain management, business administration, information science, mathematics, and industrial and systems engineering will find the real life and interdisciplinary issues presented in this book informative and useful.

  6. Optimal Resource Management in a Stochastic Schaefer Model

    OpenAIRE

    Richard Hartman

    2008-01-01

    This paper incorporates uncertainty into the growth function of the Schaefer model for the optimal management of a biological resource. There is a critical value for the biological stock, and it is optimal to do no harvesting if the biological stock is below that critical value and to exert whatever harvesting effort is necessary to prevent the stock from rising above that critical value. The introduction of uncertainty increases the critical value of the stock.

  7. Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

    DEFF Research Database (Denmark)

    Chen, Peiyuan; Siano, Pierluigi; Bak-Jensen, Birgitte

    2010-01-01

    This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements....... The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the basis of a limited autoregressive integrated moving average (LARIMA) model by introducing a crosscorrelation...... structure to the LARIMA model. The proposed stochastic optimization is carried out on a 69-bus distribution system. Simulation results confirm that, under various combinations of WPG and load demand, the system power losses are considerably reduced with the optimal setting of WT power factor as compared...

  8. Spectroscopic determination of optimal hydration time of zircon surface

    Energy Technology Data Exchange (ETDEWEB)

    Ordonez R, E. [ININ, Departamento de Quimica, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico); Garcia R, G. [Instituto Tecnologico de Toluca, Division de Estudios del Posgrado, Av. Tecnologico s/n, Ex-Rancho La Virgen, 52140 Metepec, Estado de Mexico (Mexico); Garcia G, N., E-mail: eduardo.ordonez@inin.gob.m [Universidad Autonoma del Estado de Mexico, Facultad de Quimica, Av. Colon y Av. Tollocan, 50180 Toluca, Estado de Mexico (Mexico)

    2010-07-01

    When a mineral surface is immersed in an aqueous solution, it develops and electric charge produced by the amphoteric dissociation of hydroxyl groups created by the hydration of the solid surface. This is one influential surface property. The complete hydration process takes a time which is specific for each mineral species. The knowledge of the aqueous solution contact time for complete surface hydration is mandatory for further surface phenomena studies. This study deals with the optimal hydration time of the raw zircon (ZrSiO{sub 4}) surface comparing the classical potentiometric titrations with a fluorescence spectroscopy technique. The latter is easy and rea liable as it demands only one sample batch to determine the optimal time to ensure a total hydration of the zircon surface. The analytical results of neutron activation analysis showed the presence of trace quantities of Dy{sup 3+}, Eu{sup 3+} and Er{sup 3} in the bulk of zircon. The Dy{sup 3+} is structured in the zircon crystalline lattice and undergoes the same chemical reactions as zircon. Furthermore, the Dy{sup 3+} has a good fluorescent response whose intensity is enhanced by hydration molecules. The results show that, according to the potentiometric analysis, the hydration process for each batch (at least 8 sample batches) takes around 2 h, while the spectrometric method indicates only 5 minutes from only one batch. Both methods showed that the zircon surface have a 16 h optimal hydration time. (Author)

  9. Spectroscopic determination of optimal hydration time of zircon surface

    International Nuclear Information System (INIS)

    Ordonez R, E.; Garcia R, G.; Garcia G, N.

    2010-01-01

    When a mineral surface is immersed in an aqueous solution, it develops and electric charge produced by the amphoteric dissociation of hydroxyl groups created by the hydration of the solid surface. This is one influential surface property. The complete hydration process takes a time which is specific for each mineral species. The knowledge of the aqueous solution contact time for complete surface hydration is mandatory for further surface phenomena studies. This study deals with the optimal hydration time of the raw zircon (ZrSiO 4 ) surface comparing the classical potentiometric titrations with a fluorescence spectroscopy technique. The latter is easy and rea liable as it demands only one sample batch to determine the optimal time to ensure a total hydration of the zircon surface. The analytical results of neutron activation analysis showed the presence of trace quantities of Dy 3+ , Eu 3+ and Er 3 in the bulk of zircon. The Dy 3+ is structured in the zircon crystalline lattice and undergoes the same chemical reactions as zircon. Furthermore, the Dy 3+ has a good fluorescent response whose intensity is enhanced by hydration molecules. The results show that, according to the potentiometric analysis, the hydration process for each batch (at least 8 sample batches) takes around 2 h, while the spectrometric method indicates only 5 minutes from only one batch. Both methods showed that the zircon surface have a 16 h optimal hydration time. (Author)

  10. A model for optimization of process integration investments under uncertainty

    International Nuclear Information System (INIS)

    Svensson, Elin; Stroemberg, Ann-Brith; Patriksson, Michael

    2011-01-01

    The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency. -- Highlights: → Stochastic programming approach to long-term planning of process integration investments. → Extensive mathematical model formulation. → Multi-stage investment decisions and scenario-based modelling of uncertain energy prices. → Results illustrate how investments made now affect later investment and operation opportunities. → Approach for evaluation of robustness with respect to variations in probability distribution.

  11. Modeling and optimization of an electric power distribution network ...

    African Journals Online (AJOL)

    Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...

  12. Value versus Accuracy: application of seasonal forecasts to a hydro-economic optimization model for the Sudanese Blue Nile

    Science.gov (United States)

    Satti, S.; Zaitchik, B. F.; Siddiqui, S.; Badr, H. S.; Shukla, S.; Peters-Lidard, C. D.

    2015-12-01

    The unpredictable nature of precipitation within the East African (EA) region makes it one of the most vulnerable, food insecure regions in the world. There is a vital need for forecasts to inform decision makers, both local and regional, and to help formulate the region's climate change adaptation strategies. Here, we present a suite of different seasonal forecast models, both statistical and dynamical, for the EA region. Objective regionalization is performed for EA on the basis of interannual variability in precipitation in both observations and models. This regionalization is applied as the basis for calculating a number of standard skill scores to evaluate each model's forecast accuracy. A dynamically linked Land Surface Model (LSM) is then applied to determine forecasted flows, which drive the Sudanese Hydroeconomic Optimization Model (SHOM). SHOM combines hydrologic, agronomic and economic inputs to determine the optimal decisions that maximize economic benefits along the Sudanese Blue Nile. This modeling sequence is designed to derive the potential added value of information of each forecasting model to agriculture and hydropower management. A rank of each model's forecasting skill score along with its added value of information is analyzed in order compare the performance of each forecast. This research aims to improve understanding of how characteristics of accuracy, lead time, and uncertainty of seasonal forecasts influence their utility to water resources decision makers who utilize them.

  13. A model for HIV/AIDS pandemic with optimal control

    Science.gov (United States)

    Sule, Amiru; Abdullah, Farah Aini

    2015-05-01

    Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is pandemic. It has affected nearly 60 million people since the detection of the disease in 1981 to date. In this paper basic deterministic HIV/AIDS model with mass action incidence function are developed. Stability analysis is carried out. And the disease free equilibrium of the basic model was found to be locally asymptotically stable whenever the threshold parameter (RO) value is less than one, and unstable otherwise. The model is extended by introducing two optimal control strategies namely, CD4 counts and treatment for the infective using optimal control theory. Numerical simulation was carried out in order to illustrate the analytic results.

  14. Optimal Aging and Death

    DEFF Research Database (Denmark)

    Dalgaard, Carl-Johan; Strulik, Holger

    the representative consumer is subject to physiological aging. In modeling aging we draw on recent research in the fields of biology and medicine. The speed of the aging process, and thus the time of death, are endogenously determined by optimal health investments. We calibrate the model to US data and proceed...

  15. Model for economic optimization of solar power plants; Beraekningsmodell foer ekonomisk optimering av solelanlaeggningar

    Energy Technology Data Exchange (ETDEWEB)

    Widen, Joakim

    2011-01-15

    This is the final report from a project in which an early-design-phase tool for photovoltaic (PV) systems has been developed. The aim of the tool is to provide a quick and easy way to estimate the electricity production and the economy of a PV system. Although it is effective and easy to use, the model takes into account all the important factors that affect the design, performance and economy of a system, and makes it possible with more in-depth analyses as well. The intended users of the tool are both electricity end-users thinking on investing in a small-scale system and large investors planning in an early project phase for large-scale PV systems. The developed tool is a simulation tool rather than an optimization tool. However, as the model is efficient and simple to use, it is easy to vary parameters and input data in different scenarios to arrive at an optimal solution. In order for the tool to realistically estimate the load matching of a PV system, which depends on seasonal and diurnal variations in both load and production profiles, the computations are made on an hourly basis. An hourly resolution is the most common one in meteorological data and to increase the resolution further is neither practically possible nor required for accuracy. The hourly irradiation data used in the model were collected from the publicly available STRAaNG database, which is maintained by the Swedish Meteorological and Hydrological Institute (SMHI). Idealized hourly load profiles for typical Swedish end-user categories are also included in the tool. A general computational model was implemented in Matlab, which provided easy testing, visualization and validation of the model. The computations involved can be summarized in four main steps: 1 Radiation computations. This involves a transposition of radiation components to the tilted plane of the PV array. The model takes the orientation of the system into account and uses assumed albedo values of the surroundings to add ground

  16. Time dependent optimal switching controls in online selling models

    Energy Technology Data Exchange (ETDEWEB)

    Bradonjic, Milan [Los Alamos National Laboratory; Cohen, Albert [MICHIGAN STATE UNIV

    2010-01-01

    We present a method to incorporate dishonesty in online selling via a stochastic optimal control problem. In our framework, the seller wishes to maximize her average wealth level W at a fixed time T of her choosing. The corresponding Hamilton-Jacobi-Bellmann (HJB) equation is analyzed for a basic case. For more general models, the admissible control set is restricted to a jump process that switches between extreme values. We propose a new approach, where the optimal control problem is reduced to a multivariable optimization problem.

  17. The optimization model of the logging machinery usage in forestry practice

    Directory of Open Access Journals (Sweden)

    Jitka Janová

    2009-01-01

    Full Text Available The decision support systems commonly used in industry and economy managerial practice for optimizing the processes are based on algoritmization of the typical decision problems. In Czech forestry business, there is a lack of developed decision support systems, which could be easily used in daily practice. This stems from the fact, that the application of optimization methods is less successful in forestry decision making than in industry or economy due to inherent complexity of the forestry decision problems. There is worldwide ongoing research on optimization models applicable in forestry decision making, but the results are not globally applicable and moreover the cost of possibly arising software tools are indispensable. Especially small and medium forestry companies in Czech Republic can not afford such additional costs, although the results of optimization could positively in­fluen­ce not only the business itself but also the impact of forestry business on the environment. Hence there is a need for user friendly optimization models for forestry decision making in the area of Czech Republic, which could be easily solved in commonly available software, and whose results would be both, realistic and easily applicable in the daily decision making.The aim of this paper is to develop the optimization model for the machinery use planning in Czech logging firm in such a way, that the results can be obtained using MS EXCEL. The goal is to identify the integer number of particular machines which should be outsourced for the next period, when the total cost minimization is required. The linear programming model is designed covering the typical restrictions on available machinery and total volume of trees to be cut and transported. The model offers additional result in the form of optimal employment of particular machines. The solution procedure is described in detail and the results obtained are discussed with respect to its applicability in

  18. Determination of gallic acid with rhodanine by reverse flow injection analysis using simplex optimization.

    Science.gov (United States)

    Phakthong, Wilaiwan; Liawruangrath, Boonsom; Liawruangrath, Saisunee

    2014-12-01

    A reversed flow injection (rFI) system was designed and constructed for gallic acid determination. Gallic acid was determined based on the formation of chromogen between gallic acid and rhodanine, resulting in a colored product with a λmax at 520 nm. The optimum conditions for determining gallic acid were also investigated. Optimizations of the experimental conditions were carried out based on the so-call univariate method. The conditions obtained were 0.6% (w/v) rhodanine, 70% (v/v) ethanol, 0.9 mol L(-1) NaOH, 2.0 mL min(-1) flow rate, 75 μL injection loop and 600 cm mixing tubing length, respectively. Comparative optimizations of the experimental conditions were also carried out by multivariate or simplex optimization method. The conditions obtained were 1.2% (w/v) rhodanine, 70% (v/v) ethanol, 1.2 mol L(-1) NaOH, flow rate 2.5 mL min(-1), 75 μL injection loop and 600 cm mixing tubing length, respectively. It was found that the optimum conditions obtained by the former optimization method were mostly similar to those obtained by the latter method. The linear relationship between peak height and the concentration of gallic acid was obtained over the range of 0.1-35.0 mg L(-1) with the detection limit 0.081 mg L(-1). The relative standard deviations were found to be in the ranges 0.46-1.96% for 1, 10, 30 mg L(-1) of gallic acid (n=11). The method has the advantages of simplicity extremely high selectivity and high precision. The proposed method was successfully applied to the determination of gallic acid in longan samples without interferent effects from other common phenolic compounds that might be present in the longan samples collected in northern Thailand. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. Simulation-Optimization Model for Seawater Intrusion Management at Pingtung Coastal Area, Taiwan

    Science.gov (United States)

    Huang, P. S.; Chiu, Y.

    2015-12-01

    In 1970's, the agriculture and aquaculture were rapidly developed at Pingtung coastal area in southern Taiwan. The groundwater aquifers were over-pumped and caused the seawater intrusion. In order to remedy the contaminated groundwater and find the best strategies of groundwater usage, a management model to search the optimal groundwater operational strategies is developed in this study. The objective function is to minimize the total amount of injection water and a set of constraints are applied to ensure the groundwater levels and concentrations are satisfied. A three-dimension density-dependent flow and transport simulation model, called SEAWAT developed by U.S. Geological Survey, is selected to simulate the phenomenon of seawater intrusion. The simulation model is well calibrated by the field measurements and replaced by the surrogate model of trained artificial neural networks (ANNs) to reduce the computational time. The ANNs are embedded in the management model to link the simulation and optimization models, and the global optimizer of differential evolution (DE) is applied for solving the management model. The optimal results show that the fully trained ANNs could substitute the original simulation model and reduce much computational time. Under appropriate setting of objective function and constraints, DE can find the optimal injection rates at predefined barriers. The concentrations at the target locations could decrease more than 50 percent within the planning horizon of 20 years. Keywords : Seawater intrusion, groundwater management, numerical model, artificial neural networks, differential evolution

  20. Optimal dimensioning model of water distribution systems | Gomes ...

    African Journals Online (AJOL)

    This study is aimed at developing a pipe-sizing model for a water distribution system. The optimal solution minimises the system's total cost, which comprises the hydraulic network capital cost, plus the capitalised cost of pumping energy. The developed model, called Lenhsnet, may also be used for economical design when ...

  1. Modeling the optimal management of spent nuclear fuel

    International Nuclear Information System (INIS)

    Nachlas, J.A.; Kurstedt, H.A. Jr.; Swindle, D.W. Jr.; Korcz, K.O.

    1977-01-01

    Recent governmental policy decisions dictate that strategies for managing spent nuclear fuel be developed. Two models are constructed to investigate the optimum residence time and the optimal inventory withdrawal policy for fuel material that presently must be stored. The mutual utility of the models is demonstrated through reference case application

  2. Optimal Dynamic Advertising Strategy Under Age-Specific Market Segmentation

    Science.gov (United States)

    Krastev, Vladimir

    2011-12-01

    We consider the model proposed by Faggian and Grosset for determining the advertising efforts and goodwill in the long run of a company under age segmentation of consumers. Reducing this model to optimal control sub problems we find the optimal advertising strategy and goodwill.

  3. Design optimization and uncertainty analysis of SMA morphing structures

    International Nuclear Information System (INIS)

    Oehler, S D; Hartl, D J; Lopez, R; Malak, R J; Lagoudas, D C

    2012-01-01

    The continuing implementation of shape memory alloys (SMAs) as lightweight solid-state actuators in morphing structures has now motivated research into finding optimized designs for use in aerospace control systems. This work proposes methods that use iterative analysis techniques to determine optimized designs for morphing aerostructures and consider the impact of uncertainty in model variables on the solution. A combination of commercially available and custom coded tools is utilized. ModelCenter, a suite of optimization algorithms and simulation process management tools, is coupled with the Abaqus finite element analysis suite and a custom SMA constitutive model to assess morphing structure designs in an automated fashion. The chosen case study involves determining the optimized configuration of a morphing aerostructure assembly that includes SMA flexures. This is accomplished by altering design inputs representing the placement of active components to minimize a specified cost function. An uncertainty analysis is also conducted using design of experiment methods to determine the sensitivity of the solution to a set of uncertainty variables. This second study demonstrates the effective use of Monte Carlo techniques to simulate the variance of model variables representing the inherent uncertainty in component fabrication processes. This paper outlines the modeling tools used to execute each case study, details the procedures for constructing the optimization problem and uncertainty analysis, and highlights the results from both studies. (paper)

  4. NLP model and stochastic multi-start optimization approach for heat exchanger networks

    International Nuclear Information System (INIS)

    Núñez-Serna, Rosa I.; Zamora, Juan M.

    2016-01-01

    Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.

  5. Advanced Nuclear Fuel Cycle Transitions: Optimization, Modeling Choices, and Disruptions

    Science.gov (United States)

    Carlsen, Robert W.

    Many nuclear fuel cycle simulators have evolved over time to help understan the nuclear industry/ecosystem at a macroscopic level. Cyclus is one of th first fuel cycle simulators to accommodate larger-scale analysis with it liberal open-source licensing and first-class Linux support. Cyclus also ha features that uniquely enable investigating the effects of modeling choices o fuel cycle simulators and scenarios. This work is divided into thre experiments focusing on optimization, effects of modeling choices, and fue cycle uncertainty. Effective optimization techniques are developed for automatically determinin desirable facility deployment schedules with Cyclus. A novel method fo mapping optimization variables to deployment schedules is developed. Thi allows relationships between reactor types and scenario constraints to b represented implicitly in the variable definitions enabling the usage o optimizers lacking constraint support. It also prevents wasting computationa resources evaluating infeasible deployment schedules. Deployed power capacit over time and deployment of non-reactor facilities are also included a optimization variables There are many fuel cycle simulators built with different combinations o modeling choices. Comparing results between them is often difficult. Cyclus flexibility allows comparing effects of many such modeling choices. Reacto refueling cycle synchronization and inter-facility competition among othe effects are compared in four cases each using combinations of fleet of individually modeled reactors with 1-month or 3-month time steps. There are noticeable differences in results for the different cases. The larges differences occur during periods of constrained reactor fuel availability This and similar work can help improve the quality of fuel cycle analysi generally There is significant uncertainty associated deploying new nuclear technologie such as time-frames for technology availability and the cost of buildin advanced reactors

  6. Robust Optimization Model for Production Planning Problem under Uncertainty

    Directory of Open Access Journals (Sweden)

    Pembe GÜÇLÜ

    2017-01-01

    Full Text Available Conditions of businesses change very quickly. To take into account the uncertainty engendered by changes has become almost a rule while planning. Robust optimization techniques that are methods of handling uncertainty ensure to produce less sensitive results to changing conditions. Production planning, is to decide from which product, when and how much will be produced, with a most basic definition. Modeling and solution of the Production planning problems changes depending on structure of the production processes, parameters and variables. In this paper, it is aimed to generate and apply scenario based robust optimization model for capacitated two-stage multi-product production planning problem under parameter and demand uncertainty. With this purpose, production planning problem of a textile company that operate in İzmir has been modeled and solved, then deterministic scenarios’ and robust method’s results have been compared. Robust method has provided a production plan that has higher cost but, will result close to feasible and optimal for most of the different scenarios in the future.

  7. Real-time economic optimization for a fermentation process using Model Predictive Control

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert; Jørgensen, John Bagterp

    2014-01-01

    Fermentation is a widely used process in production of many foods, beverages, and pharmaceuticals. The main goal of the control system is to maximize profit of the fermentation process, and thus this is also the main goal of this paper. We present a simple dynamic model for a fermentation process...... and demonstrate its usefulness in economic optimization. The model is formulated as an index-1 differential algebraic equation (DAE), which guarantees conservation of mass and energy in discrete form. The optimization is based on recent advances within Economic Nonlinear Model Predictive Control (E......-NMPC), and also utilizes the index-1 DAE model. The E-NMPC uses the single-shooting method and the adjoint method for computation of the optimization gradients. The process constraints are relaxed to soft-constraints on the outputs. Finally we derive the analytical solution to the economic optimization problem...

  8. An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China

    Directory of Open Access Journals (Sweden)

    Zheng-Xin Wang

    2014-01-01

    Full Text Available The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and n series related, abbreviated as GDMC(1,n, performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n, n interpolation coefficients (taken as unknown parameters are introduced into the background values of the n variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC(1,n model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1,n model. The modelling results can assist the government in developing future policies regarding high-tech industry management.

  9. Power Grid Construction Project Portfolio Optimization Based on Bi-level programming model

    Science.gov (United States)

    Zhao, Erdong; Li, Shangqi

    2017-08-01

    As the main body of power grid operation, county-level power supply enterprises undertake an important emission to guarantee the security of power grid operation and safeguard social power using order. The optimization of grid construction projects has been a key issue of power supply capacity and service level of grid enterprises. According to the actual situation of power grid construction project optimization of county-level power enterprises, on the basis of qualitative analysis of the projects, this paper builds a Bi-level programming model based on quantitative analysis. The upper layer of the model is the target restriction of the optimal portfolio; the lower layer of the model is enterprises’ financial restrictions on the size of the enterprise project portfolio. Finally, using a real example to illustrate operation proceeding and the optimization result of the model. Through qualitative analysis and quantitative analysis, the bi-level programming model improves the accuracy and normative standardization of power grid enterprises projects.

  10. A PSO–GA optimal model to estimate primary energy demand of China

    International Nuclear Information System (INIS)

    Yu Shiwei; Wei Yiming; Wang Ke

    2012-01-01

    To improve estimation efficiency for future projections, the present study has proposed a hybrid algorithm, Particle Swarm Optimization and Genetic Algorithm optimal Energy Demand Estimating (PSO–GA EDE) model, for China. The coefficients of the three forms of the model (linear, exponential, and quadratic) are optimized by PSO–GA using factors, such as GDP, population, economic structure, urbanization rate, and energy consumption structure, that affect demand. Based on 20-year historical data between 1990 and 2009, the simulation results of the proposed model have greater accuracy and reliability than other single optimization methods. Moreover, it can be used with optimal coefficients for the energy demand projections of China. The departure coefficient method is applied to get the weights of the three forms of the model to obtain a combinational prediction. The energy demand of China is going to be 4.79, 4.04, and 4.48 billion tce in 2015, and 6.91, 5.03, and 6.11 billion tce (“standard” tons coal equivalent) in 2020 under three different scenarios. Further, the projection results are compared with other estimating methods. - Highlights: ► A hybrid algorithm PSO–GA optimal energy demands estimating model for China. ► Energy demand of China is estimated by 2020 in three different scenarios. ► The projection results are compared with other estimating methods.

  11. Qualitative and Quantitative Integrated Modeling for Stochastic Simulation and Optimization

    Directory of Open Access Journals (Sweden)

    Xuefeng Yan

    2013-01-01

    Full Text Available The simulation and optimization of an actual physics system are usually constructed based on the stochastic models, which have both qualitative and quantitative characteristics inherently. Most modeling specifications and frameworks find it difficult to describe the qualitative model directly. In order to deal with the expert knowledge, uncertain reasoning, and other qualitative information, a qualitative and quantitative combined modeling specification was proposed based on a hierarchical model structure framework. The new modeling approach is based on a hierarchical model structure which includes the meta-meta model, the meta-model and the high-level model. A description logic system is defined for formal definition and verification of the new modeling specification. A stochastic defense simulation was developed to illustrate how to model the system and optimize the result. The result shows that the proposed method can describe the complex system more comprehensively, and the survival probability of the target is higher by introducing qualitative models into quantitative simulation.

  12. Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

    Science.gov (United States)

    Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

    2006-01-01

    This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

  13. Geometry and Topology Optimization of Statically Determinate Beams under Fixed and Most Unfavorably Distributed Load

    Directory of Open Access Journals (Sweden)

    Agata Kozikowska

    Full Text Available Abstract The paper concerns topology and geometry optimization of statically determinate beams with an arbitrary number of pin supports. The beams are simultaneously exposed to uniform dead load and arbitrarily distributed live load and optimized for the absolute maximum bending moment. First, all the beams with fixed topology are subjected to geometrical optimization by genetic algorithm. Strict mathematical formulas for calculation of optimal geometrical parameters are found for all topologies and any ratio of dead to live load. Then beams with the same minimal values of the objective function and different topologies are classified into groups called topological classes. The detailed characteristics of these classes are described.

  14. A Convex Optimization Model and Algorithm for Retinex

    Directory of Open Access Journals (Sweden)

    Qing-Nan Zhao

    2017-01-01

    Full Text Available Retinex is a theory on simulating and explaining how human visual system perceives colors under different illumination conditions. The main contribution of this paper is to put forward a new convex optimization model for Retinex. Different from existing methods, the main idea is to rewrite a multiplicative form such that the illumination variable and the reflection variable are decoupled in spatial domain. The resulting objective function involves three terms including the Tikhonov regularization of the illumination component, the total variation regularization of the reciprocal of the reflection component, and the data-fitting term among the input image, the illumination component, and the reciprocal of the reflection component. We develop an alternating direction method of multipliers (ADMM to solve the convex optimization model. Numerical experiments demonstrate the advantages of the proposed model which can decompose an image into the illumination and the reflection components.

  15. PEM fuel cell model suitable for energy optimization purposes

    International Nuclear Information System (INIS)

    Caux, S.; Hankache, W.; Fadel, M.; Hissel, D.

    2010-01-01

    Many fuel cell stack models or fuel cell system models exist. A model must be built with a main objective, sometimes for accurate electro-chemical behavior description, sometimes for optimization procedure at a system level. In this paper, based on the fundamental reactions present in a fuel cell stack, an accurate model and identification procedure is presented for future energy management in a Hybrid Electrical Vehicle (HEV). The proposed approach extracts all important state variables in such a system and based on the control of the fuel cell's gas flows and temperature, simplification arises to a simple electrical model. Assumptions verified due to the control of the stack allow simplifying the relationships within keeping accuracy in the description of a global fuel cell stack behavior from current demand to voltage. Modeled voltage and current dynamic behaviors are compared with actual measurements. The obtained accuracy is sufficient and less time-consuming (versus other previously published system-oriented models) leading to a suitable model for optimization iterative off-line algorithms.

  16. PEM fuel cell model suitable for energy optimization purposes

    Energy Technology Data Exchange (ETDEWEB)

    Caux, S.; Hankache, W.; Fadel, M. [LAPLACE/CODIASE: UMR CNRS 5213, Universite de Toulouse - INPT, UPS, - ENSEEIHT: 2 rue Camichel BP7122, 31071 Toulouse (France); CNRS, LAPLACE, F-31071 Toulouse (France); Hissel, D. [FEMTO-ST ENISYS/FCLAB, UMR CNRS 6174, University of Franche-Comte, Rue Thierry Mieg, 90010 Belfort (France)

    2010-02-15

    Many fuel cell stack models or fuel cell system models exist. A model must be built with a main objective, sometimes for accurate electro-chemical behavior description, sometimes for optimization procedure at a system level. In this paper, based on the fundamental reactions present in a fuel cell stack, an accurate model and identification procedure is presented for future energy management in a Hybrid Electrical Vehicle (HEV). The proposed approach extracts all important state variables in such a system and based on the control of the fuel cell's gas flows and temperature, simplification arises to a simple electrical model. Assumptions verified due to the control of the stack allow simplifying the relationships within keeping accuracy in the description of a global fuel cell stack behavior from current demand to voltage. Modeled voltage and current dynamic behaviors are compared with actual measurements. The obtained accuracy is sufficient and less time-consuming (versus other previously published system-oriented models) leading to a suitable model for optimization iterative off-line algorithms. (author)

  17. OPTIMIZATION OF ATM AND BRANCH CASH OPERATIONS USING AN INTEGRATED CASH REQUIREMENT FORECASTING AND CASH OPTIMIZATION MODEL

    Directory of Open Access Journals (Sweden)

    Canser BİLİR

    2018-04-01

    Full Text Available In this study, an integrated cash requirement forecasting and cash inventory optimization model is implemented in both the branch and automated teller machine (ATM networks of a mid-sized bank in Turkey to optimize the bank’s cash supply chain. The implemented model’s objective is to minimize the idle cash levels at both branches and ATMs without decreasing the customer service level (CSL by providing the correct amount of cash at the correct location and time. To the best of our knowledge, the model is the first integrated model in the literature to be applied to both ATMs and branches simultaneously. The results demonstrated that the integrated model dramatically decreased the idle cash levels at both branches and ATMs without degrading the availability of cash and hence customer satisfaction. An in-depth analysis of the results also indicated that the results were more remarkable for branches. The results also demonstrated that the utilization of various seasonal indices plays a very critical role in the forecasting of cash requirements for a bank. Another unique feature of the study is that the model is the first to include the recycling feature of ATMs. The results demonstrated that as a result of the inclusion of the deliberate seasonal indices in the forecasting model, the integrated cash optimization models can be used to estimate the cash requirements of recycling ATMs.

  18. Building Energy Modeling and Control Methods for Optimization and Renewables Integration

    Science.gov (United States)

    Burger, Eric M.

    dynamics within a building by learning from sensor data. Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following. This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.

  19. Understanding performance properties of chemical engines under a trade-off optimization: Low-dissipation versus endoreversible model

    Science.gov (United States)

    Tang, F. R.; Zhang, Rong; Li, Huichao; Li, C. N.; Liu, Wei; Bai, Long

    2018-05-01

    The trade-off criterion is used to systemically investigate the performance features of two chemical engine models (the low-dissipation model and the endoreversible model). The optimal efficiencies, the dissipation ratios, and the corresponding ratios of the dissipation rates for two models are analytically determined. Furthermore, the performance properties of two kinds of chemical engines are precisely compared and analyzed, and some interesting physics is revealed. Our investigations show that the certain universal equivalence between two models is within the framework of the linear irreversible thermodynamics, and their differences are rooted in the different physical contexts. Our results can contribute to a precise understanding of the general features of chemical engines.

  20. A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

    International Nuclear Information System (INIS)

    Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

    2014-01-01

    Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)