Optimization modeling with spreadsheets
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
Optimization Modeling with Spreadsheets
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
NEMO Oceanic Model Optimization
Epicoco, I.; Mocavero, S.; Murli, A.; Aloisio, G.
2012-04-01
NEMO is an oceanic model used by the climate community for stand-alone or coupled experiments. Its parallel implementation, based on MPI, limits the exploitation of the emerging computational infrastructures at peta and exascale, due to the weight of communications. As case study we considered the MFS configuration developed at INGV with a resolution of 1/16° tailored on the Mediterranenan Basin. The work is focused on the analysis of the code on the MareNostrum cluster and on the optimization of critical routines. The first performance analysis of the model aimed at establishing how much the computational performance are influenced by the GPFS file system or the local disks and wich is the best domain decomposition. The results highlight that the exploitation of local disks can reduce the wall clock time up to 40% and that the best performance is achieved with a 2D decomposition when the local domain has a square shape. A deeper performance analysis highlights the obc_rad, dyn_spg and tra_adv routines are the most time consuming routines. The obc_rad implements the evaluation of the open boundaries and it has been the first routine to be optimized. The communication pattern implemented in obc_rad routine has been redesigned. Before the introduction of the optimizations all processes were involved in the communication, but only the processes on the boundaries have the actual data to be exchanged and only the data on the boundaries must be exchanged. Moreover the data along the vertical levels are "packed" and sent with only one MPI_send invocation. The overall efficiency increases compared with the original version, as well as the parallel speed-up. The execution time was reduced of about 33.81%. The second phase of optimization involved the SOR solver routine, implementing the Red-Black Successive-Over-Relaxation method. The high frequency of exchanging data among processes represent the most part of the overall communication time. The number of communication is
Risk modelling in portfolio optimization
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.
Pyomo optimization modeling in Python
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...
Strategic Airlift Assets Optimization Model
1994-09-01
AIRLIFT USING RMIP FROM LINE 1218 MODEL STATISTICS BLOCKS OF EQUATIONS 13 SINGLE EQUATIONS 6349 BLOCKS OF VARIABLES 10 SINGLE VARIABLES 8723 NON ZERO...COMPILATION 44.700 EXECUTION 0.090 CLOSEDOWN 45.480 TCTAL SECONDS Solution Report SOLVE AIRLIFT USING RMIP FROM LINE 1218 SOLVE SUMMARY MODEL AIRLIFT...OBJECIIVE Z TYPE RMIP DIRECTION MINIMIZE SOLVER OSL FROM LINE 1218 SOLVER STATUS 1 NORMAL COMPLETION * MODEL STATUS 1 OPTIMAL OBJECTIVE VALUE 37.0139
Optimal Strategy and Business Models
DEFF Research Database (Denmark)
Johnson, Peter; Foss, Nicolai Juul
2016-01-01
, it is possible to formalize useful notions of a business model, resources, and competitive advantage. The business model that underpins strategy may be seen as a set of constraints on resources that can be interpreted as controls in optimal control theory. Strategy then might be considered to be the control......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 Hamiltonian...... 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....
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
This paper describes the modelling, simulating and optimizing including experimental verification as being carried out as part of a Ph.D. project being written resp. supervised by the authors. The work covers dynamic performance of both water-tube boilers and fire tube boilers. A detailed dynamic...... model of the boiler has been developed and simulations carried out by means of the Matlab integration routines. The model is prepared as a dynamic model consisting of both ordinary differential equations and algebraic equations, together formulated as a Differential-Algebraic-Equation system. Being able...... to operate a boiler plant dynamically means that the boiler designs must be able to absorb any fluctuations in water level and temperature gradients resulting from the pressure change in the boiler. On the one hand a large water-/steam space may be required, i.e. to build the boiler as big as possible. Due...
HIERARCHICAL OPTIMIZATION MODEL ON GEONETWORK
Directory of Open Access Journals (Sweden)
Z. Zha
2012-07-01
Full Text Available In existing construction experience of Spatial Data Infrastructure (SDI, GeoNetwork, as the geographical information integrated solution, is an effective way of building SDI. During GeoNetwork serving as an internet application, several shortcomings are exposed. The first one is that the time consuming of data loading has been considerately increasing with the growth of metadata count. Consequently, the efficiency of query and search service becomes lower. Another problem is that stability and robustness are both ruined since huge amount of metadata. The final flaw is that the requirements of multi-user concurrent accessing based on massive data are not effectively satisfied on the internet. A novel approach, Hierarchical Optimization Model (HOM, is presented to solve the incapability of GeoNetwork working with massive data in this paper. HOM optimizes the GeoNetwork from these aspects: internal procedure, external deployment strategies, etc. This model builds an efficient index for accessing huge metadata and supporting concurrent processes. In this way, the services based on GeoNetwork can maintain stable while running massive metadata. As an experiment, we deployed more than 30 GeoNetwork nodes, and harvest nearly 1.1 million metadata. From the contrast between the HOM-improved software and the original one, the model makes indexing and retrieval processes more quickly and keeps the speed stable on metadata amount increasing. It also shows stable on multi-user concurrent accessing to system services, the experiment achieved good results and proved that our optimization model is efficient and reliable.
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
, and the total stress level (i.e. stresses introduced due to internal pressure plus stresses introduced due to temperature gradients) must always be kept below the allowable stress level. In this way, the increased water-/steam space that should allow for better dynamic performance, in the end causes limited...... freedom with respect to dynamic operation of the plant. By means of an objective function including as well the price of the plant as a quantification of the value of dynamic operation of the plant an optimization is carried out. The dynamic model of the boiler plant is applied to define parts...
Modelling, simulating and optimizing Boilers
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2003-01-01
, and the total stress level (i.e. stresses introduced due to internal pressure plus stresses introduced due to temperature gradients) must always be kept below the allowable stress level. In this way, the increased water-/steam space that should allow for better dynamic performance, in the end causes limited...... freedom with respect to dynamic operation of the plant. By means of an objective function including as well the price of the plant as a quantication of the value of dynamic operation of the plant an optimization is carried out. The dynamic model of the boiler plant is applied to dene parts...
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-b...
Enterprise resource planning implementation decision & optimization models
Institute of Scientific and Technical Information of China (English)
Wang Shaojun; Wang Gang; Lü Min; Gao Guoan
2008-01-01
To study the uncertain optimization problems on implementation schedule, time-cost trade-off and quality in enterprise resource planning (ERP) implementation, combined with program evaluation and review technique (PERT), some optimization models are proposed, which include the implementation schedule model, the timecost trade-off model, the quality model, and the implementation time-cost-quality synthetic optimization model. A PERT-embedded genetic algorithm (GA) based on stochastic simulation technique is introduced to the optimization models solution. Finally, an example is presented to show that the models and algorithm are reasonable and effective, which can offer a reliable quantitative decision method for ERP implementation.
Product model structure for generalized optimal design
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The framework of the generalized optimization product model with the core of network- and tree-hierarchical structure is advanced to improve the characteristics of the generalized optimal design. Based on the proposed node-repetition technique, a network-hierarchical structure is united with the tree-hierarchical structure to facilitate the modeling of serialization and combination products. The criteria for product decomposition are investigated. Seven tree nodes are defined for the construction of a general product model, and their modeling properties are studied in detail. The developed product modeling system is applied and examined successfully in the modeling practice of the generalized optimal design for a hydraulic excavator.
Strategic Material Shortfall Risk Mitigation Optimization Model (OPTIM-SM)
2013-04-01
contracts, could be added to the existing mix . Market 40 responses to supply and demand shocks could be modeled more explicitly as could...Model (OPTIM-SM) James S. Thomason, Project Leader D. Sean Barnett James P. Bell Jerome Bracken Eleanor L. Schwartz INSTITUTE FOR DEFENSE ANALYSES 4850...Risk Mitigation Optimization Model (OPTIM-SM) James S. Thomason, Project Leader D. Sean Barnett James P. Bell Jerome Bracken Eleanor L. Schwartz iii
Optimal crossover designs for the proportional model
Zheng, Wei
2013-01-01
In crossover design experiments, the proportional model, where the carryover effects are proportional to their direct treatment effects, has draw attentions in recent years. We discover that the universally optimal design under the traditional model is E-optimal design under the proportional model. Moreover, we establish equivalence theorems of Kiefer-Wolfowitz's type for four popular optimality criteria, namely A, D, E and T (trace).
Optimal design for nonlinear response models
Fedorov, Valerii V
2013-01-01
Optimal Design for Nonlinear Response Models discusses the theory and applications of model-based experimental design with a strong emphasis on biopharmaceutical studies. The book draws on the authors' many years of experience in academia and the pharmaceutical industry. While the focus is on nonlinear models, the book begins with an explanation of the key ideas, using linear models as examples. Applying the linearization in the parameter space, it then covers nonlinear models and locally optimal designs as well as minimax, optimal on average, and Bayesian designs. The authors also discuss ada
Modelling, simulating and optimizing Boilers
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2003-01-01
of the boiler has been developed and simulations carried out by means of the Matlab integration routines. The model is prepared as a dynamic model consisting of both ordinary differential equations and algebraic equations, together formulated as a Differential-Algebraic- Equation system. Being able to operate...
Optimal Hedging with the Vector Autoregressive Model
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
Optimization in engineering models and algorithms
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 ...
Portfolio Optimization Model with Transaction Costs
Institute of Scientific and Technical Information of China (English)
Shu-ping Chen; Chong Li; Sheng-hong Li; Xiong-wei Wu
2002-01-01
The purpose of the article is to formulate, under the l∞ risk measure, a model of portfolio selection with transaction costs and then investigate the optimal strategy within the proposed. The characterization of a optimal strategy and the efficient algorithm for finding the optimal strategy are given.
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2004-01-01
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...... size. The model has been formulated with a specied building-up of the pressure during the start-up of the plant, i.e. the steam production during start-up of the boiler is output from the model. The steam outputs together with requirements with respect to steam space load have been utilized to dene...
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels
2004-01-01
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...... size. The model has been formulated with a specied building-up of the pressure during the start-up of the plant, i.e. the steam production during start-up of the boiler is output from the model. The steam outputs together with requirements with respect to steam space load have been utilized to dene...... of the boiler is (with an acceptable accuracy) proportional with the volume of the boiler. For the dynamic operation capability a cost function penalizing limited dynamic operation capability and vise-versa has been dened. The main idea is that it by mean of the parameters in this function is possible to t its...
Optimal Disturbance Accommodation with Limited Model Information
Farokhi, F; Johansson, K H
2011-01-01
The design of optimal dynamic disturbance-accommodation controller with limited model information is considered. We adapt the family of limited model information control design strategies, defined earlier by the authors, to handle dynamic-controllers. This family of limited model information design strategies construct subcontrollers distributively by accessing only local plant model information. The closed-loop performance of the dynamic-controllers that they can produce are studied using a performance metric called the competitive ratio which is the worst case ratio of the cost a control design strategy to the cost of the optimal control design with full model information.
Grey Model of the Investment Portfolio Optimization
Institute of Scientific and Technical Information of China (English)
LI Qun
2002-01-01
The theory of investment portfolio is a very important theory in the modern economical system.Based on the feature of the theory, the paper sets up new various kinds of models of investment portfolio,namely grey optimization models. These models are more practical and objective to existing problems.
Handbook on modelling for discrete optimization
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...
Portfolio optimization with mean-variance model
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.
Optimization models of natural communication
Ferrer-i-Cancho, Ramon
2014-01-01
A family of information theoretic models of communication was introduced more than a decade ago to explain the origins of Zipf's law for word frequencies. The family is a based on a combination of two information theoretic principles: maximization of mutual information between forms and meanings and minimization of form entropy. The family also sheds light on the origins of three other patterns: the principle of contrast, a related a vocabulary learning bias and the meaning-frequency law. Here two important components of the family, namely the information theoretic principles and the energy function that combines them linearly, are reviewed from the perspective of psycholinguistics, language learning, information theory and synergetic linguistics. The minimization of this linear function resembles a sort of agnostic information theoretic model selection that might be tuned by self-organization.
Real Life Decision Optimization Model
Raju, Naga; Reddy, Diwakar; Reddy, Rajeswara; Krishnaiah, G
2016-01-01
In real life scientific and engineering problems decision making is common practice. Decision making include single decision maker or group of decision makers. Decision maker’s expressions consists imprecise, inconsistent and indeterminate information. Also, the decision maker cannot select the best solution in unidirectional (single goal) way. Therefore, proposed model adopts decision makers’ opinions in Neutrosophic Values (SVNS/INV) which effectively deals imprecise, inconsistent and indet...
Surrogate Modeling for Geometry Optimization
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas; Holzwarth, Natalie;
2009-01-01
A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used.......A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used....
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.
Mathematical modeling and optimization of complex structures
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...
A New Car Following Model: Comprehensive Optimal Velocity Model
Institute of Scientific and Technical Information of China (English)
TIAN Jun-Fang; JIA Bin; LI Xing-Gang
2011-01-01
In this paper, we present a new car-following model, i.e.comprehensive optimal velocity model (COVM),whose optimal velocity function not only depends on the following distance of the preceding vehicle, but also depends on the velocity difference with preceding vehicle.Simulation results show that COVM is an improvement over the previous ones theoretically.Then, the stability condition of the model is obtained by the linear stability analysis, which has shorwn that the model could obtain a bigger stable region than previous models in the phase diagram.Through the nonlinear analysis, the Burgers, Korteweg-de Vries (KdV) and modified KdV (mKdV) equations are derived for the triangular shock wave, the soliton wave, and the kink-antikink soliton wave.At the same time, numerical simulations are edso carried out to show that the model could simulate these density waves.
Modelling in Optimal Inspection and Repair
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Rackwitz, R.; Faber, M.H.;
1991-01-01
A model for reliability based optimal inspection and repair strategies is described. The total expected costs in the lifetime is minimized with the number of inspections, the inspection times and efforts, the repair crack size limit and a design parameter as optimization variables. The equivalenc...
An Optimization Model Based on Game Theory
Directory of Open Access Journals (Sweden)
Yang Shi
2014-04-01
Full Text Available Game Theory has a wide range of applications in department of economics, but in the field of computer science, especially in the optimization algorithm is seldom used. In this paper, we integrate thinking of game theory into optimization algorithm, and then propose a new optimization model which can be widely used in optimization processing. This optimization model is divided into two types, which are called “the complete consistency” and “the partial consistency”. In these two types, the partial consistency is added disturbance strategy on the basis of the complete consistency. When model’s consistency is satisfied, the Nash equilibrium of the optimization model is global optimal and when the model’s consistency is not met, the presence of perturbation strategy can improve the application of the algorithm. The basic experiments suggest that this optimization model has broad applicability and better performance, and gives a new idea for some intractable problems in the field of artificial intelligence
Modelling and Optimizing Mathematics Learning in Children
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…
Maintenance Optimization of High Voltage Substation Model
Directory of Open Access Journals (Sweden)
Jan Gala
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.
Optimized $\\delta$ expansion for relativistic nuclear models
Krein, G I; Peres-Menezes, D; Nielsen, M; Pinto, M B
1998-01-01
The optimized $\\delta$-expansion is a nonperturbative approach for field theoretic models which combines the techniques of perturbation theory and the variational principle. This technique is discussed in the $\\lambda \\phi^4$ model and then implemented in the Walecka model for the equation of state of nuclear matter. The results obtained with the $\\delta$ expansion are compared with those obtained with the traditional mean field, relativistic Hartree and Hartree-Fock approximations.
Modeling and optimization of LCD optical performance
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
Enhanced index tracking modelling in portfolio optimization
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.
Dynamic optimization deterministic and stochastic models
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.
Optimal Data Split Methodology for Model Validation
Morrison, Rebecca; Terejanu, Gabriel; Miki, Kenji; Prudhomme, Serge
2011-01-01
The decision to incorporate cross-validation into validation processes of mathematical models raises an immediate question - how should one partition the data into calibration and validation sets? We answer this question systematically: we present an algorithm to find the optimal partition of the data subject to certain constraints. While doing this, we address two critical issues: 1) that the model be evaluated with respect to predictions of a given quantity of interest and its ability to reproduce the data, and 2) that the model be highly challenged by the validation set, assuming it is properly informed by the calibration set. This framework also relies on the interaction between the experimentalist and/or modeler, who understand the physical system and the limitations of the model; the decision-maker, who understands and can quantify the cost of model failure; and the computational scientists, who strive to determine if the model satisfies both the modeler's and decision maker's requirements. We also note...
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
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.
An overview of the optimization modelling applications
Singh, Ajay
2012-10-01
SummaryThe optimal use of available resources is of paramount importance in the backdrop of the increasing food, fiber, and other demands of the burgeoning global population and the shrinking resources. The optimal use of these resources can be determined by employing an optimization technique. The comprehensive reviews on the use of various programming techniques for the solution of different optimization problems have been provided in this paper. The past reviews are grouped into nine sections based on the solutions of the theme-based real world problems. The sections include: use of optimization modelling for conjunctive use planning, groundwater management, seawater intrusion management, irrigation management, achieving optimal cropping pattern, management of reservoir systems operation, management of resources in arid and semi-arid regions, solid waste management, and miscellaneous uses which comprise, managing problems of hydropower generation and sugar industry. Conclusions are drawn where gaps exist and more research needs to be focused.
Optimization of mathematical models for thematic maps
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The thematic map is a major class of maps designed to demonstrate particular features or concepts,functioning as an indispensable tool in geographical research.The process of thematic mapping is one into which geographical research goes deeply and broadly.The key activity and course of thematic map production is the use of mathematical models to create thematic data layers.Therefore,the selection and optimization of mathematical models is in the forefront of thematic map research.The theoretical foundations,mechanisms and methods of mathematical model optimization are expounded in this paper,including two approaches,the phase by phase mode and the multi-aim scheme balance mode.Case studies in eco-environment mapping and emergency mapping are described and analyzed,with a hierarchical analysis method being used in the model optimization for eco-environment fragility and sensitivity assessment mapping in Beibuwan (Guangxi) District,the dynamic system (DS) method being used in the model optimization for ecological security adjustment mapping in Xishuang Banna,Yunnan province,and the multi-phase mode being used in the models for forest fire and infectious diseases mapping.
An Optimization Model of Tunnel Support Parameters
Directory of Open Access Journals (Sweden)
Su Lijuan
2015-05-01
Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.
Modeling, simulation and optimization of bipedal walking
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...
Graphical Models for Optimal Power Flow
Dvijotham, Krishnamurthy; Chertkov, Michael; Misra, Sidhant; Vuffray, Marc
2016-01-01
Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithm for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary distribution networks an...
Hybrid optimization model of product concepts
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the "evolution - evaluation" iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the proposed method and associated algorithms.
MODELING AND OPTIMIZATION OF THE AEROCONCRETE TECHNOLOGY
Directory of Open Access Journals (Sweden)
Zhukov Aleksey Dmitrievich
2012-07-01
Selection of the appropriate composition and optimal technological parameters is performed with the help of G-BAT-2011 software programme developed at MSUCE. The software is based on the methodology that is based on complete factorial experiments, experiments based on fractional replicates and testing of all essential statistical hypotheses. Linear, incomplete quadratic and quadratic equations generated as a result of experiments make it possible to design a model that represents natural processes in the adequate manner. The model is analytically optimized and interpreted thereafter.
Stochastic Optimal Control Models for Online Stores
Bradonjić, Milan
2011-01-01
We present a model for the optimal design of an online auction/store by a seller. The framework we use is a stochastic optimal control problem. In our setting, the seller wishes to maximize her average wealth level, where she can control her price per unit via her reputation level. The corresponding Hamilton-Jacobi-Bellmann equation is analyzed for an introductory case. We then turn to an empirically justified model, and present introductory analysis. In both cases, {\\em pulsing} advertising strategies are recovered for resource allocation. Further numerical and functional analysis will appear shortly.
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.
Modeling and Optimization for Piercing Energy Consumption
Institute of Scientific and Technical Information of China (English)
XIAO Dong; PAN Xiao-li; YUAN Yong; MAO Zhi-zhong; WANG Fu-li
2009-01-01
Energy consumption is an important quality index in the production of seamless tubes. The complex factors affecting energy consumption make it difficult to build its mechanism model, and optimization is also very difficult, if not impossible. The piercing process was divided into three parts based on the production process, and an energy consumption prediction model was proposed based on the step mean value staged multiway partial least square meth-od. On the basis of the batch process prediction model, a genetic algorithm was adopted to calculate the optimum mean value of each process parameter and the minimum piercing energy consumption. Simulation proves that the op-timization method based on the energy consumption prediction model can obtain the optimum process parameters ef-fectively and also provide reliable evidences for practical production.
Constrained regression models for optimization and forecasting
Directory of Open Access Journals (Sweden)
P.J.S. Bruwer
2003-12-01
Full Text Available Linear regression models and the interpretation of such models are investigated. In practice problems often arise with the interpretation and use of a given regression model in spite of the fact that researchers may be quite "satisfied" with the model. In this article methods are proposed which overcome these problems. This is achieved by constructing a model where the "area of experience" of the researcher is taken into account. This area of experience is represented as a convex hull of available data points. With the aid of a linear programming model it is shown how conclusions can be formed in a practical way regarding aspects such as optimal levels of decision variables and forecasting.
Optimal control, optimization and asymptotic analysis of Purcell's microswimmer model
Wiezel, Oren; Or, Yizhar
2016-11-01
Purcell's swimmer (1977) is a classic model of a three-link microswimmer that moves by performing periodic shape changes. Becker et al. (2003) showed that the swimmer's direction of net motion is reversed upon increasing the stroke amplitude of joint angles. Tam and Hosoi (2007) used numerical optimization in order to find optimal gaits for maximizing either net displacement or Lighthill's energetic efficiency. In our work, we analytically derive leading-order expressions as well as next-order corrections for both net displacement and energetic efficiency of Purcell's microswimmer. Using these expressions enables us to explicitly show the reversal in direction of motion, as well as obtaining an estimate for the optimal stroke amplitude. We also find the optimal swimmer's geometry for maximizing either displacement or energetic efficiency. Additionally, the gait optimization problem is revisited and analytically formulated as an optimal control system with only two state variables, which can be solved using Pontryagin's maximum principle. It can be shown that the optimal solution must follow a "singular arc". Numerical solution of the boundary value problem is obtained, which exactly reproduces Tam and Hosoi's optimal gait.
Modeling optimal mineral nutrition for hazelnut micropropagation
Micropropagation of hazelnut (Corylus avellana L.) is typically difficult due to the wide variation in response among cultivars. This study was designed to overcome that difficulty by modeling the optimal mineral nutrients for micropropagation of C. avellana selections using a response surface desig...
Modelling Robust Design Problems via Conic Optimization
Chaerani, D.
2006-01-01
This thesis deals with optimization problems with uncertain data. Uncertainty here means that the data is not known exactly at the time when its solution has to be determined. In many models the uncertainty is ignored and a representative nominal value of the data is used. The uncertainty may be due
Applied probability models with optimization applications
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.
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
to check if the network is controllable. Afterward the pressure control problem in water supply systems is formulated as an optimal control problem. The goal is to minimize the power consumption in pumps and also to regulate the pressure drop at the end-users to a desired value. The formulated optimal...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...
Fuzzy Stochastic Optimization Theory, Models and Applications
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...
Optimal information diffusion in stochastic block models
Curato, Gianbiagio
2016-01-01
We use the linear threshold model to study the diffusion of information on a network generated by the stochastic block model. We focus our analysis on a two community structure where the initial set of informed nodes lies only in one of the two communities and we look for optimal network structures, i.e. those maximizing the asymptotic extent of the diffusion. We find that, constraining the mean degree and the fraction of initially informed nodes, the optimal structure can be assortative (modular), core-periphery, or even disassortative. We then look for minimal cost structures, i.e. those such that a minimal fraction of initially informed nodes is needed to trigger a global cascade. We find that the optimal networks are assortative but with a structure very close to a core-periphery graph, i.e. a very dense community linked to a much more sparsely connected periphery.
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
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.
Space Mapping Optimization of Microwave Circuits Exploiting Surrogate Models
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
A powerful new space-mapping (SM) optimization algorithm is presented in this paper. It draws upon recent developments in both surrogate model-based optimization and modeling of microwave devices, SM optimization is formulated as a general optimization problem of a surrogate model. This model...
Modeling and Optimization : Theory and Applications Conference
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.
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.
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.
Optimal Control Design with Limited Model Information
Farokhi, F; Johansson, K H
2011-01-01
We introduce the family of limited model information control design methods, which construct controllers by accessing the plant's model in a constrained way, according to a given design graph. We investigate the achievable closed-loop performance of discrete-time linear time-invariant plants under a separable quadratic cost performance measure with structured static state-feedback controllers. We find the optimal control design strategy (in terms of the competitive ratio and domination metrics) when the control designer has access to the local model information and the global interconnection structure of the plant-to-be-controlled. At last, we study the trade-off between the amount of model information exploited by a control design method and the best closed-loop performance (in terms of the competitive ratio) of controllers it can produce.
Optimization and mathematical modeling in computer architecture
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
Directory of Open Access Journals (Sweden)
Leonardo Leiderman
1992-03-01
Full Text Available Simulating an Optimizing Model of Currency Substitution This paper reports simulations based on the parameter estimates of an intertemporal model of currency substitution under nonexpected utility obtained by Bufman and Leiderman (1991. Here we first study the quantitative impact of changes in the degree of dollarization and in the elasticity of currency substitution on government seigniorage. Then, when examine whether the model can account for the comovement of consumption growth and assets' returnr after the 1985 stabilization program, and in particular for the consumption boom of 1986-87. The results are generally encouraging for future applications of optimizing models of currencysubstitution to policy and practical issues.
Aerodynamic Modelling and Optimization of Axial Fans
DEFF Research Database (Denmark)
Sørensen, Dan Nørtoft
A numerically efficient mathematical model for the aerodynamics oflow speed axial fans of the arbitrary vortex flow type has been developed.The model is based on a blade-element principle, whereby therotor is divided into a number of annular streamtubes.For each of these streamtubes relations...... for velocity, pressure andradial position are derived from the conservationlaws for mass, tangential momentum and energy.The resulting system of equations is non-linear and, dueto mass conservation and pressure equilibrium far downstream of the rotor,strongly coupled.The equations are solved using the Newton...... distributionsof pitch angle and chord length have been chosen as independent variablesin the optimizations.Besides restricting the geometry of the rotor,constraints have been added to ensure a required pressure rise as well asnon-stalled flow conditions.Optimizations have been performed tomaximize the mean value...
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.
Modeling and Optimization of Superhydrophobic Condensation
Miljkovic, Nenad; Enright, Ryan; Wang, Evelyn N.
2012-01-01
Superhydrophobic micro/nanostructured surfaces for dropwise condensation have recently received significant attention due to their potential to enhance heat transfer performance by shedding water droplets via coalescence-induced droplet jumping at length scales below the capillary length. However, achieving optimal surface designs for such behavior requires capturing the details of transport processes that is currently lacking. While comprehensive models have been developed for flat hydrophob...
Brookhaven buildings energy conservation optimization model
Energy Technology Data Exchange (ETDEWEB)
Carhart, S C; Mulherkar, S S; Sanborn, Y
1978-01-01
The Brookhaven Buildings Energy Conservation Optimization Model is a linear programming representation of energy use in buildings. Starting with engineering and economic data on cost and performance of energy technologies used in buildings, including both conversion devices (such as heat pumps) and structural improvements, the model constructs alternative flows for energy through the technologies to meet demands for space heating, air conditioning, thermal applications, and electric lighting and appliances. Alternative paths have different costs and efficiencies. Within constraints such as total demand for energy services, retirement of existing buildings, seasonal operation of certain devices, and others, the model calculates an optimal configuration of energy technologies in buildings. The penetration of the various basic technologies within this configuration is specified in considerable detail, covering new and retrofit markets for nine building types in four regions. Each market may choose from several appropriate conversion devices and four levels each of new and retrofit structural improvement. The principal applications for which the model was designed described briefly.
Combined optimization model for sustainable energization strategy
Abtew, Mohammed Seid
Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.
Parameter optimization in S-system models
Directory of Open Access Journals (Sweden)
Vasconcelos Ana
2008-04-01
Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.
Optimal control application to an Ebola model
Institute of Scientific and Technical Information of China (English)
Ebenezer Bonyah; Kingsley Badu; Samuel Kwesi Asiedu-Addo
2016-01-01
Ebola virus is a severe,frequently fatal illness,with a case fatality rate up to 90%.The outbreak of the disease has been acknowledged by World Health Organization as Public Health Emergency of International Concern.The threat of Ebola in West Africa is still a major setback to the socioeconomic development.Optimal control theory is applied to a system of ordinary differential equations which is modeling Ebola infection through three different routes including contact between humans and a dead body.In an attempt to reduce infection in susceptible population,a preventive control is put in the form of education and campaign and two treatment controls are applied to infected and late-stage infected(super) human population.The Pontryagins maximum principle is employed to characterize optimality control,which is then solved numerically.It is observed that time optimal control is existed in the model.The activation of each control showed a positive reduction of infection.The overall effect of activation of all the controls simultaneously reduced the effort required for the reduction of the infection quickly.The obtained results present a good framework for planning and designing cost-effective strategies for good interventions in dealing with Ebola disease.It is established that in order to reduce Ebola threat all the three controls must be taken into consideration concurrently.
Models and Methods for Free Material Optimization
DEFF Research Database (Denmark)
Weldeyesus, Alemseged Gebrehiwot
FMO problem formulations with stress constraints. These problems are highly nonlinear and lead to the so-called singularity phenomenon. The method described in the thesis has successfully solved these problems. In the numerical experiments the stress constraints have been satisfied with high...... conditions for physical attainability, in the context that, it has to be symmetric and positive semidefinite. FMO problems have been studied for the last two decades in many articles that led to the development of a wide range of models, methods, and theories. As the design variables in FMO are the local....... These problems are more difficult to solve and demand higher computational efforts than the standard optimization problems. The focus of today’s development of solution methods for FMO problems is based on first-order methods that require a large number of iterations to obtain optimal solutions. The scope...
Model based optimization of EMC input filters
Energy Technology Data Exchange (ETDEWEB)
Raggl, K; Kolar, J. W. [Swiss Federal Institute of Technology, Power Electronic Systems Laboratory, Zuerich (Switzerland); Nussbaumer, T. [Levitronix GmbH, Zuerich (Switzerland)
2008-07-01
Input filters of power converters for compliance with regulatory electromagnetic compatibility (EMC) standards are often over-dimensioned in practice due to a non-optimal selection of number of filter stages and/or the lack of solid volumetric models of the inductor cores. This paper presents a systematic filter design approach based on a specific filter attenuation requirement and volumetric component parameters. It is shown that a minimal volume can be found for a certain optimal number of filter stages for both the differential mode (DM) and common mode (CM) filter. The considerations are carried out exemplarily for an EMC input filter of a single phase power converter for the power levels of 100 W, 300 W, and 500 W. (author)
Business model optimization of Prego Gourmet
2013-01-01
A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics Prego Gourmet is a fast food restaurant which sells refined versions of a traditional Portuguese dish inside shopping centers in the area of Lisbon. The company is at the beginning of its expansion strategy. This work project is a prospective analysis on what the company should do to in order to optimize its business model and grow in Portug...
Velocity anticipation in the optimal velocity model
Institute of Scientific and Technical Information of China (English)
DONG Li-yun; WENG Xu-dan; LI Qing-ding
2009-01-01
In this paper,the velocity anticipation in the optimal velocity model (OVM) is investigated.The driver adjusts the velocity of his vehicle by the desired headway,which depends on both instantaneous headway and relative velocity.The effect of relative velocity is measured by a sensitivity function.A specific form of the sensitivity function is supposed and the involved parameters are determined by the both numerical simulation and empirical data.It is shown that inclusion of velocity anticipation enhances the stability of traffic flow.Numerical simulations show a good agreement with empirical data.This model provides a better description of real traffic,including the acceleration process from standing states and the deceleration process approaching a stopped car.
Utilizing computer models for optimizing classroom acoustics
Hinckley, Jennifer M.; Rosenberg, Carl J.
2002-05-01
The acoustical conditions in a classroom play an integral role in establishing an ideal learning environment. Speech intelligibility is dependent on many factors, including speech loudness, room finishes, and background noise levels. The goal of this investigation was to use computer modeling techniques to study the effect of acoustical conditions on speech intelligibility in a classroom. This study focused on a simulated classroom which was generated using the CATT-acoustic computer modeling program. The computer was utilized as an analytical tool in an effort to optimize speech intelligibility in a typical classroom environment. The factors that were focused on were reverberation time, location of absorptive materials, and background noise levels. Speech intelligibility was measured with the Rapid Speech Transmission Index (RASTI) method.
Optimal evolution models for quantum tomography
Czerwiński, Artur
2016-02-01
The research presented in this article concerns the stroboscopic approach to quantum tomography, which is an area of science where quantum physics and linear algebra overlap. In this article we introduce the algebraic structure of the parametric-dependent quantum channels for 2-level and 3-level systems such that the generator of evolution corresponding with the Kraus operators has no degenerate eigenvalues. In such cases the index of cyclicity of the generator is equal to 1, which physically means that there exists one observable the measurement of which performed a sufficient number of times at distinct instants provides enough data to reconstruct the initial density matrix and, consequently, the trajectory of the state. The necessary conditions for the parameters and relations between them are introduced. The results presented in this paper seem to have considerable potential applications in experiments due to the fact that one can perform quantum tomography by conducting only one kind of measurement. Therefore, the analyzed evolution models can be considered optimal in the context of quantum tomography. Finally, we introduce some remarks concerning optimal evolution models in the case of n-dimensional Hilbert space.
Solvable Optimal Velocity Models and Asymptotic Trajectory
Nakanishi, K; Igarashi, Y; Bando, M
1996-01-01
In the Optimal Velocity Model proposed as a new version of Car Following Model, it has been found that a congested flow is generated spontaneously from a homogeneous flow for a certain range of the traffic density. A well-established congested flow obtained in a numerical simulation shows a remarkable repetitive property such that the velocity of a vehicle evolves exactly in the same way as that of its preceding one except a time delay $T$. This leads to a global pattern formation in time development of vehicles' motion, and gives rise to a closed trajectory on $\\Delta x$-$v$ (headway-velocity) plane connecting congested and free flow points. To obtain the closed trajectory analytically, we propose a new approach to the pattern formation, which makes it possible to reduce the coupled car following equations to a single difference-differential equation (Rondo equation). To demonstrate our approach, we employ a class of linear models which are exactly solvable. We also introduce the concept of ``asymptotic traj...
Application of simulation models for the optimization of business processes
Jašek, Roman; Sedláček, Michal; Chramcov, Bronislav; Dvořák, Jiří
2016-06-01
The paper deals with the applications of modeling and simulation tools in the optimization of business processes, especially in solving an optimization of signal flow in security company. As a modeling tool was selected Simul8 software that is used to process modeling based on discrete event simulation and which enables the creation of a visual model of production and distribution processes.
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...
Optimal feedback scheduling of model predictive controllers
Institute of Scientific and Technical Information of China (English)
Pingfang ZHOU; Jianying XIE; Xiaolong DENG
2006-01-01
Model predictive control (MPC) could not be reliably applied to real-time control systems because its computation time is not well defined. Implemented as anytime algorithm, MPC task allows computation time to be traded for control performance, thus obtaining the predictability in time. Optimal feedback scheduling (FS-CBS) of a set of MPC tasks is presented to maximize the global control performance subject to limited processor time. Each MPC task is assigned with a constant bandwidth server (CBS), whose reserved processor time is adjusted dynamically. The constraints in the FSCBS guarantee scheduler of the total task set and stability of each component. The FS-CBS is shown robust against the variation of execution time of MPC tasks at runtime. Simulation results illustrate its effectiveness.
Kanban simulation model for production process optimization
Directory of Open Access Journals (Sweden)
Golchev Riste
2015-01-01
Full Text Available A long time has passed since the KANBAN system has been established as an efficient method for coping with the excessive inventory. Still, the possibilities for its improvement through its integration with other different approaches should be investigated further. The basic research challenge of this paper is to present benefits of KANBAN implementation supported with Discrete Event Simulation (DES. In that direction, at the beginning, the basics of KANBAN system are presented with emphasis on the information and material flow, together with a methodology for implementation of KANBAN system. Certain analysis on combining the simulation with this methodology is presented. The paper is concluded with a practical example which shows that through understanding the philosophy of the implementation methodology of KANBAN system and the simulation methodology, a simulation model can be created which can serve as a basis for a variety of experiments that can be conducted within a short period of time, resulting with production process optimization.
Optimized Markov State Models for Metastable Systems
Guarnera, Enrico
2016-01-01
A method is proposed to identify target states that optimize a metastability index amongst a set of trial states and use these target states as milestones to build Markov State Models. If the optimized metastability index is small, this automatically guarantees the accuracy of the MSM in the sense that the transitions between the target milestones is indeed approximately Markovian. The method is simple to implement and use, it does not require that the dynamics on the trial milestones be Markovian, and it also offers the possibility to partition the system's state-space by assigning every trial milestone to the target milestones it is most likely to visit next and to identify transition state regions. Here the method is tested on the Gly-Ala-Gly peptide, where it shown to correctly identify the known metastable states in the dihedral angle space of the molecule without a priori information about these states. It is also applied to analyze the folding landscape of the Beta3s min-protein, where it is shown to i...
Determining Reduced Order Models for Optimal Stochastic Reduced Order Models
Energy Technology Data Exchange (ETDEWEB)
Bonney, Matthew S. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Brake, Matthew R.W. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2015-08-01
The use of parameterized reduced order models(PROMs) within the stochastic reduced order model (SROM) framework is a logical progression for both methods. In this report, five different parameterized reduced order models are selected and critiqued against the other models along with truth model for the example of the Brake-Reuss beam. The models are: a Taylor series using finite difference, a proper orthogonal decomposition of the the output, a Craig-Bampton representation of the model, a method that uses Hyper-Dual numbers to determine the sensitivities, and a Meta-Model method that uses the Hyper-Dual results and constructs a polynomial curve to better represent the output data. The methods are compared against a parameter sweep and a distribution propagation where the first four statistical moments are used as a comparison. Each method produces very accurate results with the Craig-Bampton reduction having the least accurate results. The models are also compared based on time requirements for the evaluation of each model where the Meta- Model requires the least amount of time for computation by a significant amount. Each of the five models provided accurate results in a reasonable time frame. The determination of which model to use is dependent on the availability of the high-fidelity model and how many evaluations can be performed. Analysis of the output distribution is examined by using a large Monte-Carlo simulation along with a reduced simulation using Latin Hypercube and the stochastic reduced order model sampling technique. Both techniques produced accurate results. The stochastic reduced order modeling technique produced less error when compared to an exhaustive sampling for the majority of methods.
A DYNAMIC OPTIMAL ADVERTISING MODEL FOR NEW PRODUCTS
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Many dynamic optimal control models for advertising make efforts to solve the problem of determining optimal advertising expenditures and other variables of interest over time for a firm or several competing firms,However,after analyzing the extant literature,one can find that few dynamic optimal advertising models available consider the problem within the product diffusion framework.Furthermore,the established models involving product diffusion are inspired by the Bass model,which has been out of date.This paper poses a dynamic optimal advertising model for new products,which considers the product diffusion based on the relative newly developed generalized version of the Bass model.In this paper,the optimal control model is used to derive the optimal advertising expenditure policy,which gives some implications to advertising practice.
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.
Markowitz portfolio optimization model employing fuzzy measure
Ramli, Suhailywati; Jaaman, Saiful Hafizah
2017-04-01
Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.
Variational Data Assimilation for Optimizing Boundary Conditions in Ocean Models
Kazantsev, Christine; Tolstykh, Mikhail
2016-01-01
The review describes the development of ideas Gury Ivanovich Marchuk in the field of variational data assimilation for ocean models applied in particular in coupled models for long-range weather forecasts. Particular attention is paid to the optimization of boundary conditions on rigid boundaries. As idealized and realistic model configurations are considered. It is shown that the optimization allows us to determine the most sensitive model operators and bring the model solution closer to the assimilated data.
Initialization and Optimation of Deformable Models
DEFF Research Database (Denmark)
Jensen, Rune Fisker; Carstensen, Jens Michael; Madsen, Kaj
1999-01-01
The deformable model literature has in general been very focused on the formulation and development of new models or the solution of a specific application. Teh final and crucial steps of initialization and optimazation of the deformable model, needed for making inference, have received very little...
Modeling and Optimizing RF Multipole Ion Traps
Fanghaenel, Sven; Asvany, Oskar; Schlemmer, Stephan
2016-06-01
Radio frequency (rf) ion traps are very well suited for spectroscopy experiments thanks to the long time storage of the species of interest in a well defined volume. The electrical potential of the ion trap is determined by the geometry of its electrodes and the applied voltages. In order to understand the behavior of trapped ions in realistic multipole traps it is necessary to characterize these trapping potentials. Commercial programs like SIMION or COMSOL, employing the finite difference and/or finite element method, are often used to model the electrical fields of the trap in order to design traps for various purposes, e.g. introducing light from a laser into the trap volume. For a controlled trapping of ions, e.g. for low temperature trapping, the time dependent electrical fields need to be known to high accuracy especially at the minimum of the effective (mechanical) potential. The commercial programs are not optimized for these applications and suffer from a number of limitations. Therefore, in our approach the boundary element method (BEM) has been employed in home-built programs to generate numerical solutions of real trap geometries, e.g. from CAD drawings. In addition the resulting fields are described by appropriate multipole expansions. As a consequence, the quality of a trap can be characterized by a small set of multipole parameters which are used to optimize the trap design. In this presentation a few example calculations will be discussed. In particular the accuracy of the method and the benefits of describing the trapping potentials via multipole expansions will be illustrated. As one important application heating effects of cold ions arising from non-ideal multipole fields can now be understood as a consequence of imperfect field configurations.
Optimal pricing decision model based on activity-based costing
Institute of Scientific and Technical Information of China (English)
王福胜; 常庆芳
2003-01-01
In order to find out the applicability of the optimal pricing decision model based on conventional costbehavior model after activity-based costing has given strong shock to the conventional cost behavior model andits assumptions, detailed analyses have been made using the activity-based cost behavior and cost-volume-profitanalysis model, and it is concluded from these analyses that the theory behind the construction of optimal pri-cing decision model is still tenable under activity-based costing, but the conventional optimal pricing decisionmodel must be modified as appropriate to the activity-based costing based cost behavior model and cost-volume-profit analysis model, and an optimal pricing decision model is really a product pricing decision model construc-ted by following the economic principle of maximizing profit.
Visual prosthesis wireless energy transfer system optimal modeling.
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.
An optimal promotion cost control model for a markovian manpower ...
African Journals Online (AJOL)
An optimal promotion cost control model for a markovian manpower system. ... Log in or Register to get access to full text downloads. ... A theory concerning the existence of an optimal promotion control strategy for controlling a Markovian ...
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.
Hybrid and adaptive meta-model-based global optimization
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
Method of product portfolio analysis based on optimization models
Directory of Open Access Journals (Sweden)
V.M. Lozyuk
2011-12-01
Full Text Available The research is devoted to optimization of the structure of product portfolio of trading company with using the principles of the investment modeling. We further developed the models of investment portfolio optimization, using the known Markowitz and Sharp methods to determine the optimal portfolio of trade company. Adapted to the goods market the models in this study could be applied to the business of trade companies.
Parameter optimization model in electrical discharge machining process
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.
Improved Propulsion Modeling for Low-Thrust Trajectory Optimization
Knittel, Jeremy M.; Englander, Jacob A.; Ozimek, Martin T.; Atchison, Justin A.; Gould, Julian J.
2017-01-01
Low-thrust trajectory design is tightly coupled with spacecraft systems design. In particular, the propulsion and power characteristics of a low-thrust spacecraft are major drivers in the design of the optimal trajectory. Accurate modeling of the power and propulsion behavior is essential for meaningful low-thrust trajectory optimization. In this work, we discuss new techniques to improve the accuracy of propulsion modeling in low-thrust trajectory optimization while maintaining the smooth derivatives that are necessary for a gradient-based optimizer. The resulting model is significantly more realistic than the industry standard and performs well inside an optimizer. A variety of deep-space trajectory examples are presented.
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment
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...
A MILP-Model for the Optimization of Transports
Björk, Kaj-Mikael
2010-09-01
This paper presents a work in developing a mathematical model for the optimization of transports. The decisions to be made are routing decisions, truck assignment and the determination of the pickup order for a set of loads and available trucks. The model presented takes these aspects into account simultaneously. The MILP model is implemented in the Microsoft Excel environment, utilizing the LP-solve freeware as the optimization engine and Visual Basic for Applications as the modeling interface.
A DYNAMIC OPTIMAL ADVERTISING MODEL FOR NEW PRODUCTS
Institute of Scientific and Technical Information of China (English)
DU Rong; HU Qiying
2003-01-01
Many dynamic optimal control models for advertising make efforts to solve theproblem of determining optimal advertising expenditures and other variables of interestover time for a firm or several competing firms. However, after analyzing the extantliterature, one can find that few dynamic optimal advertising models available considerthe problem within the product diffusion framework. Furthermore, the established modelsinvolving product diffusion are inspired by the Bass model, which has been out of date.This paper poses a dynamic optimal advertising model for new products, which considersthe product diffusion based on the relative newly developed generalized version of the Bassmodel. In this paper, the optimal control model is used to derive the optimal advertisingexpenditure policy, which gives some implications to advertising practice.
RF building block modelling : optimization and synthesis
Cheng, Wei
2012-01-01
For circuit designers it is desirable to have relatively simple RF circuit models that do give decent estimation accuracy and provide sufficient understanding of circuits. Chapter 2 in this thesis shows a general weak nonlinearity model that meets these demands. Using a method that is related to har
RF building block modeling: optimization and synthesis
Cheng, W.
2012-01-01
For circuit designers it is desirable to have relatively simple RF circuit models that do give decent estimation accuracy and provide sufficient understanding of circuits. Chapter 2 in this thesis shows a general weak nonlinearity model that meets these demands. Using a method that is related to
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.
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.
Model and method for optimizing heterogeneous systems
Antamoshkin, O. A.; Antamoshkina, O. A.; Zelenkov, P. V.; Kovalev, I. V.
2016-11-01
Methodology of distributed computing performance boost by reduction of delays number is proposed. Concept of n-dimentional requirements triangle is introduced. Dynamic mathematical model of resource use in distributed computing systems is described.
Optimization of experimental human leukemia models (review
Directory of Open Access Journals (Sweden)
D. D. Pankov
2012-01-01
Full Text Available Actual problem of assessing immunotherapy prospects including antigenpecific cell therapy using animal models was covered in this review.Describe the various groups of currently existing animal models and methods of their creating – from different immunodeficient mice to severalvariants of tumor cells engraftment in them. The review addresses the possibility of tumor stem cells studying using mouse models for the leukemia treatment with adoptive cell therapy including WT1. Also issues of human leukemia cells migration and proliferation in a mice withdifferent immunodeficiency degree are discussed. To assess the potential immunotherapy efficacy comparison of immunodeficient mouse model with clinical situation in oncology patients after chemotherapy is proposed.
Optimization of experimental human leukemia models (review
Directory of Open Access Journals (Sweden)
D. D. Pankov
2014-07-01
Full Text Available Actual problem of assessing immunotherapy prospects including antigenpecific cell therapy using animal models was covered in this review.Describe the various groups of currently existing animal models and methods of their creating – from different immunodeficient mice to severalvariants of tumor cells engraftment in them. The review addresses the possibility of tumor stem cells studying using mouse models for the leukemia treatment with adoptive cell therapy including WT1. Also issues of human leukemia cells migration and proliferation in a mice withdifferent immunodeficiency degree are discussed. To assess the potential immunotherapy efficacy comparison of immunodeficient mouse model with clinical situation in oncology patients after chemotherapy is proposed.
Optimization models in a transition economy
Sergienko, Ivan V; Koshlai, Ludmilla
2014-01-01
This book opens new avenues in understanding mathematical models within the context of a transition economy. The exposition lays out the methods for combining different mathematical structures and tools to effectively build the next model that will accurately reflect real world economic processes. Mathematical modeling of weather phenomena allows us to forecast certain essential weather parameters without any possibility of changing them. By contrast, modeling of transition economies gives us the freedom to not only predict changes in important indexes of all types of economies, but also to influence them more effectively in the desired direction. Simply put: any economy, including a transitional one, can be controlled. This book is useful to anyone who wants to increase profits within their business, or improve the quality of their family life and the economic area they live in. It is beneficial for undergraduate and graduate students specializing in the fields of Economic Informatics, Economic Cybernetic...
Surrogate Modeling for Geometry Optimization in Material Design
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas B.; Holzwarth, Natalie A.W.;
2007-01-01
We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)......We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)...
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.
Improved Modeling of Intelligent Tutoring Systems Using Ant Colony Optimization
Rastegarmoghadam, Mahin; Ziarati, Koorush
2017-01-01
Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is…
Modeling of Biological Intelligence for SCM System Optimization
Shengyong Chen; Yujun Zheng; Carlo Cattani; Wanliang Wang
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 c...
Mathematical Model For Engineering Analysis And Optimization
Sobieski, Jaroslaw
1992-01-01
Computational support for engineering design process reveals behavior of designed system in response to external stimuli; and finds out how behavior modified by changing physical attributes of system. System-sensitivity analysis combined with extrapolation forms model of design complementary to model of behavior, capable of direct simulation of effects of changes in design variables. Algorithms developed for this method applicable to design of large engineering systems, especially those consisting of several subsystems involving many disciplines.
Mathematical Model For Engineering Analysis And Optimization
Sobieski, Jaroslaw
1992-01-01
Computational support for engineering design process reveals behavior of designed system in response to external stimuli; and finds out how behavior modified by changing physical attributes of system. System-sensitivity analysis combined with extrapolation forms model of design complementary to model of behavior, capable of direct simulation of effects of changes in design variables. Algorithms developed for this method applicable to design of large engineering systems, especially those consisting of several subsystems involving many disciplines.
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.
On our best behavior: optimality models in human behavioral ecology.
Driscoll, Catherine
2009-06-01
This paper discusses problems associated with the use of optimality models in human behavioral ecology. Optimality models are used in both human and non-human animal behavioral ecology to test hypotheses about the conditions generating and maintaining behavioral strategies in populations via natural selection. The way optimality models are currently used in behavioral ecology faces significant problems, which are exacerbated by employing the so-called 'phenotypic gambit': that is, the bet that the psychological and inheritance mechanisms responsible for behavioral strategies will be straightforward. I argue that each of several different possible ways we might interpret how optimality models are being used for humans face similar and additional problems. I suggest some ways in which human behavioral ecologists might adjust how they employ optimality models; in particular, I urge the abandonment of the phenotypic gambit in the human case.
Optimization Model for Environmental Stress Screening of Electronic Components
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Environmental stress screening (ESS) is a technological process to reduce the costly early field failure ofelectronic components. This paper builds an optimization model for ESS of electronic components to obtain the optimalESS duration. The failure phenomena of ESS are modeled by mix ed distribution, and optimal ESS duration is definedby maximizing life-cycle cost savings under the condition of meeting reliability requirement.
Optimal designs for the Michaelis Menten model with correlated observations
Dette, Holger; Kunert, Joachim
2012-01-01
In this paper we investigate the problem of designing experiments for weighted least squares analysis in the Michaelis Menten model. We study the structure of exact D-optimal designs in a model with an autoregressive error structure. Explicit results for locally D-optimal are derived for the case where 2 observations can be taken per subject. Additionally standardized maximin D-optimal designs are obtained in this case. The results illustrate the enormous difficulties to find e...
The Optimal Selection for Restricted Linear Models with Average Estimator
Directory of Open Access Journals (Sweden)
Qichang Xie
2014-01-01
Full Text Available The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC, which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.
Process Model Construction and Optimization Using Statistical Experimental Design,
1988-04-01
Memo No. 88-442 ~LECTE March 1988 31988 %,.. MvAY 1 98 0) PROCESS MODEL CONSTRUCTION AND OPTIMIZATION USING STATISTICAL EXPERIMENTAL DESIGN Emmanuel...Sachs and George Prueger Abstract A methodology is presented for the construction of process models by the combination of physically based mechanistic...253-8138. .% I " Process Model Construction and Optimization Using Statistical Experimental Design" by Emanuel Sachs Assistant Professor and George
Optimization using surrogate models - by the space mapping technique
DEFF Research Database (Denmark)
Søndergaard, Jacob
2003-01-01
mapping surrogate has a lower approximation error for long steps. For short steps, however, the Taylor model of the expensive model is best, due to exact interpolation at the model origin. Five algorithms for space mapping optimization are presented and the numerical performance is evaluated. Three...... conditions are satisfied. So hybrid methods, combining the space mapping technique with classical optimization methods, should be used if convergence to high accuracy is wanted. Approximation abilities of the space mapping surrogate are compared with those of a Taylor model of the expensive model. The space...
Optimization using surrogate models - by the space mapping technique
DEFF Research Database (Denmark)
Søndergaard, Jacob
2003-01-01
mapping surrogate has a lower approximation error for long steps. For short steps, however, the Taylor model of the expensive model is best, due to exact interpolation at the model origin. Five algorithms for space mapping optimization are presented and the numerical performance is evaluated. Three...... conditions are satisfied. So hybrid methods, combining the space mapping technique with classical optimization methods, should be used if convergence to high accuracy is wanted. Approximation abilities of the space mapping surrogate are compared with those of a Taylor model of the expensive model. The space...
COBRA-SFS modifications and cask model optimization
Energy Technology Data Exchange (ETDEWEB)
Rector, D.R.; Michener, T.E.
1989-01-01
Spent-fuel storage systems are complex systems and developing a computational model for one can be a difficult task. The COBRA-SFS computer code provides many capabilities for modeling the details of these systems, but these capabilities can also allow users to specify a more complex model than necessary. This report provides important guidance to users that dramatically reduces the size of the model while maintaining the accuracy of the calculation. A series of model optimization studies was performed, based on the TN-24P spent-fuel storage cask, to determine the optimal model geometry. Expanded modeling capabilities of the code are also described. These include adding fluid shear stress terms and a detailed plenum model. The mathematical models for each code modification are described, along with the associated verification results. 22 refs., 107 figs., 7 tabs.
Space Mapping Optimization of Microwave Circuits Exploiting Surrogate Models
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
is a convex combination of a mapped coarse model and a linearized fine model. It exploits, in a novel way, a linear frequency-sensitive mapping. During the optimization iterates, the coarse and fine models are simulated at different sets of frequencies. This approach is shown to be especially powerful...
A novel fluence map optimization model incorporating leaf sequencing constraints.
Jin, Renchao; Min, Zhifang; Song, Enmin; Liu, Hong; Ye, Yinyu
2010-02-21
A novel fluence map optimization model incorporating leaf sequencing constraints is proposed to overcome the drawbacks of the current objective inside smoothing models. Instead of adding a smoothing item to the objective function, we add the total number of monitor unit (TNMU) requirement directly to the constraints which serves as an important factor to balance the fluence map optimization and leaf sequencing optimization process at the same time. Consequently, we formulate the fluence map optimization models for the trailing (left) leaf synchronized, leading (right) leaf synchronized and the interleaf motion constrained non-synchronized leaf sweeping schemes, respectively. In those schemes, the leaves are all swept unidirectionally from left to right. Each of those models is turned into a linear constrained quadratic programming model which can be solved effectively by the interior point method. Those new models are evaluated with two publicly available clinical treatment datasets including a head-neck case and a prostate case. As shown by the empirical results, our models perform much better in comparison with two recently emerged smoothing models (the total variance smoothing model and the quadratic smoothing model). For all three leaf sweeping schemes, our objective dose deviation functions increase much slower than those in the above two smoothing models with respect to the decreasing of the TNMU. While keeping plans in the similar conformity level, our new models gain much better performance on reducing TNMU.
A Niche Width Model of Optimal Specialization
Bruggeman, Jeroen; Ó Nualláin, Breanndán
2000-01-01
Niche width theory, a part of organizational ecology, predicts whether “specialist” or “generalist” forms of organizations have higher “fitness,” in a continually changing environment. To this end, niche width theory uses a mathematical model borrowed from biology. In this paper, we first loosen th
Optimal Experimental Design for Model Discrimination
Myung, Jay I.; Pitt, Mark A.
2009-01-01
Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it…
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
Reliability-based design optimization with progressive surrogate models
Kanakasabai, Pugazhendhi; Dhingra, Anoop K.
2014-12-01
Reliability-based design optimization (RBDO) has traditionally been solved as a nested (bilevel) optimization problem, which is a computationally expensive approach. Unilevel and decoupled approaches for solving the RBDO problem have also been suggested in the past to improve the computational efficiency. However, these approaches also require a large number of response evaluations during optimization. To alleviate the computational burden, surrogate models have been used for reliability evaluation. These approaches involve construction of surrogate models for the reliability computation at each point visited by the optimizer in the design variable space. In this article, a novel approach to solving the RBDO problem is proposed based on a progressive sensitivity surrogate model. The sensitivity surrogate models are built in the design variable space outside the optimization loop using the kriging method or the moving least squares (MLS) method based on sample points generated from low-discrepancy sampling (LDS) to estimate the most probable point of failure (MPP). During the iterative deterministic optimization, the MPP is estimated from the surrogate model for each design point visited by the optimizer. The surrogate sensitivity model is also progressively updated for each new iteration of deterministic optimization by adding new points and their responses. Four example problems are presented showing the relative merits of the kriging and MLS approaches and the overall accuracy and improved efficiency of the proposed approach.
Calibration of Conceptual Rainfall-Runoff Models Using Global Optimization
Directory of Open Access Journals (Sweden)
Chao Zhang
2015-01-01
Full Text Available Parameter optimization for the conceptual rainfall-runoff (CRR model has always been the difficult problem in hydrology since watershed hydrological model is high-dimensional and nonlinear with multimodal and nonconvex response surface and its parameters are obviously related and complementary. In the research presented here, the shuffled complex evolution (SCE-UA global optimization method was used to calibrate the Xinanjiang (XAJ model. We defined the ideal data and applied the method to observed data. Our results show that, in the case of ideal data, the data length did not affect the parameter optimization for the hydrological model. If the objective function was selected appropriately, the proposed method found the true parameter values. In the case of observed data, we applied the technique to different lengths of data (1, 2, and 3 years and compared the results with ideal data. We found that errors in the data and model structure lead to significant uncertainties in the parameter optimization.
Pavement maintenance optimization model using Markov Decision Processes
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.
Modeling and optimization of ultrasonic linear motors
Fernandez Lopez, José; Perriard, Yves
2007-01-01
Ultrasonic motors have received much attention these last years, in particular with regard to their modeling and their design principle. Their operating principle is based on piezoelectric ceramics that convert electrical energy into mechanical energy in the form of vibrations of an elastic body whose surface points perform an elliptic motion with a frequency in the ultrasonic range (≥ 20 kHz). The moving part, which is pressed against the vibrating body by a prestressing force, can move than...
An Optimization Model for Aircraft Service Logistics
Institute of Scientific and Technical Information of China (English)
Angus; Cheung; W; H; Ip; Angel; Lai; Eva; Cheung
2002-01-01
Scheduling is one of the most difficult issues in t he planning and operations of the aircraft services industry. In this paper, t he various scheduling problems in ground support operation of an aircraft mainte nance service company are addressed. The authors developed a set of vehicle rout ings to cover each schedule flights; the objectives pursued are the maximization of vehicle and manpower utilization and minimization of operation time. To obta in the goals, an integer-programming model with geneti...
Modelling Driver Assitance Systems by Optimal Control
Wang, M.; Daamen, W.; Hoogendoorn, S.P.; Van Arem, B.
2012-01-01
Driver assistance systems support drivers in operating vehicles in a safe, comfortable and efficient way, and thus may induce changes in traffic flow characteristics. This paper put forward a receding horizon control framework to model driver assistance systems. The accelerations of automated vehicles are determined to optimise a cost function, assuming other vehicles driving at stationary conditions over a prediction horizon. The flexibility of the framework is demonstrated with controller d...
On optimization of data assimilation in the HBM -circulation model
VÃ€hÃ€-PiikkiÃ¶, Olga
2015-01-01
The purpose of this study is to develop a method for optimizing the data assimilation system of the HIROMB-BOOS -model at the Finnish Meteorological Institute by finding an optimal time interval and an optimal grid for the data assimilation. This is needed to balance the extra time the data assimilation adds to the runtime of the model and the improved accuracy it provides. Data assimilation is the process of combining observations with a numerical model to improve the accuracy of the mod...
An optimization approach to kinetic model reduction for combustion chemistry
Lebiedz, Dirk
2013-01-01
Model reduction methods are relevant when the computation time of a full convection-diffusion-reaction simulation based on detailed chemical reaction mechanisms is too large. In this article, we review a model reduction approach based on optimization of trajectories and show its applicability to realistic combustion models. As most model reduction methods, it identifies points on a slow invariant manifold based on time scale separation in the dynamics of the reaction system. The numerical approximation of points on the manifold is achieved by solving a semi-infinite optimization problem, where the dynamics enter the problem as constraints. The proof of existence of a solution for an arbitrarily chosen dimension of the reduced model (slow manifold) is extended to the case of realistic combustion models including thermochemistry by considering the properties of proper maps. The model reduction approach is finally applied to three models based on realistic reaction mechanisms: 1. ozone decomposition as a small t...
Modeling and Optimization of Cement Raw Materials Blending Process
Directory of Open Access Journals (Sweden)
Xianhong Li
2012-01-01
Full Text Available This paper focuses on modelling and solving the ingredient ratio optimization problem in cement raw material blending process. A general nonlinear time-varying (G-NLTV model is established for cement raw material blending process via considering chemical composition, feed flow fluctuation, and various craft and production constraints. Different objective functions are presented to acquire optimal ingredient ratios under various production requirements. The ingredient ratio optimization problem is transformed into discrete-time single objective or multiple objectives rolling nonlinear constraint optimization problem. A framework of grid interior point method is presented to solve the rolling nonlinear constraint optimization problem. Based on MATLAB-GUI platform, the corresponding ingredient ratio software is devised to obtain optimal ingredient ratio. Finally, several numerical examples are presented to study and solve ingredient ratio optimization problems.
Nonlinear model predictive control based on collective neurodynamic optimization.
Yan, Zheng; Wang, Jun
2015-04-01
In general, nonlinear model predictive control (NMPC) entails solving a sequential global optimization problem with a nonconvex cost function or constraints. This paper presents a novel collective neurodynamic optimization approach to NMPC without linearization. Utilizing a group of recurrent neural networks (RNNs), the proposed collective neurodynamic optimization approach searches for optimal solutions to global optimization problems by emulating brainstorming. Each RNN is guaranteed to converge to a candidate solution by performing constrained local search. By exchanging information and iteratively improving the starting and restarting points of each RNN using the information of local and global best known solutions in a framework of particle swarm optimization, the group of RNNs is able to reach global optimal solutions to global optimization problems. The essence of the proposed collective neurodynamic optimization approach lies in the integration of capabilities of global search and precise local search. The simulation results of many cases are discussed to substantiate the effectiveness and the characteristics of the proposed approach.
Modeling and optimal design of multilayer thermal cantilever microactuators
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
A model of curvature and tip deflection of multilayer thermal cantilever actuators is derived.The sim-plified expression received from the model avoids inverting complex matrices enhances understanding and makes it easier to optimize the structure parameters.Experiment is performed,the modeled and experimental results demonstrate the validity of the model,and it also indicates that Young’s module makes great contribution to the deflection;therefore,thin layers cannot be ignored arbitrarily.
The Optimal Economic Order: the simplest model
J. Tinbergen (Jan)
1992-01-01
textabstractIn the last five years humanity has become faced with the problem of the optimal socioeconomic order more clearly than ever. After the confrontation of capitalism and socialism, which was the core of the Marxist thesis, the fact transpired that capitalism was not the optimal order. It wa
Life cycle optimization of automobile replacement: model and application.
Kim, Hyung Chul; Keoleian, Gregory A; Grande, Darby E; Bean, James C
2003-12-01
Although recent progress in automotive technology has reduced exhaust emissions per mile for new cars, the continuing use of inefficient, higher-polluting old cars as well as increasing vehicle miles driven are undermining the benefits of this progress. As a way to address the "inefficient old vehicle" contribution to this problem, a novel life cycle optimization (LCO) model is introduced and applied to the automobile replacement policy question. The LCO model determines optimal vehicle lifetimes, accounting for technology improvements of new models while considering deteriorating efficiencies of existing models. Life cycle inventories for different vehicle models that represent materials production, manufacturing, use, maintenance, and end-of-life environmental burdens are required as inputs to the LCO model. As a demonstration, the LCO model was applied to mid-sized passenger car models between 1985 and 2020. An optimization was conducted to minimize cumulative carbon monoxide (CO), non-methane hydrocarbon (NMHC), oxides of nitrogen (NOx), carbon dioxide (CO2), and energy use over the time horizon (1985-2020). For CO, NMHC, and NOx pollutants with 12000 mi of annual mileage, automobile lifetimes ranging from 3 to 6 yr are optimal for the 1980s and early 1990s model years while the optimal lifetimes are expected to be 7-14 yr for model year 2000s and beyond. On the other hand, a lifetime of 18 yr minimizes cumulative energy and CO2 based on driving 12000 miles annually. Optimal lifetimes are inversely correlated to annual vehicle mileage, especially for CO, NMHC, and NOx emissions. On the basis of the optimization results, policies improving durability of emission controls, retiring high-emitting vehicles, and improving fuel economies are discussed.
The General Optimal Market Area Model
1988-06-01
Spatial Competition, American Economic Review 68 (1978) 896. [19] G.M. Carter, J.M. Chaiken, and E. Ignall, Response Areas for Two Emergency Units...25] B.C. Eaton and R.G. Lipsey, The Non-Uniqueness of Equilibrium in the L6schian Location Model, American Economic Review 66 (1976) 77. [26, B.C...4 (1972) 154. [86] S. Valavanis, L6sch on Location, American Economic Review 45 (1955) 637. [87] B. Von Hohenbalken and D.S. West, Manhattan versus
Optimality models in the age of experimental evolution and genomics.
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.
Optimal vaccination and treatment of an epidemic network model
Energy Technology Data Exchange (ETDEWEB)
Chen, Lijuan [Department of Mathematics, Tongji University, Shanghai 200092 (China); College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350002 (China); Sun, Jitao, E-mail: sunjt@sh163.net [Department of Mathematics, Tongji University, Shanghai 200092 (China)
2014-08-22
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1–5 are presented to show the global stability and the efficiency of this optimal control. - Highlights: • Propose an optimally controlled SIRS epidemic model on heterogeneous networks. • Obtain criteria of global stability of the disease-free equilibrium and the endemic equilibrium. • Investigate existence of optimal control for the control problem. • The results be illustrated by some numerical simulations.
An Optimal Design Model for New Water Distribution Networks in ...
African Journals Online (AJOL)
An Optimal Design Model for New Water Distribution Networks in Kigali City. ... a Linear Programming Problem (LPP) which involves the design of a new network of water distribution considering the cost in the form of unit price ... Article Metrics.
The Optimal Portfolio Selection Model under g -Expectation
National Research Council Canada - National Science Library
Li Li
2014-01-01
This paper solves the optimal portfolio selection model under the framework of the prospect theory proposed by Kahneman and Tversky in the 1970s with decision rule replaced by the g -expectation introduced by Peng...
Modeling, Instrumentation, Automation, and Optimization of Water Resource Recovery Facilities.
Sweeney, Michael W; Kabouris, John C
2016-10-01
A review of the literature published in 2015 on topics relating to water resource recovery facilities (WRRF) in the areas of modeling, automation, measurement and sensors and optimization of wastewater treatment (or water resource reclamation) is presented.
The optimization model of the heat conduction structure
Institute of Scientific and Technical Information of China (English)
Yongcun Zhang; Shutian Liu
2008-01-01
An optimization model considering a novel thermal performance index to be the objective function is proposed for minimizing the highest temperature in this paper. Firstly, the performance of the conventional heat conduction optimization model, with the dissipation of heat transport potential capacity as the objective function, is evaluated by a one-dimensional heat conduction problem in a planar plate exchanger. Then, a new thermal performance index, named the geometric average temperature, is introduced. The new heat conduction optimization model, with the geometric average temperature as the objective function, is developed and the corresponding finite element formula is presented. The results show that the geometric average temperature is an ideal thermal performance index and the solution of the new model is close to the theoretical optimal solution.
Tradeoff Analysis for Optimal Multiobjective Inventory Model
Directory of Open Access Journals (Sweden)
Longsheng Cheng
2013-01-01
Full Text Available Deterministic inventory model, the economic order quantity (EOQ, reveals that carrying inventory or ordering frequency follows a relation of tradeoff. For probabilistic demand, the tradeoff surface among annual order, expected inventory and shortage are useful because they quantify what the firm must pay in terms of ordering workload and inventory investment to meet the customer service desired. Based on a triobjective inventory model, this paper employs the successive approximation to obtain efficient control policies outlining tradeoffs among conflicting objectives. The nondominated solutions obtained by successive approximation are further used to plot a 3D scatterplot for exploring the relationships between objectives. Visualization of the tradeoffs displayed by the scatterplots justifies the computation effort done in the experiment, although several iterations needed to reach a nondominated solution make the solution procedure lengthy and tedious. Information elicited from the inverse relationships may help managers make deliberate inventory decisions. For the future work, developing an efficient and effective solution procedure for tradeoff analysis in multiobjective inventory management seems imperative.
Optimal inference in dynamic models with conditional moment restrictions
DEFF Research Database (Denmark)
Christensen, Bent Jesper; Sørensen, Michael
By an application of the theory of optimal estimating function, optimal in- struments for dynamic models with conditional moment restrictions are derived. The general efficiency bound is provided, along with estimators attaining the bound. It is demonstrated that the optimal estimators are always...... optimal estimator reduces to Newey's. Specification and hypothesis testing in our framework are introduced. We derive the theory of optimal instruments and the associated asymptotic dis- tribution theory for general cases including non-martingale estimating functions and general history dependence...
A Total Generalized Optimal Velocity Model and Its Numerical Tests
Institute of Scientific and Technical Information of China (English)
ZHU Wen-xing; LIU Yun-cai
2008-01-01
A car-following model named total generalized optimal velocity model (TGOVM) was developed with a consideration of an arbitrary number of preceding vehicles before current one based on analyzing the previous models such as optimal velocity model (OVM), generalized OVM (GOVM) and improved GOVM (IGOVM). This model describes the physical phenomena of traffic flow more exactly and realistically than previous models. Also the performance of this model was checked out by simulating the acceleration and de- celeration process for a small delay time. On a single circular lane, the evolution of the traffic congestion was studied for a different number of headways and relative velocities of the preceding vehicles being taken into account. The simulation results show that TGOVM is reasonable and correct.
Deterministic operations research models and methods in linear optimization
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
Portfolio optimization for index tracking modelling in Malaysia stock market
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.
Optimal vaccination policies for an SIR model with limited resources.
Zhou, Yinggao; Yang, Kuan; Zhou, Kai; Liang, Yiting
2014-06-01
The purpose of the paper is to use analytical method and optimization tool to suggest a vaccination program intensity for a basic SIR epidemic model with limited resources for vaccination. We show that there are two different scenarios for optimal vaccination strategies, and obtain analytical solutions for the optimal control problem that minimizes the total cost of disease under the assumption of daily vaccine supply being limited. These solutions and their corresponding optimal control policies are derived explicitly in terms of initial conditions, model parameters and resources for vaccination. With sufficient resources, the optimal control strategy is the normal Bang-Bang control. However, with limited resources, the optimal control strategy requires to switch to time-variant vaccination.
Review: Optimization methods for groundwater modeling and management
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.
RPOA Model-Based Optimal Resource Provisioning
Directory of Open Access Journals (Sweden)
Noha El. Attar
2014-01-01
Full Text Available Optimal utilization of resources is the core of the provisioning process in the cloud computing. Sometimes the local resources of a data center are not adequate to satisfy the users’ requirements. So, the providers need to create several data centers at different geographical area around the world and spread the users’ applications on these resources to satisfy both service providers and customers QoS requirements. By considering the expansion of the resources and applications, the transmission cost and time have to be concerned as significant factors in the allocation process. According to the work of our previous paper, a Resource Provision Optimal Algorithm (RPOA based on Particle Swarm Optimization (PSO has been introduced to find the near optimal resource utilization with considering the customer budget and suitable for deadline time. This paper is considered an enhancement to RPOA algorithm to find the near optimal resource utilization with considering the data transfer time and cost, in addition to the customer budget and deadline time, in the performance measurement.
Water Modeling of Optimizing Tundish Flow Field
Institute of Scientific and Technical Information of China (English)
LIU Jin-gang; YAN Hui-cheng; LIU Liu; WANG Xin-hua
2007-01-01
In the water modeling experiments, three cases were considered, i.e. , a bare tundish, a tundish equipped with a turbulence inhibitor, and a rectangular tundish equipped with weirs (dams) and a turbulence inhibitor. Comparing the RTD curves, inclusion separation, and the result of the streamline experiment, it can be found that the tundish equipped with weirs (dams) and a turbulence inhibitor has a great effect on the flow field and the inclusion separation when compared with the sole use or no use of the turbulent inhibitor or weirs (dams). In addition, the enlargement of the distance between the weir and dam will result in a better effect when the tundish equipped with weirs (dam) and a turbulence inhibitor was used.
RF Circuit linearity optimization using a general weak nonlinearity model
Cheng, W.; Oude Alink, M.S.; Annema, Anne J.; Croon, Jeroen A.; Nauta, Bram
2012-01-01
This paper focuses on optimizing the linearity in known RF circuits, by exploring the circuit design space that is usually available in today’s deep submicron CMOS technologies. Instead of using brute force numerical optimizers we apply a generalized weak nonlinearity model that only involves AC
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...
Optimal Boundary Conditions for ORCA-2 Model
Kazantsev, Eugene
2012-01-01
A 4D-Var data assimilation technique is applied to a ORCA-2 configuration of the NEMO in order to identify the optimal parametrization of the boundary conditions on the lateral boundaries as well as on the bottom and on the surface of the ocean. The influence of the boundary conditions on the solution is analyzed as in the assimilation window and beyond the window. It is shown that optimal conditions for vertical operators allows to get stronger and finer jet streams (Gulf Stream, Kuroshio) in the solution. Analyzing the reasons of the jets reinforcement, we see that the major impact of the data assimilation is made on the parametrization of the bottom boundary conditions for lateral velocities u and v. Automatic generation of the tangent and adjoint codes is also discussed. Tapenade software is shown to be able to produce the adjoint code that can be used after a memory usage optimization.
Modeling for Optimal Control : A Validated Diesel-Electric Powertrain Model
Sivertsson, Martin; Eriksson, Lars
2014-01-01
An optimal control ready model of a diesel-electric powertrain is developed,validated and provided to the research community. The aim ofthe model is to facilitate studies of the transient control of diesel-electricpowertrains and also to provide a model for developers of optimizationtools. The resulting model is a four state three control mean valueengine model that captures the significant nonlinearity of the diesel engine, while still being continuously differentiable.
THE EXISTENCE THEOREM OF OPTIMAL GROWTH MODEL
Institute of Scientific and Technical Information of China (English)
Gong Liutang; Peng Xianze
2005-01-01
This paper proves a general existence theorem of optimal growth theory. This theorem is neither restricted to the case of a constant technology progress, nor stated in terms of mathematical conditions which have no direct economic interpretation and moreover, are difficult to apply.
Optimization and emergence in marine ecosystem models
DEFF Research Database (Denmark)
Mariani, Patrizio; Visser, Andre
2010-01-01
Ingestion rates and mortality rates of zooplankton are dynamic parameters reflecting a behavioural trade-off between encounters with food and predators. An evolutionarily consistent behaviour is that which optimizes the trade-off in terms of the fitness conferred to an individual. We argue that i....... All rights reserved....
LIMIT THEOREMS AND OPTIMAL DESIGN WITH ADAPTIVE URN MODELS
Institute of Scientific and Technical Information of China (English)
CHEN Guijing; ZHU Chunhua; WANG Yao-hung
2005-01-01
In this paper we study urn model, using some available estimates of successes probabilities, and adding particle parameter, we establish adaptive models. We obtain some strong convergence theorems, rates of convergence, asymptotic normality of components in the urn, and estimates. With these asymptotical results, we show that the adaptive designs given in this paper are asymptotically optimal designs.
Runtime Optimizations for Tree-Based Machine Learning Models
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
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment plants
Modeling and optimization of magnetostrictive actuator amplified by compliant mechanism
Niu, Muqing; Yang, Bintang; Yang, Yikun; Meng, Guang
2017-09-01
Magnetostrictive actuators are commonly used in precision engineering with the advantages of high resolution and fast response. Their limited strokes are always amplified by compliant mechanisms without wear and backlash. This paper proposes a hybrid model for the actuation system considering the coupling of the actuator and the amplifier. The magnetostrictive model, based on the Jiles-Atherton model, is related to the input stiffness of the amplifier when quantifying the magneto-mechanical effects, including stress-dependent magnetization, stress-dependent magnetostriction and ΔE effect. The compliant mechanism model aims at constructing the flexibility matrix with the amplification ratio and input stiffness related to the spring factor of the load. The deformation and structural stress of the amplifier are also dependent on the output strain of magnetostrictive material. Experiments under both free load and spring load conditions have been done to verify the effectiveness of the hybrid model. The proposed model is suitable for parameter optimization and the performance indicators can be precisely quantified. Optimization based on hybrid model is more preferred than optimizing the actuator and amplifier independently for maximum output displacement. Furthermore, ‘stiffness match principle’ is no longer applicable when considering ΔE effect, and the optimal external stiffness problem can be numerically solved by the hybrid model for maximum output energy of magnetostrictive material.
Reverse electrodialysis : A validated process model for design and optimization
Veerman, J.; Saakes, M.; Metz, S. J.; Harmsen, G. J.
2011-01-01
Reverse electrodialysis (RED) is a technology to generate electricity using the entropy of the mixing of sea and river water. A model is made of the RED process and validated experimentally. The model is used to design and optimize the RED process. It predicts very small differences between counter-
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment plants
Optimal Model-Based Control in HVAC Systems
DEFF Research Database (Denmark)
Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik;
2008-01-01
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...
Optimization of multi-model ensemble forecasting of typhoon waves
Directory of Open Access Journals (Sweden)
Shun-qi Pan
2016-01-01
Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.
Optimization Framework for Stochastic Modeling of Annual Streamflows
Srivastav, R. K.; Srinivasan, K.; Sudheer, K.
2008-12-01
Synthetic streamflow data generation involves the synthesis of likely streamflow patterns that are statistically indistinguishable from the observed streamflow data. The various kinds of stochastic models adopted for streamflow generation in hydrology are: i) parametric models which hypothesize the form of the dependence structure and the distributional form a priori (examples are AR, ARMA); ii) Nonparametric models (examples are bootstrap/kernel based methods), which characterize the laws of chance, describing the stream flow process, without recourse to prior assumptions as to the form or structure of these laws; iii) Hybrid models which blend both parametric and non-parametric models advantageously to model the streamflows effectively. Despite many of these developments that have taken place in the field of stochastic modeling of streamflows over the last four decades, accurate prediction of the storage and the critical drought (water use) characteristics has been posing a persistent challenge to the stochastic modeler. This may be because, usually, the stochastic streamflow model parameters are estimated by minimizing a statistically based objective function (such as maximum likelihood (MLE) or least squares estimation) and subsequently the efficacy of the models is being validated based on the accuracy of prediction of the estimates of the water- use characteristics. In this study a framework is proposed to find the optimal hybrid model (blend of ARMA(1,1) and moving block bootstrap (MBB)) based on the explicit objective function of minimizing the relative bias in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi-dimensional parameter space involving simultaneous exploration of the parametric (ARMA[1,1]) as well as the non-parametric (MBB) components. This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic
Optimal vaccination and treatment of an epidemic network model
Chen, Lijuan; Sun, Jitao
2014-08-01
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1-5 are presented to show the global stability and the efficiency of this optimal control.
General model for boring tool optimization
Moraru, G. M.; rbes, M. V. Ze; Popescu, L. G.
2016-08-01
Optimizing a tool (and therefore those for boring) consist in improving its performance through maximizing the objective functions chosen by the designer and/or by user. In order to define and to implement the proposed objective functions, contribute numerous features and performance required by tool users. Incorporation of new features makes the cutting tool to be competitive in the market and to meet user requirements.
Optimization of SAGD process with proxy models: case study of a 3-well-pair model
Energy Technology Data Exchange (ETDEWEB)
Fedutenko, Eugene; Yang, Chaodong; Card, Colin; Nghiem, Long [Computer Modeling Group Ltd. (Canada)
2011-07-01
In the heavy oil industry, steam assisted gravity drainage (SAGD) is a thermal recovery method used to enhance oil recovery. In order to improve the economics of SAGD operations, optimization of both SAGD operating conditions and well placements is necessary. Unfortunately this requires a large number of simulations which have a high computational cost. The aim of this paper is present a new workflow for improving the economics of SAGD with fewer simulations. A synthetic 3 well pair SAGD model was developed and simulations were conducted with a base case scenario and polynomial and Kriging proxy interpolation models. Results showed that both optimizations improved the NPV, SOR and RF of the model. In addition it was found that the polynomial model is faster than the Kriging model and that the Kriging model requires fewer iterations to obtain the optimal solution. This paper demonstrated that the use of polynomial or Kriging models helps improve the economics of SAGD operations.
Sensor Optimization Selection Model Based on Testability Constraint
Institute of Scientific and Technical Information of China (English)
YANG Shuming; QIU Jing; LIU Guanjun
2012-01-01
Sensor selection and optimization is one of the important parts in design for testability.To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability,a novel sensor optimization selection model is proposed.Firstly,a universal architecture for sensor selection and optimization is provided.Secondly,a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability.Thirdly,a sensor selection and optimization model for prognostics and health management is constructed,which takes sensor cost as objective finction and the defined testability indexes as constraint conditions.Due to NP-hard property of the model,a generic algorithm is designed to obtain the optimal solution.At last,a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform.The application results and comparison analysis show the proposed model and algorithm are effective and feasible.This approach can be used to select sensors for prognostics and health management of any system.
Optimization model for Green Vendor Managed Inventory under disruptions
Baruah, Swapnali
2015-01-01
Purpose: This dissertation reviews the literature in the field of Vehicle Routing Problem to analyze gaps in the literature of Green Vehicle Routing Problem and proposes a model in this field. This model bridges one such gap in literature to find optimal routes to a set of customers minimizing the total cost and taking carbon emission into consideration. There is no model in literature that caters to these objectives all at the same time. Methodology: Previous research has mostly focused o...
Optimal ordering policies for continuous review perishable inventory models.
Weiss, H J
1980-01-01
This paper extends the notions of perishable inventory models to the realm of continuous review inventory systems. The traditional perishable inventory costs of ordering, holding, shortage or penalty, disposal and revenue are incorporated into the continuous review framework. The type of policy that is optimal with respect to long run average expected cost is presented for both the backlogging and lost-sales models. In addition, for the lost-sales model the cost function is presented and analyzed.
Optimal schooling formations using a potential flow model
Tchieu, Andrew; Gazzola, Mattia; de Brauer, Alexia; Koumoutsakos, Petros
2012-11-01
A self-propelled, two-dimensional, potential flow model for agent-based swimmers is used to examine how fluid coupling affects schooling formation. The potential flow model accounts for fluid-mediated interactions between swimmers. The model is extended to include individual agent actions by means of modifying the circulation of each swimmer. A reinforcement algorithm is applied to allow the swimmers to learn how to school in specified lattice formations. Lastly, schooling lattice configurations are optimized by combining reinforcement learning and evolutionary optimization to minimize total control effort and energy expenditure.
Fuzzy Modelling of Knee Joint with Genetic Optimization
Directory of Open Access Journals (Sweden)
B. S. K. K. Ibrahim
2011-01-01
Full Text Available Modelling of joint properties of lower limbs in people with spinal cord injury is significantly challenging for researchers due to the complexity of the system. The objective of this study is to develop a knee joint model capable of relating electrical parameters to dynamic joint torque as well as knee angle for functional electrical stimulation application. The joint model consists of a segmental dynamic, time-invariant passive properties and uncertain time-variant active properties. The knee joint model structure comprising optimised equations of motion and fuzzy models to represent the passive viscoelasticity and active muscle properties is formulated. The model thus formulated is optimised using genetic optimization, and validated against experimental data. The developed model can be used for simulation of joint movements as well as for control development. The results show that the model developed gives an accurate dynamic characterisation of the knee joint.
Modeling to Optimize Hospital Evacuation Planning in EMS Systems.
Bish, Douglas R; Tarhini, Hussein; Amara, Roel; Zoraster, Richard; Bosson, Nichole; Gausche-Hill, Marianne
2017-01-01
To develop optimal hospital evacuation plans within a large urban EMS system using a novel evacuation planning model and a realistic hospital evacuation scenario, and to illustrate the ways in which a decision support model may be useful in evacuation planning. An optimization model was used to produce detailed evacuation plans given the number and type of patients in the evacuating hospital, resource levels (teams to move patients, vehicles, and beds at other hospitals), and evacuation rules. Optimal evacuation plans under various resource levels and rules were developed and high-level metrics were calculated, including evacuation duration and the utilization of resources. Using this model we were able to determine the limiting resources and demonstrate how strategically augmenting the resource levels can improve the performance of the evacuation plan. The model allowed the planner to test various evacuation conditions and resource levels to demonstrate the effect on performance of the evacuation plan. We present a hospital evacuation planning analysis for a hospital in a large urban EMS system using an optimization model. This model can be used by EMS administrators and medical directors to guide planning decisions and provide a better understanding of various resource allocation decisions and rules that govern a hospital evacuation.
Institute of Scientific and Technical Information of China (English)
Xu Zhang; En-min Feng
2004-01-01
This paper studies the two-dimensional layout optimization problem.An optimization model with performance constraints is presented.The layout problem is partitioned intofinite subproblems in terms of graph theory,in such a way of that each subproblem overcomes its on-o inature optimal variable.A minimax problem is constructed that is locally equivalent to each subproblem.By using this minimax problem,we present the optimality function for every subproblem and prove that the first order necessary optimality condition is satisfied at a point if and only if this point is a zero of optimality function.
Modeling urban air pollution with optimized hierarchical fuzzy inference system.
Tashayo, Behnam; Alimohammadi, Abbas
2016-10-01
Environmental exposure assessments (EEA) and epidemiological studies require urban air pollution models with appropriate spatial and temporal resolutions. Uncertain available data and inflexible models can limit air pollution modeling techniques, particularly in under developing countries. This paper develops a hierarchical fuzzy inference system (HFIS) to model air pollution under different land use, transportation, and meteorological conditions. To improve performance, the system treats the issue as a large-scale and high-dimensional problem and develops the proposed model using a three-step approach. In the first step, a geospatial information system (GIS) and probabilistic methods are used to preprocess the data. In the second step, a hierarchical structure is generated based on the problem. In the third step, the accuracy and complexity of the model are simultaneously optimized with a multiple objective particle swarm optimization (MOPSO) algorithm. We examine the capabilities of the proposed model for predicting daily and annual mean PM2.5 and NO2 and compare the accuracy of the results with representative models from existing literature. The benefits provided by the model features, including probabilistic preprocessing, multi-objective optimization, and hierarchical structure, are precisely evaluated by comparing five different consecutive models in terms of accuracy and complexity criteria. Fivefold cross validation is used to assess the performance of the generated models. The respective average RMSEs and coefficients of determination (R (2)) for the test datasets using proposed model are as follows: daily PM2.5 = (8.13, 0.78), annual mean PM2.5 = (4.96, 0.80), daily NO2 = (5.63, 0.79), and annual mean NO2 = (2.89, 0.83). The obtained results demonstrate that the developed hierarchical fuzzy inference system can be utilized for modeling air pollution in EEA and epidemiological studies.
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
Characterization, Modeling, and Optimization of Light-Emitting Diode System
DEFF Research Database (Denmark)
Thorseth, Anders
. It is shown that the droop in quantum efficiency can be approximated by a simple parabolic function. The investigated models of the spectral power distributions (SPD) from LEDs are the strictly empirical single and double Gaussian functions, and a semi empirical model using quasi Fermi levels and other basic...... solid state principles. The models are fitted to measured SPDs, using the free parameters. The result show a high correlation between the measured LED SPD and the fitted models. When comparing the chromaticity of the measured SPD with fitted models, the deviation is found to be larger than the lower...... limit of human color perception. A method has been developed to optimize multicolored cluster LED systems with respect to light quality, using multi objective optimization. The results are simulated SPDs similar to traditional light sources, and with high light quality. As part of this work...
A revised model of fluid transport optimization in Physarum polycephalum.
Bonifaci, Vincenzo
2017-02-01
Optimization of fluid transport in the slime mold Physarum polycephalum has been the subject of several modeling efforts in recent literature. Existing models assume that the tube adaptation mechanism in P. polycephalum's tubular network is controlled by the sheer amount of fluid flow through the tubes. We put forward the hypothesis that the controlling variable may instead be the flow's pressure gradient along the tube. We carry out the stability analysis of such a revised mathematical model for a parallel-edge network, proving that the revised model supports the global flow-optimizing behavior of the slime mold for a substantially wider class of response functions compared to previous models. Simulations also suggest that the same conclusion may be valid for arbitrary network topologies.
FUZZY OPTIMIZATION MODEL OF MAINTENANCE DESIGN FOR PRODUCT LEVEL REUSE
Institute of Scientific and Technical Information of China (English)
Feng Zhen; Xu Guohua
2004-01-01
Most used products must be maintained before they are reused.The modeling method for maintenance design of product level reuse based on quality function deployment is presented.A fuzzy linear optimization model is developed under financial uncertainty.Objective of the model is to maximize improvement rate of customer satisfaction level.Maintenance cost constrain is fuzzy.The algorithm for solution to the model is given.Its optimized results not only give attention to satisfaction degree of cost constraint,but also maximize objective value.An illustrative example involved water bump reuse is studied and the results show that the proposed model can effectively help maintenance planner determine the better design scheme.
Finite element model selection using Particle Swarm Optimization
Mthembu, Linda; Friswell, Michael I; Adhikari, Sondipon
2009-01-01
This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-based stochastic search algorithm inspired by the behaviour of biological entities in nature when they are foraging for resources. Each potentially correct model is represented as a particle that exhibits both individualistic and group behaviour. Each particle moves within the model search space looking for the best solution by updating the parameters values that define it. The most important step in the particle swarm algorithm is the method of representing models which should take into account the number, location and variables of parameters to be updated. One example structural system is used to show the applicability of PSO in finding an optimal FEM. An optimal model is defined as the model that has t...
Optimization models for flight test scheduling
Holian, Derreck
As threats around the world increase with nations developing new generations of warfare technology, the Unites States is keen on maintaining its position on top of the defense technology curve. This in return indicates that the U.S. military/government must research, develop, procure, and sustain new systems in the defense sector to safeguard this position. Currently, the Lockheed Martin F-35 Joint Strike Fighter (JSF) Lightning II is being developed, tested, and deployed to the U.S. military at Low Rate Initial Production (LRIP). The simultaneous act of testing and deployment is due to the contracted procurement process intended to provide a rapid Initial Operating Capability (IOC) release of the 5th Generation fighter. For this reason, many factors go into the determination of what is to be tested, in what order, and at which time due to the military requirements. A certain system or envelope of the aircraft must be assessed prior to releasing that capability into service. The objective of this praxis is to aide in the determination of what testing can be achieved on an aircraft at a point in time. Furthermore, it will define the optimum allocation of test points to aircraft and determine a prioritization of restrictions to be mitigated so that the test program can be best supported. The system described in this praxis has been deployed across the F-35 test program and testing sites. It has discovered hundreds of available test points for an aircraft to fly when it was thought none existed thus preventing an aircraft from being grounded. Additionally, it has saved hundreds of labor hours and greatly reduced the occurrence of test point reflight. Due to the proprietary nature of the JSF program, details regarding the actual test points, test plans, and all other program specific information have not been presented. Generic, representative data is used for example and proof-of-concept purposes. Apart from the data correlation algorithms, the optimization associated
Multiobjective muffler shape optimization with hybrid acoustics modeling.
Airaksinen, Tuomas; Heikkola, Erkki
2011-09-01
This paper considers the combined use of a hybrid numerical method for the modeling of acoustic mufflers and a genetic algorithm for multiobjective optimization. The hybrid numerical method provides accurate modeling of sound propagation in uniform waveguides with non-uniform obstructions. It is based on coupling a wave based modal solution in the uniform sections of the waveguide to a finite element solution in the non-uniform component. Finite element method provides flexible modeling of complicated geometries, varying material parameters, and boundary conditions, while the wave based solution leads to accurate treatment of non-reflecting boundaries and straightforward computation of the transmission loss (TL) of the muffler. The goal of optimization is to maximize TL at multiple frequency ranges simultaneously by adjusting chosen shape parameters of the muffler. This task is formulated as a multiobjective optimization problem with the objectives depending on the solution of the simulation model. NSGA-II genetic algorithm is used for solving the multiobjective optimization problem. Genetic algorithms can be easily combined with different simulation methods, and they are not sensitive to the smoothness properties of the objective functions. Numerical experiments demonstrate the accuracy and feasibility of the model-based optimization method in muffler design.
Optimal control of information epidemics modeled as Maki Thompson rumors
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.
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
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.
Optimization Model for Refinery Hydrogen Networks Part II
Directory of Open Access Journals (Sweden)
Enrique E. Tarifa
2016-10-01
Full Text Available In the first part of this work, a model of optimization was presented that minimizes the consumption of the hydrogen of a refinery. In this second part, the model will be augmented to take into account the length of the pipelines, the addition of purification units and the installation of new compressors, all features of industrial real networks. The model developed was implemented in the LINGO software environment. For data input and results output, an Excel spreadsheet was developed that interfaces with LINGO. The model is currently being used in YPFLuján de Cuyo refinery (Mendoza, Argentina
Model reduction for optimization of structural-acoustic coupling problems
DEFF Research Database (Denmark)
Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Brunskog, Jonas;
2016-01-01
, which becomes highly time consuming since many iterations may be required. The use of model reduction techniques to speed up the computations is studied in this work. The Component Mode Synthesis (CMS) method and the Multi-Model Reduction (MMR) method are adapted for problems with structure......Fully coupled structural-acoustic models of complex systems, such as those used in the hearing aid field, may have several hundreds of thousands of nodes. When there is a strong structure-acoustic interaction, performing optimization on one part requires the complete model to be taken into account...
Hybrid Modeling and Optimization of Yogurt Starter Culture Continuous Fermentation
Directory of Open Access Journals (Sweden)
Silviya Popova
2009-10-01
Full Text Available The present paper presents a hybrid model of yogurt starter mixed culture fermentation. The main nonlinearities within a classical structure of continuous process model are replaced by neural networks. The new hybrid model accounts for the dependence of the two microorganisms' kinetics from the on-line measured characteristics of the culture medium - pH. Then the model was used further for calculation of the optimal time profile of pH. The obtained results are with agreement with the experimental once.
Modeling, estimation and optimal filtration in signal processing
Najim, Mohamed
2010-01-01
The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the
Institute of Scientific and Technical Information of China (English)
Jongbin Im; Jungsun Park
2013-01-01
This paper focuses on a method to solve structural optimization problems using particle swarm optimization (PSO),surrogate models and Bayesian statistics.PSO is a random/stochastic search algorithm designed to find the global optimum.However,PSO needs many evaluations compared to gradient-based optimization.This means PSO increases the analysis costs of structural optimization.One of the methods to reduce computing costs in stochastic optimization is to use approximation techniques.In this work,surrogate models are used,including the response surface method (RSM) and Kriging.When surrogate models are used,there are some errors between exact values and approximated values.These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models.In this paper,Bayesian statistics is used to obtain more reliable results.To verify and confirm the efficiency of the proposed method using surrogate models and Bayesian statistics for stochastic structural optimization,two numerical examples are optimized,and the optimization of a hub sleeve is demonstrated as a practical problem.
Optimization routine for identification of model parameters in soil plasticity
Mattsson, Hans; Klisinski, Marek; Axelsson, Kennet
2001-04-01
The paper presents an optimization routine especially developed for the identification of model parameters in soil plasticity on the basis of different soil tests. Main focus is put on the mathematical aspects and the experience from application of this optimization routine. Mathematically, for the optimization, an objective function and a search strategy are needed. Some alternative expressions for the objective function are formulated. They capture the overall soil behaviour and can be used in a simultaneous optimization against several laboratory tests. Two different search strategies, Rosenbrock's method and the Simplex method, both belonging to the category of direct search methods, are utilized in the routine. Direct search methods have generally proved to be reliable and their relative simplicity make them quite easy to program into workable codes. The Rosenbrock and simplex methods are modified to make the search strategies as efficient and user-friendly as possible for the type of optimization problem addressed here. Since these search strategies are of a heuristic nature, which makes it difficult (or even impossible) to analyse their performance in a theoretical way, representative optimization examples against both simulated experimental results as well as performed triaxial tests are presented to show the efficiency of the optimization routine. From these examples, it has been concluded that the optimization routine is able to locate a minimum with a good accuracy, fast enough to be a very useful tool for identification of model parameters in soil plasticity.
A New Approach for Parameter Optimization in Land Surface Model
Institute of Scientific and Technical Information of China (English)
LI Hongqi; GUO Weidong; SUN Guodong; ZHANG Yaocun; FU Congbin
2011-01-01
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation (CNOP) in a land surface model (LSM) using long-term enhanced field observations at Tongyn station in Jilin Province,China,combined with a sophisticated LSM (common land model,CoLM).Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project (CEOP) that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments:First,a single-parameter optimization was performed,while the second and third experiments performed triple- and six-parameter optinizations,respectively.Notable improvements in simulating sensible heat flux (SH),latent heat flux (LH),soil temperature (TS),and moisture (MS) at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
Modeling and Multi-objective Optimization of Refinery Hydrogen Network
Institute of Scientific and Technical Information of China (English)
焦云强; 苏宏业; 廖祖维; 侯卫锋
2011-01-01
The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming （MINLP）. A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.
Characterization, Modeling, and Optimization of Light-Emitting Diode Systems
DEFF Research Database (Denmark)
Thorseth, Anders
This thesis explores, characterization, modeling, and optimization of light-emitting diodes (LED) for general illumination. An automated setup has been developed for spectral radiometric characterization of LED components with precise control of the settings of forward current and operating...... comparing the chromaticity of the measured SPD with tted models, the deviation is found to be larger than the lower limit of human color perception. A method has been developed to optimize multicolored cluster LED systems with respect to light quality, using multi objective optimization. The results...... temperature. The automated setup has been used to characterize commercial LED components with respect to multiple settings. It is shown that the droop in quantum efficiency can be approximated by a simple parabolic function. The investigated models of the spectral power distributions (SPD) from LEDs...
Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers
Rogers, Adam
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 automa...
Model-based control of fuel cells (2): Optimal efficiency
Energy Technology Data Exchange (ETDEWEB)
Golbert, Joshua; Lewin, Daniel R. [PSE Research Group, Wolfson Department of Chemical Engineering, Technion IIT, Haifa 32000 (Israel)
2007-11-08
A dynamic PEM fuel cell model has been developed, taking into account spatial dependencies of voltage, current, material flows, and temperatures. The voltage, current, and therefore, the efficiency are dependent on the temperature and other variables, which can be optimized on the fly to achieve optimal efficiency. In this paper, we demonstrate that a model predictive controller, relying on a reduced-order approximation of the dynamic PEM fuel cell model can satisfy setpoint changes in the power demand, while at the same time, minimize fuel consumption to maximize the efficiency. The main conclusion of the paper is that by appropriate formulation of the objective function, reliable optimization of the performance of a PEM fuel cell can be performed in which the main tunable parameter is the prediction and control horizons, V and U, respectively. We have demonstrated that increased fuel efficiency can be obtained at the expense of slower responses, by increasing the values of these parameters. (author)
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
Modeling the crop transpiration using an optimality-based approach
Institute of Scientific and Technical Information of China (English)
Stanislaus; J.Schymanski; Murugesu; Sivapalan
2008-01-01
Evapotranspiration constitutes more than 80% of the long-term water balance in Northern China.In this area,crop transpiration due to large areas of agriculture and irrigation is responsible for the majority of evapotranspiration.A model for crop transpiration is therefore essential for estimating the agricultural water consumption and understanding its feedback to the environment.However,most existing hydrological models usually calculate transpiration by relying on parameter calibration against local observations,and do not take into account crop feedback to the ambient environment.This study presents an optimality-based ecohydrology model that couples an ecological hypothesis,the photosynthetic process,stomatal movement,water balance,root water uptake and crop senescence,with the aim of predicting crop characteristics,CO2 assimilation and water balance based only on given meteorological data.Field experiments were conducted in the Weishan Irrigation District of Northern China to evaluate performance of the model.Agreement between simulation and measurement was achieved for CO2 assimilation,evapotranspiration and soil moisture content.The vegetation optimality was proven valid for crops and the model was applicable for both C3 and C4 plants.Due to the simple scheme of the optimality-based approach as well as its capability for modeling dynamic interactions between crops and the water cycle without prior vegetation information,this methodology is potentially useful to couple with the distributed hydrological model for application at the watershed scale.
Integer programming model for optimizing bus timetable using genetic algorithm
Wihartiko, F. D.; Buono, A.; Silalahi, B. P.
2017-01-01
Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.
Optimality principles for model-based prediction of human gait.
Ackermann, Marko; van den Bogert, Antonie J
2010-04-19
Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient's gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait.
Space engineering modeling and optimization with case studies
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 ...
Optimization of Operations Resources via Discrete Event Simulation Modeling
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.
An uncertain multidisciplinary design optimization method using interval convex models
Li, Fangyi; Luo, Zhen; Sun, Guangyong; Zhang, Nong
2013-06-01
This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss-Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.
HYPERSTATIC STRUCTURE MAPPING MODEL BUILDING AND OPTIMIZING DESIGN
Institute of Scientific and Technical Information of China (English)
XU Gening; GAO Youshan; ZHANG Xueliang; YANG Ruigang
2007-01-01
Hyperstatic structure plane model being built by structural mechanics is studied. Space model precisely reflected in real stress of the structure is built by finite element method (FEM) analysis commerce software. Mapping model of complex structure system is set up, with convenient calculation just as in plane model and comprehensive information as in space model. Plane model and space model are calculated under the same working condition. Plane model modular construction inner force is considered as input data; Space model modular construction inner force is considered as output data. Thus specimen is built on input data and output data. Character and affiliation are extracted through training specimen, with the employment of nonlinear mapping capability of the artificial neural network. Mapping model with interpolation and extrapolation is gained, laying the foundation for optimum design. The steel structure of high-layer parking system (SSHLPS) is calculated as an instance. A three-layer back-propagation (BP) net including one hidden layer is constructed with nine input nodes and eight output nodes for a five-layer SSHLPS. The three-layer structure optimization result through the mapping model interpolation contrasts with integrity re-analysis, and seven layers structure through the mapping model extrapolation contrasts with integrity re-analysis. Any layer SSHLPS among 1～8 can be calculated with much accuracy. Amount of calculation can also be reduced if it is applied into the same topological structure, with reduced distortion and assured precision.
A Multidisciplinary Design Optimization Model for AUV Synthetic Conceptual Design
Institute of Scientific and Technical Information of China (English)
BU Guang-zhi; ZHANG Yu-wen
2006-01-01
Autonomous undersea vehicle (AUV) is a typical complex engineering system. This paper studies the disciplines and coupled variables in AUV design with multidisciplinary design optimization (M DO) methods. The framework of AUV synthetic conceptual design is described first, and then a model with collaborative optimization is studied. At last,an example is given to verify the validity and efficiency of MDO in AUV synthetic conceptual design.
CADLIVE optimizer: web-based parameter estimation for dynamic models
Directory of Open Access Journals (Sweden)
Inoue Kentaro
2012-08-01
Full Text Available Abstract Computer simulation has been an important technique to capture the dynamics of biochemical networks. In most networks, however, few kinetic parameters have been measured in vivo because of experimental complexity. We develop a kinetic parameter estimation system, named the CADLIVE Optimizer, which comprises genetic algorithms-based solvers with a graphical user interface. This optimizer is integrated into the CADLIVE Dynamic Simulator to attain efficient simulation for dynamic models.
EXPERIENCE WITH SYNCHRONOUS GENERATOR MODEL USING PARTICLE SWARM OPTIMIZATION TECHNIQUE
N.RATHIKA; Dr.A.Senthil kumar; A.ANUSUYA
2014-01-01
This paper intends to the modeling of polyphase synchronous generator and minimization of power losses using Particle swarm optimization (PSO) technique with a constriction factor. Usage of Polyphase synchronous generator mainly leads to the total power circulation in the system which can be distributed in all phases. Another advantage of polyphase system is the fault at one winding does not lead to the system shutdown. The Process optimization is the chastisement of adjusting a process so as...
TLM modeling and system identification of optimized antenna structures
Directory of Open Access Journals (Sweden)
N. Fichtner
2008-05-01
Full Text Available The transmission line matrix (TLM method in conjunction with the genetic algorithm (GA is presented for the bandwidth optimization of a low profile patch antenna. The optimization routine is supplemented by a system identification (SI procedure. By the SI the model parameters of the structure are estimated which is used for a reduction of the total TLM simulation time. The SI utilizes a new stability criterion of the physical poles for the parameter extraction.
Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.
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.
Optimal control design that accounts for model mismatch errors
Energy Technology Data Exchange (ETDEWEB)
Kim, T.J. [Sandia National Labs., Albuquerque, NM (United States); Hull, D.G. [Texas Univ., Austin, TX (United States). Dept. of Aerospace Engineering and Engineering Mechanics
1995-02-01
A new technique is presented in this paper that reduces the complexity of state differential equations while accounting for modeling assumptions. The mismatch controls are defined as the differences between the model equations and the true state equations. The performance index of the optimal control problem is formulated with a set of tuning parameters that are user-selected to tune the control solution in order to achieve the best results. Computer simulations demonstrate that the tuned control law outperforms the untuned controller and produces results that are comparable to a numerically-determined, piecewise-linear optimal controller.
Multiscale modeling and topology optimization of poroelastic actuators
DEFF Research Database (Denmark)
Andreasen, Casper Schousboe; Sigmund, Ole
2012-01-01
This paper presents a method for design of optimized poroelastic materials which under internal pressurization turn into actuators for application in, for example, linear motors. The actuators are modeled in a two-scale fluid–structure interaction approach. The fluid saturated material microstruc......This paper presents a method for design of optimized poroelastic materials which under internal pressurization turn into actuators for application in, for example, linear motors. The actuators are modeled in a two-scale fluid–structure interaction approach. The fluid saturated material...
Dynamic Modeling, Optimization, and Advanced Control for Large Scale Biorefineries
DEFF Research Database (Denmark)
Prunescu, Remus Mihail
with building a plantwide model-based optimization layer, which searches for optimal values regarding the pretreatment temperature, enzyme dosage in liquefaction, and yeast seed in fermentation such that profit is maximized [7]. When biomass is pretreated, by-products are also created that affect the downstream...... processes acting as inhibitors in enzymatic hydrolysis and fermentation. Therefore, the biorefinery is treated in an integrated manner capturing the trade-offs between the conversion steps. Sensitivity and uncertainty analysis is also performed in order to identify the modeling bottlenecks and which...
On the optimal control problem for two regions’ macroeconomic model
Directory of Open Access Journals (Sweden)
Surkov Platon G.
2015-12-01
Full Text Available In this paper we consider a model of joint economic growth of two regions. This model bases on the classical Kobb-Douglas function and is described by a nonlinear system of differential equations. The interaction between regions is carried out by changing the balance of trade. The optimal control problem for this system is posed and the Pontryagin maximum principle is used for analysis the problem. The maximized functional represents the global welfare of regions. The numeric solution of the optimal control problem for particular regions is found. The used parameters was obtained from the basic scenario of the MERGE
The optimal inventory policy for EPQ model under trade credit
Chung, Kun-Jen
2010-09-01
Huang and Huang [(2008), 'Optimal Inventory Replenishment Policy for the EPQ Model Under Trade Credit without Derivatives International Journal of Systems Science, 39, 539-546] use the algebraic method to determine the optimal inventory replenishment policy for the retailer in the extended model under trade credit. However, the algebraic method has its limit of application such that validities of proofs of Theorems 1-4 in Huang and Huang (2008) are questionable. The main purpose of this article is not only to indicate shortcomings but also to present the accurate proofs for Huang and Huang (2008).
Optimal policies for a finite-horizon batching inventory model
Al-Khamis, Talal M.; Benkherouf, Lakdere; Omar, Mohamed
2014-10-01
This paper is concerned with finding an optimal inventory policy for the integrated replenishment-production batching model of Omar and Smith (2002). Here, a company produces a single finished product which requires a single raw material and the objective is to minimise the total inventory costs over a finite planning horizon. Earlier work in the literature considered models with linear demand rate function of the finished product. This work proposes a general methodology for finding an optimal inventory policy for general demand rate functions. The proposed methodology is adapted from the recent work of Benkherouf and Gilding (2009).
Spectral optimization and uncertainty quantification in combustion modeling
Sheen, David Allan
Reliable simulations of reacting flow systems require a well-characterized, detailed chemical model as a foundation. Accuracy of such a model can be assured, in principle, by a multi-parameter optimization against a set of experimental data. However, the inherent uncertainties in the rate evaluations and experimental data leave a model still characterized by some finite kinetic rate parameter space. Without a careful analysis of how this uncertainty space propagates into the model's predictions, those predictions can at best be trusted only qualitatively. In this work, the Method of Uncertainty Minimization using Polynomial Chaos Expansions is proposed to quantify these uncertainties. In this method, the uncertainty in the rate parameters of the as-compiled model is quantified. Then, the model is subjected to a rigorous multi-parameter optimization, as well as a consistency-screening process. Lastly, the uncertainty of the optimized model is calculated using an inverse spectral optimization technique, and then propagated into a range of simulation conditions. An as-compiled, detailed H2/CO/C1-C4 kinetic model is combined with a set of ethylene combustion data to serve as an example. The idea that the hydrocarbon oxidation model should be understood and developed in a hierarchical fashion has been a major driving force in kinetics research for decades. How this hierarchical strategy works at a quantitative level, however, has never been addressed. In this work, we use ethylene and propane combustion as examples and explore the question of hierarchical model development quantitatively. The Method of Uncertainty Minimization using Polynomial Chaos Expansions is utilized to quantify the amount of information that a particular combustion experiment, and thereby each data set, contributes to the model. This knowledge is applied to explore the relationships among the combustion chemistry of hydrogen/carbon monoxide, ethylene, and larger alkanes. Frequently, new data will
Optimization methods and silicon solar cell numerical models
Girardini, K.; Jacobsen, S. E.
1986-01-01
An optimization algorithm for use with numerical silicon solar cell models was developed. By coupling an optimization algorithm with a solar cell model, it is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junction depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm was developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAP1D). SCAP1D uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the performance of a solar cell. A major obstacle is that the numerical methods used in SCAP1D require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the values associated with the maximum efficiency. This problem was alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution.
Shell model of optimal passive-scalar mixing
Miles, Christopher; Doering, Charles
2015-11-01
Optimal mixing is significant to process engineering within industries such as food, chemical, pharmaceutical, and petrochemical. An important question in this field is ``How should one stir to create a homogeneous mixture while being energetically efficient?'' To answer this question, we consider an initially unmixed scalar field representing some concentration within a fluid on a periodic domain. This passive-scalar field is advected by the velocity field, our control variable, constrained by a physical quantity such as energy or enstrophy. We consider two objectives: local-in-time (LIT) optimization (what will maximize the mixing rate now?) and global-in-time (GIT) optimization (what will maximize mixing at the end time?). Throughout this work we use the H-1 mix-norm to measure mixing. To gain a better understanding, we provide a simplified mixing model by using a shell model of passive-scalar advection. LIT optimization in this shell model gives perfect mixing in finite time for the energy-constrained case and exponential decay to the perfect-mixed state for the enstrophy-constrained case. Although we only enforce that the time-average energy (or enstrophy) equals a chosen value in GIT optimization, interestingly, the optimal control keeps this value constant over time.
Optimization of Component Based Software Engineering Model Using Neural Network
Directory of Open Access Journals (Sweden)
Gaurav Kumar
2014-10-01
Full Text Available The goal of Component Based Software Engineering (CBSE is to deliver high quality, more reliable and more maintainable software systems in a shorter time and within limited budget by reusing and combining existing quality components. A high quality system can be achieved by using quality components, framework and integration process that plays a significant role. So, techniques and methods used for quality assurance and assessment of a component based system is different from those of the traditional software engineering methodology. In this paper, we are presenting a model for optimizing Chidamber and Kemerer (CK metric values of component-based software. A deep analysis of a series of CK metrics of the software components design patterns is done and metric values are drawn from them. By using unsupervised neural network- Self Organizing Map, we have proposed a model that provides an optimized model for Software Component engineering model based on reusability that depends on CK metric values. Average, standard deviated and optimized values for the CK metric are compared and evaluated to show the optimized reusability of component based model.
Turbulence Model Discovery with Data-Driven Learning and Optimization
King, Ryan; Hamlington, Peter
2016-11-01
Data-driven techniques have emerged as a useful tool for model development in applications where first-principles approaches are intractable. In this talk, data-driven multi-task learning techniques are used to discover flow-specific optimal turbulence closure models. We use the recently introduced autonomic closure technique to pose an online supervised learning problem created by test filtering turbulent flows in the self-similar inertial range. The autonomic closure is modified to solve the learning problem for all stress components simultaneously with multi-task learning techniques. The closure is further augmented with a feature extraction step that learns a set of orthogonal modes that are optimal at predicting the turbulent stresses. We demonstrate that these modes can be severely truncated to enable drastic reductions in computational costs without compromising the model accuracy. Furthermore, we discuss the potential universality of the extracted features and implications for reduced order modeling of other turbulent flows.
A Multiobjective Optimization Model in Automotive Supply Chain Networks
Directory of Open Access Journals (Sweden)
Abdolhossein Sadrnia
2013-01-01
Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
Stepped spillway optimization through numerical and physical modeling
Directory of Open Access Journals (Sweden)
Hamed Sarkardeh, Morteza Marosi, Raza Roshan
2015-01-01
Full Text Available The spillway is among the most important structures of a dam. It is importance for the spillway to be designed properly and passes flood flow safely with more energy dissipation. The zone which ogee spillway crest and stepped chute profile are joined with each other is important in design view. In the present study, a physical model as well as a numerical model was employed on a case study of stepped spillway to modify the transitional zone and improve flow pattern over the spillway. Many alternatives were examined and optimized. Finally, the performance of the selected alternative was checked for different flow conditions, air entrainment and energy dissipation. To simulate the turbulence phenomenon, RNG model and for free surface VOF model was selected in the numerical model. Results of the numerical and physical models were compared and good agreement concluded in flow conditions and energy dissipation.
Model simplification and optimization of a passive wind turbine generator
Sareni, Bruno; Abdelli, Abdenour; Roboam, Xavier; Tran, Duc-Hoan
2009-01-01
International audience; In this paper, the design of a "low cost full passive structure" of wind turbine system without active electronic part (power and control) is investigated. The efficiency of such device can be obtained only if the design parameters are mutually adapted through an optimization design approach. For this purpose, sizing and simulating models are developed to characterize the behavior and the efficiency of the wind turbine system. A model simplification approach is present...
A Computationally Efficient Aggregation Optimization Strategy of Model Predictive Control
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Model Predictive Control (MPC) is a popular technique and has been successfully used in various industrial applications. However, the big drawback of MPC involved in the formidable on-line computational effort limits its applicability to relatively slow and/or small processes with a moderate number of inputs. This paper develops an aggregation optimization strategy for MPC that can improve the computational efficiency of MPC. For the regulation problem, an input decaying aggregation optimization algorithm is presented by aggregating all the original optimized variables on control horizon with the decaying sequence in respect of the current control action.
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.
Pumping Optimization Model for Pump and Treat Systems - 15091
Energy Technology Data Exchange (ETDEWEB)
Baker, S.; Ivarson, Kristine A.; Karanovic, M.; Miller, Charles W.; Tonkin, M.
2015-01-15
Pump and Treat systems are being utilized to remediate contaminated groundwater in the Hanford 100 Areas adjacent to the Columbia River in Eastern Washington. Design of the systems was supported by a three-dimensional (3D) fate and transport model. This model provided sophisticated simulation capabilities but requires many hours to calculate results for each simulation considered. Many simulations are required to optimize system performance, so a two-dimensional (2D) model was created to reduce run time. The 2D model was developed as a equivalent-property version of the 3D model that derives boundary conditions and aquifer properties from the 3D model. It produces predictions that are very close to the 3D model predictions, allowing it to be used for comparative remedy analyses. Any potential system modifications identified by using the 2D version are verified for use by running the 3D model to confirm performance. The 2D model was incorporated into a comprehensive analysis system (the Pumping Optimization Model, POM) to simplify analysis of multiple simulations. It allows rapid turnaround by utilizing a graphical user interface that: 1 allows operators to create hypothetical scenarios for system operation, 2 feeds the input to the 2D fate and transport model, and 3 displays the scenario results to evaluate performance improvement. All of the above is accomplished within the user interface. Complex analyses can be completed within a few hours and multiple simulations can be compared side-by-side. The POM utilizes standard office computing equipment and established groundwater modeling software.
Optimization methods and silicon solar cell numerical models
Girardini, K.
1986-01-01
The goal of this project is the development of an optimization algorithm for use with a solar cell model. It is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junctions depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm has been developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAPID). SCAPID uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the operation of a solar cell. A major obstacle is that the numerical methods used in SCAPID require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the value associated with the maximum efficiency. This problem has been alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution. Adapting SCAPID so that it could be called iteratively by the optimization code provided another means of reducing the cpu time required to complete an optimization. Instead of calculating the entire I-V curve, as is usually done in SCAPID, only the efficiency is calculated (maximum power voltage and current) and the solution from previous calculations is used to initiate the next solution.
Cuckoo Search Optimization for Reduction of a Greenhouse Climate Model
Directory of Open Access Journals (Sweden)
Hasni Abdelhafid
2016-07-01
Full Text Available Greenhouse climate and crop models and specially reduced models are necessary for bettering environmental management and control ability. In this paper, we present a new metaheuristic method, called Cuckoo Search (CS algorithm, established on the life of a bird family for selecting the parameters of a reduced model which optimizes their choice by minimizing a cost function. The reduced model was already developed for control purposes and published in the literature. The proposed models target at simulating and predicting the greenhouse environment. [?]. This study focuses on the dynamical behaviors of the inside air temperature and pressure using ventilation. Some experimental results are used for model validation, the greenhouse being automated with actuators and sensors connected to a greenhouse control system on the cuckoo search methods to determine the best set of parameters allowing for the convergence of a criteria based on the difference between calculated and observed state variables (inside air temperature and water vapour pressure content. The results shown that the tested Cuckoo Search algorithm allows for a faster convergence towards the optimal solution than classical optimization methods.
Velocity model optimization for surface microseismic monitoring via amplitude stacking
Jiang, Haiyu; Wang, Zhongren; Zeng, Xiaoxian; Lü, Hao; Zhou, Xiaohua; Chen, Zubin
2016-12-01
A usable velocity model in microseismic projects plays a crucial role in achieving statistically reliable microseismic event locations. Existing methods for velocity model optimization rely mainly on picking arrival times at individual receivers. However, for microseismic monitoring with surface stations, seismograms of perforation shots have such low signal-to-noise ratios (S/N) that they do not yield sufficiently reliable picks. In this study, we develop a framework for constructing a 1-D flat-layered a priori velocity model using a non-linear optimization technique based on amplitude stacking. The energy focusing of the perforation shot is improved thanks to very fast simulated annealing (VFSA), and the accuracies of shot relocations are used to evaluate whether the resultant velocity model can be used for microseismic event location. Our method also includes a conventional migration-based location technique that utilizes successive grid subdivisions to improve computational efficiency and source location accuracy. Because unreasonable a priori velocity model information and interference due to additive noise are the major contributors to inaccuracies in perforation shot locations, we use velocity model optimization as a compensation scheme. Using synthetic tests, we show that accurate locations of perforation shots can be recovered to within 2 m, even with pre-stack S/N ratios as low as 0.1 at individual receivers. By applying the technique to a coal-bed gas reservoir in Western China, we demonstrate that perforation shot location can be recovered to within the tolerance of the well tip location.
Batch Process Modelling and Optimal Control Based on Neural Network Models
Institute of Scientific and Technical Information of China (English)
Jie Zhang
2005-01-01
This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.
Electrochemical model based charge optimization for lithium-ion batteries
Pramanik, Sourav; Anwar, Sohel
2016-05-01
In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.
An improved optimal elemental method for updating finite element models
Institute of Scientific and Technical Information of China (English)
Duan Zhongdong(段忠东); Spencer B.F.; Yan Guirong(闫桂荣); Ou Jinping(欧进萍)
2004-01-01
The optimal matrix method and optimal elemental method used to update finite element models may not provide accurate results. This situation occurs when the test modal model is incomplete, as is often the case in practice. An improved optimal elemental method is presented that defines a new objective function, and as a byproduct, circumvents the need for mass normalized modal shapes, which are also not readily available in practice. To solve the group of nonlinear equations created by the improved optimal method, the Lagrange multiplier method and Matlab function fmincon are employed. To deal with actual complex structures,the float-encoding genetic algorithm (FGA) is introduced to enhance the capability of the improved method. Two examples, a 7-degree of freedom (DOF) mass-spring system and a 53-DOF planar frame, respectively, are updated using the improved method.Thc example results demonstrate the advantages of the improved method over existing optimal methods, and show that the genetic algorithm is an effective way to update the models used for actual complex structures.
Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.
2015-01-01
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
Optimal control of a dengue epidemic model with vaccination
Rodrigues, Helena Sofia; Torres, Delfim F M
2011-01-01
We present a SIR+ASI epidemic model to describe the interaction between human and dengue fever mosquito populations. A control strategy in the form of vaccination, to decrease the number of infected individuals, is used. An optimal control approach is applied in order to find the best way to fight the disease.
Discover for Yourself: An Optimal Control Model in Insect Colonies
Winkel, Brian
2013-01-01
We describe the enlightening path of self-discovery afforded to the teacher of undergraduate mathematics. This is demonstrated as we find and develop background material on an application of optimal control theory to model the evolutionary strategy of an insect colony to produce the maximum number of queen or reproducer insects in the colony at…
Real-Time Optimization for Economic Model Predictive Control
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Edlund, Kristian; Frison, Gianluca
2012-01-01
In this paper, we develop an efficient homogeneous and self-dual interior-point method for the linear programs arising in economic model predictive control. To exploit structure in the optimization problems, the algorithm employs a highly specialized Riccati iteration procedure. Simulations show...
Optimal Tax Reduction by Depreciation : A Stochastic Model
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 st
The Optimal Portfolio Selection Model under g-Expectation
Directory of Open Access Journals (Sweden)
Li Li
2014-01-01
complicated and sophisticated, the optimal solution turns out to be surprisingly simple, the payoff of a portfolio of two binary claims. Also I give the economic meaning of my model and the comparison with that one in the work of Jin and Zhou, 2008.
Optimal Tax Reduction by Depreciation : A Stochastic Model
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
Stochastic Modelling and Optimization of Complex Infrastructure Systems
DEFF Research Database (Denmark)
Thoft-Christensen, Palle
In this paper it is shown that recent progress in stochastic modelling and optimization in combination with advanced computer systems has now made it possible to improve the design and the maintenance strategies for infrastructure systems. The paper concentrates on highway networks and single lar...
Discover for Yourself: An Optimal Control Model in Insect Colonies
Winkel, Brian
2013-01-01
We describe the enlightening path of self-discovery afforded to the teacher of undergraduate mathematics. This is demonstrated as we find and develop background material on an application of optimal control theory to model the evolutionary strategy of an insect colony to produce the maximum number of queen or reproducer insects in the colony at…
Analytical models integrated with satellite images for optimized pest management
The global field protection (GFP) was developed to protect and optimize pest management resources integrating satellite images for precise field demarcation with physical models of controlled release devices of pesticides to protect large fields. The GFP was implemented using a graphical user interf...
Water-resources optimization model for Santa Barbara, California
Nishikawa, T.
1998-01-01
A simulation-optimization model has been developed for the optimal management of the city of Santa Barbara's water resources during a drought. The model, which links groundwater simulation with linear programming, has a planning horizon of 5 years. The objective is to minimize the cost of water supply subject to: water demand constraints, hydraulic head constraints to control seawater intrusion, and water capacity constraints. The decision variables are montly water deliveries from surface water and groundwater. The state variables are hydraulic heads. The drought of 1947-51 is the city's worst drought on record, and simulated surface-water supplies for this period were used as a basis for testing optimal management of current water resources under drought conditions. The simulation-optimization model was applied using three reservoir operation rules. In addition, the model's sensitivity to demand, carry over [the storage of water in one year for use in the later year(s)], head constraints, and capacity constraints was tested.
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...
Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU
Directory of Open Access Journals (Sweden)
Yong Xia
2015-01-01
Full Text Available Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation and the other is the diffusion term of the monodomain model (partial differential equation. Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations.
Gong, Wei; Duan, Qingyun; Li, Jianduo; Wang, Chen; Di, Zhenhua; Ye, Aizhong; Miao, Chiyuan; Dai, Yongjiu
2016-03-01
Parameter specification is an important source of uncertainty in large, complex geophysical models. These models generally have multiple model outputs that require multiobjective optimization algorithms. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this paper, a multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) algorithm is introduced that aims to reduce computational cost while maintaining optimization effectiveness. Geophysical dynamic models usually have a prior parameterization scheme derived from the physical processes involved, and our goal is to improve all of the objectives by parameter calibration. In this study, we developed a method for directing the search processes toward the region that can improve all of the objectives simultaneously. We tested the MO-ASMO algorithm against NSGA-II and SUMO with 13 test functions and a land surface model - the Common Land Model (CoLM). The results demonstrated the effectiveness and efficiency of MO-ASMO.
Modelling, Optimization and Optimal Control of Small Scale Stirred Tank Bioreactors
Directory of Open Access Journals (Sweden)
Mitko Petrov
2004-10-01
Full Text Available Models of the mass-transfer in a stirred tank bioreactor depending on general indexes of the processes of aeration and mixing in concrete simplifications of the hydrodynamic structure of the flows are developed. The offered combined model after parameters identification is used for optimization of the parameters of the apparatus construction. The optimization problem is solved by using of the fuzzy sets theory and in this way the unspecified as a result of the model simplification are read. In conclusion an optimal control of a fed-batch fermentation process of E. coli is completed by using Neuro-Dynamic programming. The received results after optimization show a considerable improvement of the mass-transfer indexes and the quantity indexes at the end of the process.
Optimal reinsurance/investment problems for general insurance models
Liu, Yuping; 10.1214/08-AAP582
2009-01-01
In this paper the utility optimization problem for a general insurance model is studied. The reserve process of the insurance company is described by a stochastic differential equation driven by a Brownian motion and a Poisson random measure, representing the randomness from the financial market and the insurance claims, respectively. The random safety loading and stochastic interest rates are allowed in the model so that the reserve process is non-Markovian in general. The insurance company can manage the reserves through both portfolios of the investment and a reinsurance policy to optimize a certain utility function, defined in a generic way. The main feature of the problem lies in the intrinsic constraint on the part of reinsurance policy, which is only proportional to the claim-size instead of the current level of reserve, and hence it is quite different from the optimal investment/consumption problem with constraints in finance. Necessary and sufficient conditions for both well posedness and solvability...
EXPERIENCE WITH SYNCHRONOUS GENERATOR MODEL USING PARTICLE SWARM OPTIMIZATION TECHNIQUE
Directory of Open Access Journals (Sweden)
N.RATHIKA
2014-07-01
Full Text Available This paper intends to the modeling of polyphase synchronous generator and minimization of power losses using Particle swarm optimization (PSO technique with a constriction factor. Usage of Polyphase synchronous generator mainly leads to the total power circulation in the system which can be distributed in all phases. Another advantage of polyphase system is the fault at one winding does not lead to the system shutdown. The Process optimization is the chastisement of adjusting a process so as to optimize some stipulated set of parameters without violating some constraint. Accurate value can be extracted using PSO and it can be reformulated. Modeling and simulation of the machine is executed. MATLAB/Simulink has been cast-off to implement and validate the result.
Optimization model for rotor blades of horizontal axis wind turbines
Institute of Scientific and Technical Information of China (English)
LIU Xiong; CHEN Yan; YE Zhiquan
2007-01-01
This paper presents an optimization model for rotor blades of horizontal axis wind turbines. The model refers to the wind speed distribution function on the specific wind site, with an objective to satisfy the maximum annual energy output. To speed up the search process and guarantee a global optimal result, the extended compact genetic algorithm (ECGA) is used to carry out the search process.Compared with the simple genetic algorithm, ECGA runs much faster and can get more accurate results with a much smaller population size and fewer function evaluations. Using the developed optimization program, blades of a 1.3 MW stall-regulated wind turbine are designed. Compared with the existing blades, the designed blades have obviously better aerodynamic performance.
Modeling of biological intelligence for SCM system optimization.
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.
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.
Optimization of Regression Models of Experimental Data Using Confirmation Points
Ulbrich, N.
2010-01-01
A new search metric is discussed that may be used to better assess the predictive capability of different math term combinations during the optimization of a regression model of experimental data. The new search metric can be determined for each tested math term combination if the given experimental data set is split into two subsets. The first subset consists of data points that are only used to determine the coefficients of the regression model. The second subset consists of confirmation points that are exclusively used to test the regression model. The new search metric value is assigned after comparing two values that describe the quality of the fit of each subset. The first value is the standard deviation of the PRESS residuals of the data points. The second value is the standard deviation of the response residuals of the confirmation points. The greater of the two values is used as the new search metric value. This choice guarantees that both standard deviations are always less or equal to the value that is used during the optimization. Experimental data from the calibration of a wind tunnel strain-gage balance is used to illustrate the application of the new search metric. The new search metric ultimately generates an optimized regression model that was already tested at regression model independent confirmation points before it is ever used to predict an unknown response from a set of regressors.
Optimized volume models of earthquake-triggered landslides.
Xu, Chong; Xu, Xiwei; Shen, Lingling; Yao, Qi; Tan, Xibin; Kang, Wenjun; Ma, Siyuan; Wu, Xiyan; Cai, Juntao; Gao, Mingxing; Li, Kang
2016-07-12
In this study, we proposed three optimized models for calculating the total volume of landslides triggered by the 2008 Wenchuan, China Mw 7.9 earthquake. First, we calculated the volume of each deposit of 1,415 landslides triggered by the quake based on pre- and post-quake DEMs in 20 m resolution. The samples were used to fit the conventional landslide "volume-area" power law relationship and the 3 optimized models we proposed, respectively. Two data fitting methods, i.e. log-transformed-based linear and original data-based nonlinear least square, were employed to the 4 models. Results show that original data-based nonlinear least square combining with an optimized model considering length, width, height, lithology, slope, peak ground acceleration, and slope aspect shows the best performance. This model was subsequently applied to the database of landslides triggered by the quake except for two largest ones with known volumes. It indicates that the total volume of the 196,007 landslides is about 1.2 × 10(10) m(3) in deposit materials and 1 × 10(10) m(3) in source areas, respectively. The result from the relationship of quake magnitude and entire landslide volume related to individual earthquake is much less than that from this study, which reminds us the necessity to update the power-law relationship.
$T$-optimal designs for discrimination between two polynomial models
Dette, Holger; Shpilev, Petr; 10.1214/11-AOS956
2012-01-01
This paper is devoted to the explicit construction of optimal designs for discrimination between two polynomial regression models of degree $n-2$ and $n$. In a fundamental paper, Atkinson and Fedorov [Biometrika 62 (1975a) 57--70] proposed the $T$-optimality criterion for this purpose. Recently, Atkinson [MODA 9, Advances in Model-Oriented Design and Analysis (2010) 9--16] determined $T$-optimal designs for polynomials up to degree 6 numerically and based on these results he conjectured that the support points of the optimal design are cosines of the angles that divide half of the circle into equal parts if the coefficient of $x^{n-1}$ in the polynomial of larger degree vanishes. In the present paper we give a strong justification of the conjecture and determine all $T$-optimal designs explicitly for any degree $n\\in\\mathbb{N}$. In particular, we show that there exists a one-dimensional class of $T$-optimal designs. Moreover, we also present a generalization to the case when the ratio between the coefficients...
Health benefit modelling and optimization of vehicular pollution control strategies
Sonawane, Nayan V.; Patil, Rashmi S.; Sethi, Virendra
2012-12-01
This study asserts that the evaluation of pollution reduction strategies should be approached on the basis of health benefits. The framework presented could be used for decision making on the basis of cost effectiveness when the strategies are applied concurrently. Several vehicular pollution control strategies have been proposed in literature for effective management of urban air pollution. The effectiveness of these strategies has been mostly studied as a one at a time approach on the basis of change in pollution concentration. The adequacy and practicality of such an approach is studied in the present work. Also, the assessment of respective benefits of these strategies has been carried out when they are implemented simultaneously. An integrated model has been developed which can be used as a tool for optimal prioritization of various pollution management strategies. The model estimates health benefits associated with specific control strategies. ISC-AERMOD View has been used to provide the cause-effect relation between control options and change in ambient air quality. BenMAP, developed by U.S. EPA, has been applied for estimation of health and economic benefits associated with various management strategies. Valuation of health benefits has been done for impact indicators of premature mortality, hospital admissions and respiratory syndrome. An optimization model has been developed to maximize overall social benefits with determination of optimized percentage implementations for multiple strategies. The model has been applied for sub-urban region of Mumbai city for vehicular sector. Several control scenarios have been considered like revised emission standards, electric, CNG, LPG and hybrid vehicles. Reduction in concentration and resultant health benefits for the pollutants CO, NOx and particulate matter are estimated for different control scenarios. Finally, an optimization model has been applied to determine optimized percentage implementation of specific
A simple model of optimal population coding for sensory systems.
Doi, Eizaburo; Lewicki, Michael S
2014-08-01
A fundamental task of a sensory system is to infer information about the environment. It has long been suggested that an important goal of the first stage of this process is to encode the raw sensory signal efficiently by reducing its redundancy in the neural representation. Some redundancy, however, would be expected because it can provide robustness to noise inherent in the system. Encoding the raw sensory signal itself is also problematic, because it contains distortion and noise. The optimal solution would be constrained further by limited biological resources. Here, we analyze a simple theoretical model that incorporates these key aspects of sensory coding, and apply it to conditions in the retina. The model specifies the optimal way to incorporate redundancy in a population of noisy neurons, while also optimally compensating for sensory distortion and noise. Importantly, it allows an arbitrary input-to-output cell ratio between sensory units (photoreceptors) and encoding units (retinal ganglion cells), providing predictions of retinal codes at different eccentricities. Compared to earlier models based on redundancy reduction, the proposed model conveys more information about the original signal. Interestingly, redundancy reduction can be near-optimal when the number of encoding units is limited, such as in the peripheral retina. We show that there exist multiple, equally-optimal solutions whose receptive field structure and organization vary significantly. Among these, the one which maximizes the spatial locality of the computation, but not the sparsity of either synaptic weights or neural responses, is consistent with known basic properties of retinal receptive fields. The model further predicts that receptive field structure changes less with light adaptation at higher input-to-output cell ratios, such as in the periphery.
A model for HIV/AIDS pandemic with optimal control
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.
Wind-Wave Model with an Optimized Source Function
Polnikov, Vladislav
2010-01-01
On the basis of the author's earlier results, a new source function for a numerical wind-wave model optimized by the criterion of accuracy and speed of calculation is substantiated. The proposed source function includes (a) an optimized version of the discrete interaction approximation for parametrization of the nonlinear evolution mechanism, (b) a generalized empirical form of the input term modified by adding a special block of the dynamic boundary layer of the atmosphere, and (c) a dissipation term quadratic in the wave spectrum. Particular attention is given to a theoretical substantiation of the least investigated dissipation term. The advantages of the proposed source function are discussed by its comparison to the analogues used in the widespread models of the third generation WAM and WAVEWATCH. At the initial stage of assessing the merits of the proposed model, the results of its testing by the system of academic tests are presented. In the course of testing, some principals of this procedure are form...
Learning with Admixture: Modeling, Optimization, and Applications in Population Genetics
DEFF Research Database (Denmark)
Cheng, Jade Yu
2016-01-01
Population genetics is a branch of applied mathematics. It is a translation of scientific observations into mathematical models and their manipulations in order to produce quantitative predictions about evolution. Combining knowledge from genetics, statistics, and computer science, population...... data. Ohana's admixture module is based on classical structure modeling but uses new optimization subroutines through quadratic programming, which outperform the current state-of-the-art software in both speed and accuracy. Ohana presents a new method for phylogenetic tree inference using Gaussian...... the foundation for both CoalHMM and Ohana. Optimization modeling has been the main theme throughout my PhD, and it will continue to shape my work for the years to come. The algorithms and software I developed to study historical admixture and population evolution fall into a larger family of machine learning...
Modeling the dynamic optimal advertising in stochastic condition
Institute of Scientific and Technical Information of China (English)
Rong DU; Qiying HU; Zhiqing MENG
2004-01-01
An effort to model the dynamic optimal advertising was made with the uncertainty of sales responses in consideration. The problem of dynamic advertising was depicted as a Markov decision process with two state variables. When a firm launches an advertising campaign, it may predict the probability that the campaign will obtain the sales reponse. This probability was chosen as one state variable. Cumulative sales volume was chosen as another state variable which varies randomly with advertising. The only decision variable was advertising expenditure. With these variables, a multi-stage Markov decision process model was formulated. On the basis of some propositions the model was analyzed. Some analytical results about the optimal strategy have been derived, and their practical implications have been explained.
Orbital Optimization in the Active Space Decomposition Model
Kim, Inkoo; Shiozaki, Toru
2015-01-01
We report the derivation and implementation of orbital optimization algorithms for the active space decomposition (ASD) model, which are extensions of complete active space self-consistent field (CASSCF) and its occupation-restricted variants in the conventional multiconfiguration electronic-structure theory. Orbital rotations between active subspaces are included in the optimization, which allows us to unambiguously partition the active space into subspaces, enabling application of ASD to electron and exciton dynamics in covalently linked chromophores. One- and two-particle reduced density matrices, which are required for evaluation of orbital gradient and approximate Hessian elements, are computed from the intermediate tensors in the ASD energy evaluation. Numerical results on 4-(2-naphthylmethyl)-benzaldehyde and [3$_6$]cyclophane and model Hamiltonian analyses of triplet energy transfer processes in the Closs systems are presented. Furthermore model Hamiltonians for hole and electron transfer processes in...
Multiview coding mode decision with hybrid optimal stopping model.
Zhao, Tiesong; Kwong, Sam; Wang, Hanli; Wang, Zhou; Pan, Zhaoqing; Kuo, C-C Jay
2013-04-01
In a generic decision process, optimal stopping theory aims to achieve a good tradeoff between decision performance and time consumed, with the advantages of theoretical decision-making and predictable decision performance. In this paper, optimal stopping theory is employed to develop an effective hybrid model for the mode decision problem, which aims to theoretically achieve a good tradeoff between the two interrelated measurements in mode decision, as computational complexity reduction and rate-distortion degradation. The proposed hybrid model is implemented and examined with a multiview encoder. To support the model and further promote coding performance, the multiview coding mode characteristics, including predicted mode probability and estimated coding time, are jointly investigated with inter-view correlations. Exhaustive experimental results with a wide range of video resolutions reveal the efficiency and robustness of our method, with high decision accuracy, negligible computational overhead, and almost intact rate-distortion performance compared to the original encoder.
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.
Dynamic optimization model for allocating medical resources in epidemic controlling
Directory of Open Access Journals (Sweden)
Ming Liu
2013-03-01
Full Text Available Purpose: The model proposed in this paper addresses a dynamic optimization model for allocating medical resources in epidemic controlling.Design/methodology/approach: In this work, a three-level and dynamic linear programming model for allocating medical resources based on epidemic diffusion model is proposed. The epidemic diffusion model is used to construct the forecasting mechanism for dynamic demand of medical resources. Heuristic algorithm coupled with MTLAB mathematical programming solver is adopted to solve the model. A numerical example is presented for testing the model’s practical applicability.Findings: The main contribution of the present study is that a discrete time-space network model to study the medical resources allocation problem when an epidemic outbreak is formulated. It takes consideration of the time evolution and dynamic nature of the demand, which is different from most existing researches on medical resources allocation.Practical implications: In our model, the medicine logistics operation problem has been decomposed into several mutually correlated sub-problems, and then be solved systematically in the same decision scheme. Thus, the result will be much more suitable for real operations.Originality/value: In our model, the rationale that the medical resources allocated in early periods will take effect in subduing the spread of the epidemic spread and thus impact the demand in later periods has been for the first time incorporated. A win-win emergency rescue effect is achieved by the integrated and dynamic optimization model. The total rescue cost is controlled effectively, and meanwhile, inventory level in each urban health departments is restored and raised gradually.
A Multi-Stage Optimization Model With Minimum Energy Consumption-Wireless Mesh Networks
Directory of Open Access Journals (Sweden)
S. Krishnakumar
2012-09-01
Full Text Available Optimization models related with routing, bandwidth utilization and power consumption are developed in the wireless mesh computing environment using the operations research techniques such as maximal flow model, transshipment model and minimax optimizing algorithm. The Path creation algorithm is used to find the multiple paths from source to destination.A multi-stage optimization model is developed by combining the multi-path optimization model, optimization model in capacity utilization and energy optimization model and minimax optimizing algorithm. The input to the multi-stage optimization model is a network with many source and destination. The optimal solution obtained from this model is a minimum energy consuming path from source to destination along with the maximum data rate over each link. The performance is evaluated by comparing the data rate values of superimposed algorithm and minimax optimizing algorithm. The main advantage of this model is the reduction of traffic congestion in the network.
Parameter optimization in differential geometry based solvation models.
Wang, Bao; Wei, G W
2015-10-01
Differential geometry (DG) based solvation models are a new class of variational implicit solvent approaches that are able to avoid unphysical solvent-solute boundary definitions and associated geometric singularities, and dynamically couple polar and non-polar interactions in a self-consistent framework. Our earlier study indicates that DG based non-polar solvation model outperforms other methods in non-polar solvation energy predictions. However, the DG based full solvation model has not shown its superiority in solvation analysis, due to its difficulty in parametrization, which must ensure the stability of the solution of strongly coupled nonlinear Laplace-Beltrami and Poisson-Boltzmann equations. In this work, we introduce new parameter learning algorithms based on perturbation and convex optimization theories to stabilize the numerical solution and thus achieve an optimal parametrization of the DG based solvation models. An interesting feature of the present DG based solvation model is that it provides accurate solvation free energy predictions for both polar and non-polar molecules in a unified formulation. Extensive numerical experiment demonstrates that the present DG based solvation model delivers some of the most accurate predictions of the solvation free energies for a large number of molecules.
Optimization of Valve Disc Using Orthogonal Array and Kriging Model
Song, Xueguan; Wang, Lin; Kang, Jungho; Kim, Seung Gyu; Jo, Young Jik; Park, Youngchul
2008-10-01
A butterfly valve is a type of flow control device, typically used to regulate a fluid flowing. Currently, FEA is often used to predict the safety in the design of valve disc. Also, the study about the affection of butterfly valve's disc to the valve flow characteristics by using CFD has been done by many researchers. Along with the development of computer technique, design and analysis of computer experiments has becoming more and more important in engineering design and optimization. Hereinto Kriging model is one popular analysis approach for the purpose of creating a cheap "meta-model" as a surrogate to a computationally expensive simulation model. In this paper, the numerical analysis considered the strength, pressure loss coefficient and weight of valve disc simultaneously is investigated to improve the shape of a traditional butterfly valve disc. Firstly, an initial model of butterfly valve is made to evaluate the performance of the valve disc by using CFD and FEM. Then several experiments with different variables combination of the valve disc are conducted by mean of orthogonal array. Finally, the Kriging model is used to find the optimum variables combination of valve disc based on the result of computer experiments. In addition, the optimum result is verified by FEA and CFD simulation again. The result shows that compared with traditional computer experiments, optimization by using Kriging model can improve the weight of the valve disc very effectively in a short time.
Linear versus quadratic portfolio optimization model with transaction cost
Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah
2014-06-01
Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.
Variable Neighborhood Simplex Search Methods for Global Optimization Models
Directory of Open Access Journals (Sweden)
Pongchanun Luangpaiboon
2012-01-01
Full Text Available Problem statement: Many optimization problems of practical interest are encountered in various fields of chemical, engineering and management sciences. They are computationally intractable. Therefore, a practical algorithm for solving such problems is to employ approximation algorithms that can find nearly optimums within a reasonable amount of computational time. Approach: In this study the hybrid methods combining the Variable Neighborhood Search (VNS and simplexs family methods are proposed to deal with the global optimization problems of noisy continuous functions including constrained models. Basically, the simplex methods offer a search scheme without the gradient information whereas the VNS has the better searching ability with a systematic change of neighborhood of the current solution within a local search. Results: The VNS modified simplex method has a better searching ability for optimization problems with noise. The VNS modified simplex method also outperforms in average on the characteristics of intensity and diversity during the evolution of design point moving stage for the constrained optimization. Conclusion: The adaptive hybrid versions have proved to obtain significantly better results than the conventional methods. The amount of computation effort required for successful optimization is very sensitive to the rate of noise decrease of the process yields. Under circumstances of constrained optimization and gradually increasing the noise during an optimization the most preferred approach is the VNS modified simplex method.
Some Results on Optimal Dividend Problem in Two Risk Models
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Shuaiqi Zhang
2010-12-01
Full Text Available The compound Poisson risk model and the compound Poisson risk model perturbed by diffusion are considered in the presence of a dividend barrier with solvency constraints. Moreover, it extends the known result due to [1]. Ref. [1] finds the optimal dividend policy is of a barrier type for a jump-diffusion model with exponentially distributed jumps. In this paper, it turns out that there can be two different solutions depending on the model’s parameters. Furthermore, an interesting result is given: the proportional transaction cost has no effect on the dividend barrier. The objective of the corporation is to maximize the cumulative expected discounted dividends payout with solvency constraints before the time of ruin. It is well known that under some reasonable assumptions, optimal dividend strategy is a barrier strategy, i.e., there is a level b_{1}(b_{2} so that whenever surplus goes above the level b_{1}(b_{2}, the excess is paid out as dividends. However, the optimal level b_{1}(b_{2} may be unacceptably low from a solvency point of view. Therefore, some constraints should imposed on an insurance company such as to pay out dividends unless the surplus has reached a level b^{1}_{c}>b_{1}(b^2_{c}>b_{2} . We show that in this case a barrier strategy at b^{1}_{c}(b^2_{c} is optimal.
Optimal model-free prediction from multivariate time series.
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.
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.
Directory of Open Access Journals (Sweden)
G. Madasamy Raja
2013-01-01
Full Text Available Texture analysis is one of the important as well as useful tasks in image processing applications. Many texture models have been developed over the past few years and Local Binary Patterns (LBP is one of the simple and efficient approach among them. A number of extensions to the LBP method have been also presented but the problem remains challenging in feature vector generation and comparison. As textures are oriented and scaled differently, a texture model should effectively handle grey-scale variation, rotation variation, illumination variation and noise. The length of the feature vector in a texture model also plays an important role in deciding the time complexity of the texture analysis. This study proposes a new texture model, called Optimized Local Ternary Patterns (OLTP in the spatial methods of texture analysis. The proposed texture model is based on Local Ternary Patterns (LTP, which in turn is based on LBP. A new concept called âLevel of Optimalityâ to select the optimal set of patterns is discussed in this study. This proposed texture model uses only optimal patterns to extract the textural information from the digital images and thereby reducing the length of the feature vector. This proposed model is robust to image rotation, grey-scale transformation, histogram equalization and noise. The results are compared with other widely used texture models by applying classification tests to variety of texture images from the standard Brodatz texture database. Experimental results prove that the proposed texture model is robust to grey-scale variation, image rotation, histogram equalization and noise. Experimental results also show that the proposed texture model improves the classification accuracy and the speed of the classification process. In all tested tasks, the proposed method outperforms the earlier methods.
RISK LOAN PORTFOLIO OPTIMIZATION MODEL BASED ON CVAR RISK MEASURE
Directory of Open Access Journals (Sweden)
Ming-Chang LEE
2015-07-01
Full Text Available In order to achieve commercial banks liquidity, safety and profitability objective requirements, loan portfolio risk analysis based optimization decisions are rational allocation of assets. The risk analysis and asset allocation are the key technology of banking and risk management. The aim of this paper, build a loan portfolio optimization model based on risk analysis. Loan portfolio rate of return by using Value-at-Risk (VaR and Conditional Value-at-Risk (CVaR constraint optimization decision model reflects the bank's risk tolerance, and the potential loss of direct control of the bank. In this paper, it analyze a general risk management model applied to portfolio problems with VaR and CVaR risk measures by using Using the Lagrangian Algorithm. This paper solves the highly difficult problem by matrix operation method. Therefore, the combination of this paper is easy understanding the portfolio problems with VaR and CVaR risk model is a hyperbola in mean-standard deviation space. It is easy calculation in proposed method.
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.
Exchange Rate Forecasting Using Entropy Optimized Multivariate Wavelet Denoising Model
Directory of Open Access Journals (Sweden)
Kaijian He
2014-01-01
Full Text Available Exchange rate is one of the key variables in the international economics and international trade. Its movement constitutes one of the most important dynamic systems, characterized by nonlinear behaviors. It becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulation and global integration worldwide. Facing the increasingly diversified and more integrated market environment, the forecasting model in the exchange markets needs to address the individual and interdependent heterogeneity. In this paper, we propose the heterogeneous market hypothesis- (HMH- based exchange rate modeling methodology to model the micromarket structure. Then we further propose the entropy optimized wavelet-based forecasting algorithm under the proposed methodology to forecast the exchange rate movement. The multivariate wavelet denoising algorithm is used to separate and extract the underlying data components with distinct features, which are modeled with multivariate time series models of different specifications and parameters. The maximum entropy is introduced to select the best basis and model parameters to construct the most effective forecasting algorithm. Empirical studies in both Chinese and European markets have been conducted to confirm the significant performance improvement when the proposed model is tested against the benchmark models.
Optimal control applied to a thoraco-abdominal CPR model.
Jung, Eunok; Lenhart, Suzanne; Protopopescu, Vladimir; Babbs, Charles
2008-06-01
The techniques of optimal control are applied to a validated blood circulation model of cardiopulmonary resuscitation (CPR), consisting of a system of seven difference equations. In this system, the non-homogeneous forcing terms are chest and abdominal pressures acting as the 'controls'. We seek to maximize the blood flow, as measured by the pressure difference between the thoracic aorta and the right atrium. By applying optimal control methods, we characterize the optimal waveforms for external chest and abdominal compression during cardiac arrest and CPR in terms of the solutions of the circulation model and of the corresponding adjoint system. Numerical results are given for various scenarios. The optimal waveforms confirm the previously discovered positive effects of active decompression and interposed abdominal compression. These waveforms can be implemented with manual (Lifestick-like) and mechanical (vest-like) devices to achieve levels of blood flow substantially higher than those provided by standard CPR, a technique which, despite its long history, is far from optimal.
The Virtual Continuous TEG Model: Efficient Optimization of Thermogenerators
Kitte, J.; Beck, F.; Jänsch, D.
2013-07-01
Dimensioning a thermoelectric generator for vehicle applications poses major challenges. Besides the fundamental process of determining the layout, an optimization procedure is necessary to harness the maximum potential from a thermoelectric system under given boundary conditions. The thermal boundary conditions encountered in this application are not constant. In this context, a multichannel thermogenerator shows benefits by distributing individual mass flows in relation to the operating point maximizing power output across the entire range of operating points. The innovative approach underlying the continuous thermogenerator model supports the process of global optimization. The parameters to be optimized are configured as dimensionless variables. The model not only guarantees very short computation times but also maintains high quality. The optimization method is presented in detail using an example of searching for an optimum material layout, variable fin geometry, and variable leg height across and along the direction of gas flow. The materials or material combinations to be analyzed are lead and bismuth telluride. The heat exchanger has a reference geometry. The article describes the combination of dimensionless optimization parameters that provides the greatest increase in thermoelectric power output compared with the basic concept. The discussion concludes with a cost-benefit analysis of the measures chosen.
Advanced Nuclear Fuel Cycle Transitions: Optimization, Modeling Choices, and Disruptions
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
Distributionally Robust Return-Risk Optimization Models and Their Applications
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Li Yang
2014-01-01
Full Text Available Based on the risk control of conditional value-at-risk, distributionally robust return-risk optimization models with box constraints of random vector are proposed. They describe uncertainty in both the distribution form and moments (mean and covariance matrix of random vector. It is difficult to solve them directly. Using the conic duality theory and the minimax theorem, the models are reformulated as semidefinite programming problems, which can be solved by interior point algorithms in polynomial time. An important theoretical basis is therefore provided for applications of the models. Moreover, an application of the models to a practical example of portfolio selection is considered, and the example is evaluated using a historical data set of four stocks. Numerical results show that proposed methods are robust and the investment strategy is safe.
H∞ Optimal Model Reduction for Singular Fast Subsystems
Institute of Scientific and Technical Information of China (English)
WANGJing; ZHANGQing-Ling; LIUWan-Quan; ZHOUYue
2005-01-01
In this paper, H∞ optimal model reduction for singular fast subsystems will be investigated. First, error system is established to measure the error magnitude between the original and reduced systems, and it is demonstrated that the new feature for model reduction of singular systems is to make H∞ norm of the error system finite and minimal. The necessary and sufficient condition is derived for the existence of the H∞ suboptimal model reduction problem. Next, we give an exactand practicable algorithm to get the parameters of the reduced subsystems by applying the matrix theory. Meanwhile, the reduced system may be also impulsive. The advantages of the proposed algorithm are that it is more flexible in a straight-forward way without much extra computation, and the order of the reduced systems is as minimal as possible. Finally, one illustrative example is given to illustrate the effectiveness of the proposed model reduction approach.
Application of mesoscale modeling optimization to development of advanced materials
Institute of Scientific and Technical Information of China (English)
SONG Xiaoyan
2004-01-01
The rapid development of computer modeling in recent years offers opportunities for materials preparation in a more economic and efficient way. In the present paper, a practicable route for research and development of advanced materials by applying the visual and quantitative modeling technique on the mesoscale is introduced. A 3D simulation model is developed to describe the microstructure evolution during the whole process of deformation, recrystallization and grain growth in a material containing particles. In the light of simulation optimization, the long-term stabilized fine grain structures ideal for high-temperature applications are designed and produced. In addition, the feasibility, reliability and prospects of material development based on mesoscale modeling are discussed.
Non-linear DSGE Models and The Optimized Particle Filter
DEFF Research Database (Denmark)
Andreasen, Martin Møller
This paper improves the accuracy and speed of particle filtering for non-linear DSGE models with potentially non-normal shocks. This is done by introducing a new proposal distribution which i) incorporates information from new observables and ii) has a small optimization step that minimizes...... the distance to the optimal proposal distribution. A particle filter with this proposal distribution is shown to deliver a high level of accuracy even with relatively few particles, and this filter is therefore much more efficient than the standard particle filter....
Neighboring extremal optimal control design including model mismatch errors
Energy Technology Data Exchange (ETDEWEB)
Kim, T.J. [Sandia National Labs., Albuquerque, NM (United States); Hull, D.G. [Texas Univ., Austin, TX (United States). Dept. of Aerospace Engineering and Engineering Mechanics
1994-11-01
The mismatch control technique that is used to simplify model equations of motion in order to determine analytic optimal control laws is extended using neighboring extremal theory. The first variation optimal control equations are linearized about the extremal path to account for perturbations in the initial state and the final constraint manifold. A numerical example demonstrates that the tuning procedure inherent in the mismatch control method increases the performance of the controls to the level of a numerically-determined piecewise-linear controller.
Optimization of flagellar swimming by a model sperm
Felderhof, B U
2014-01-01
The swimming of a bead-spring chain in a viscous incompressible fluid as a model of a sperm is studied in the framework of low Reynolds number hydrodynamics. The optimal mode in the class of planar flagellar strokes of small amplitude is determined on the basis of a generalized eigenvalue problem involving two matrices which can be evaluated from the mobility matrix of the set of spheres constituting the chain. For an elastic chain with a cargo constraint for its spherical head, the actuating forces yielding a nearly optimal stroke can be determined. These can be used in a Stokesian dynamics simulation of large amplitude swimming.
Replica Analysis for Portfolio Optimization with Single-Factor Model
Shinzato, Takashi
2017-06-01
In this paper, we use replica analysis to investigate the influence of correlation among the return rates of assets on the solution of the portfolio optimization problem. We consider the behavior of an optimal solution for the case where the return rate is described with a single-factor model and compare the findings obtained from our proposed methods with correlated return rates with those obtained with independent return rates. We then analytically assess the increase in the investment risk when correlation is included. Furthermore, we also compare our approach with analytical procedures for minimizing the investment risk from operations research.
Modelling of Rabies Transmission Dynamics Using Optimal Control Analysis
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Joshua Kiddy K. Asamoah
2017-01-01
Full Text Available We examine an optimal way of eradicating rabies transmission from dogs into the human population, using preexposure prophylaxis (vaccination and postexposure prophylaxis (treatment due to public education. We obtain the disease-free equilibrium, the endemic equilibrium, the stability, and the sensitivity analysis of the optimal control model. Using the Latin hypercube sampling (LHS, the forward-backward sweep scheme and the fourth-order Range-Kutta numerical method predict that the global alliance for rabies control’s aim of working to eliminate deaths from canine rabies by 2030 is attainable through mass vaccination of susceptible dogs and continuous use of pre- and postexposure prophylaxis in humans.
Optimal Tax Reduction by Depreciation : A Stochastic Model
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 straight line depreciation method and a speci c accelerated depreciation method. We show how the distributions of the cash- ows, the discount rate, and the tax structure can in uence the optimal deci...
Dynamic stochastic optimization models for air traffic flow management
Mukherjee, Avijit
This dissertation presents dynamic stochastic optimization models for Air Traffic Flow Management (ATFM) that enables decisions to adapt to new information on evolving capacities of National Airspace System (NAS) resources. Uncertainty is represented by a set of capacity scenarios, each depicting a particular time-varying capacity profile of NAS resources. We use the concept of a scenario tree in which multiple scenarios are possible initially. Scenarios are eliminated as possibilities in a succession of branching points, until the specific scenario that will be realized on a particular day is known. Thus the scenario tree branching provides updated information on evolving scenarios, and allows ATFM decisions to be re-addressed and revised. First, we propose a dynamic stochastic model for a single airport ground holding problem (SAGHP) that can be used for planning Ground Delay Programs (GDPs) when there is uncertainty about future airport arrival capacities. Ground delays of non-departed flights can be revised based on updated information from scenario tree branching. The problem is formulated so that a wide range of objective functions, including non-linear delay cost functions and functions that reflect equity concerns can be optimized. Furthermore, the model improves on existing practice by ensuring efficient use of available capacity without necessarily exempting long-haul flights. Following this, we present a methodology and optimization models that can be used for decentralized decision making by individual airlines in the GDP planning process, using the solutions from the stochastic dynamic SAGHP. Airlines are allowed to perform cancellations, and re-allocate slots to remaining flights by substitutions. We also present an optimization model that can be used by the FAA, after the airlines perform cancellation and substitutions, to re-utilize vacant arrival slots that are created due to cancellations. Finally, we present three stochastic integer programming
Simulation platform to model, optimize and design wind turbines
Energy Technology Data Exchange (ETDEWEB)
Iov, F.; Hansen, A.D.; Soerensen, P.; Blaabjerg, F.
2004-03-01
This report is a general overview of the results obtained in the project 'Electrical Design and Control. Simulation Platform to Model, Optimize and Design Wind Turbines'. The motivation for this research project is the ever-increasing wind energy penetration into the power network. Therefore, the project has the main goal to create a model database in different simulation tools for a system optimization of the wind turbine systems. Using this model database a simultaneous optimization of the aerodynamic, mechanical, electrical and control systems over the whole range of wind speeds and grid characteristics can be achieved. The report is structured in six chapters. First, the background of this project and the main goals as well as the structure of the simulation platform is given. The main topologies for wind turbines, which have been taken into account during the project, are briefly presented. Then, the considered simulation tools namely: HAWC, DIgSILENT, Saber and Matlab/Simulink have been used in this simulation platform are described. The focus here is on the modelling and simulation time scale aspects. The abilities of these tools are complementary and they can together cover all the modelling aspects of the wind turbines e.g. mechanical loads, power quality, switching, control and grid faults. However, other simulation packages e.g PSCAD/EMTDC can easily be added in the simulation platform. New models and new control algorithms for wind turbine systems have been developed and tested in these tools. All these models are collected in dedicated libraries in Matlab/Simulink as well as in Saber. Some simulation results from the considered tools are presented for MW wind turbines. These simulation results focuses on fixed-speed and variable speed/pitch wind turbines. A good agreement with the real behaviour of these systems is obtained for each simulation tool. These models can easily be extended to model different kinds of wind turbines or large wind
Web malware spread modelling and optimal control strategies
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.
Roll levelling semi-analytical model for process optimization
Silvestre, E.; Garcia, D.; Galdos, L.; Saenz de Argandoña, E.; Mendiguren, J.
2016-08-01
Roll levelling is a primary manufacturing process used to remove residual stresses and imperfections of metal strips in order to make them suitable for subsequent forming operations. In the last years the importance of this process has been evidenced with the apparition of Ultra High Strength Steels with strength > 900 MPa. The optimal setting of the machine as well as a robust machine design has become critical for the correct processing of these materials. Finite Element Method (FEM) analysis is the widely used technique for both aspects. However, in this case, the FEM simulation times are above the admissible ones in both machine development and process optimization. In the present work, a semi-analytical model based on a discrete bending theory is presented. This model is able to calculate the critical levelling parameters i.e. force, plastification rate, residual stresses in a few seconds. First the semi-analytical model is presented. Next, some experimental industrial cases are analyzed by both the semi-analytical model and the conventional FEM model. Finally, results and computation times of both methods are compared.
Utilization-Based Modeling and Optimization for Cognitive Radio Networks
Liu, Yanbing; Huang, Jun; Liu, Zhangxiong
The cognitive radio technique promises to manage and allocate the scarce radio spectrum in the highly varying and disparate modern environments. This paper considers a cognitive radio scenario composed of two queues for the primary (licensed) users and cognitive (unlicensed) users. According to the Markov process, the system state equations are derived and an optimization model for the system is proposed. Next, the system performance is evaluated by calculations which show the rationality of our system model. Furthermore, discussions among different parameters for the system are presented based on the experimental results.
Process analysis and optimization models defining recultivation surface mines
Directory of Open Access Journals (Sweden)
Dimitrijević Bojan V.
2015-01-01
Full Text Available Surface mines are generally open and very dynamic systems influenced by a large number of technical, economic, environmental and safety factors and limitations in all stages of the life cycle. In this paper the dynamic compliance period surface mining phases and of the reclamation. Also, an analysis of the reclamation of surface mines, and process flow management project recultivation is determined by the principled management model reclamation. The analysis of the planning process is defined by the model optimization recultivation surface mine.
High Resolution Beam Modeling and Optimization with IMPACT
Qiang, Ji
2017-01-01
The LCLS-II, a new BES x-ray FEL facility at SLAC, is being designed using the IMPACT simulation code which includes a full model for the electron beam transport with 3-D space charge effects as well as IntraBeam Scattering and Coherent Synchrotron Radiation. A 22 parameter optimization is being used to find injector and linac configurations that achieve the design specifications. The detailed physics models in IMPACT are being benchmarked against experiments at LCLS. This work was done in collaboration with SLAC LCLS-II design team and supported by the DOE under contract No. DE-AC02-05CH11231.
Power System Aggregate Load Area Modelling by Particle Swarm Optimization
Institute of Scientific and Technical Information of China (English)
Jian-Lin Wei; Ji-Hong Wang; Q.H.Wu; Nan Lu
2005-01-01
This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.
A Smoothed Eclipse Model for Solar Electric Propulsion Trajectory Optimization
Aziz, Jonathan; Scheeres, Daniel; Parker, Jeffrey; Englander, Jacob
2017-01-01
Solar electric propulsion (SEP) is the dominant design option for employing low-thrust propulsion on a space mission. Spacecraft solar arrays power the SEP system but are subject to blackout periods during solar eclipse conditions. Discontinuity in power available to the spacecraft must be accounted for in trajectory optimization, but gradient-based methods require a differentiable power model. This work presents a power model that smooths the eclipse transition from total eclipse to total sunlight with a logistic function. Example trajectories are computed with differential dynamic programming, a second-order gradient-based method.
Survey of E-Commerce Modeling and Optimization Strategies
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Electronic commerce is impacting almost all commercial activities. The resulting emerging commercial activities bring with them many new modeling and optimization problems. This survey reviews pioneering works in this new area, covering topics in advertising strategy, web page design, automatic pricing, auction methods, brokerage strategy, and customer behavior analysis. Mathematical models for problems in these areas and their solution algorithms are discussed. In addition to presenting and commenting on these works, we also discuss possible extensions and related problems. The objective of this survey is to encourage more researchers to pay attention to this emerging area.
Optimizing ZigBee Security using Stochastic Model Checking
DEFF Research Database (Denmark)
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming
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......, 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...
Optimizing ZigBee Security using Stochastic Model Checking
Yüksel, Ender; Nielson, Flemming; Fruth, Matthias; Kwiatkowska, Marta
2012-01-01
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, 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 application scenarios.
Segla, S.
The paper deals with modelling and optimization of the half model of a passenger car with an ideal semi-active suspension, semi-active suspension equipped with magnetorheological dampers, passive suspension equipped with hydraulic dampers without control and compares their dynamic characteristics. The conventional skyhook control is used to control semi-active dampers taking into account the time delay. Selected parameters of the suspension systems are optimized for given road profiles using genetic algorithms. The results show that implementation of the magnetorheological dampers can lead to a significant improvement of the ride comfort and handling properties of passenger cars provided that the time delay is low enough.
Process Optimization with Simulation Modeling in a Manufacturing System
Directory of Open Access Journals (Sweden)
Akbel Yildiz
2011-04-01
Full Text Available Computer simulation has become an important tool in modeling systems in the last ten years due to parallel improvement in computer technologies. Companies tend to computer based system modeling and simulation not to lose any extra income or time to their competitors but to make future investments while they both have the same labor force, resources and technology. This study is an implementation of a machine spare parts manufacturer factory located in city of Turkey. The purpose of the study depends on increasing the utilization rates and optimizing the manufacture process to decrease prouction costs via identifying the bottlenecks in manufacture system. Therefore, ProModel simulation software was used to model the production line of the factory. Production line consists of nineteen work stations and was modeled for the most manufactured two products. The manufacture in the factory is divided into two weeks of batch production time and simulation model was demonstrated and replicated for ten times to get results. Thus, statistics including existing capacity usages of work stations in the whole production line were found to identify the bottlenecks of the critical work stations and machines. With the use of the simulation model, creating scenarios while making changes of the system parameters, taking the cycle times of the work stations, total production quantity, batch sizes and the shifts of the factory in hand helped to make suggestions.
Optimized GPU simulation of continuous-spin glass models
Yavors'kii, Taras
2012-01-01
We develop a highly optimized code for simulating the Edwards-Anderson Heisenberg model on graphics processing units (GPUs). Using a number of computational tricks such as tiling, data compression and appropriate memory layouts, the simulation code combining over-relaxation, heat bath and parallel tempering moves achieves a peak performance of 0.29 ns per spin update on realistic system sizes, corresponding to a more than 150 fold speed-up over a serial CPU reference implementation. The optimized implementation is used to study the spin-glass transition in a random external magnetic field to probe the existence of a de Almeida-Thouless line in the model, for which we give benchmark results.
Optimized GPU simulation of continuous-spin glass models
Yavors'kii, T.; Weigel, M.
2012-08-01
We develop a highly optimized code for simulating the Edwards-Anderson Heisenberg model on graphics processing units (GPUs). Using a number of computational tricks such as tiling, data compression and appropriate memory layouts, the simulation code combining over-relaxation, heat bath and parallel tempering moves achieves a peak performance of 0.29 ns per spin update on realistic system sizes, corresponding to a more than 150 fold speed-up over a serial CPU reference implementation. The optimized implementation is used to study the spin-glass transition in a random external magnetic field to probe the existence of a de Almeida-Thouless line in the model, for which we give benchmark results.
Autonomous guided vehicles methods and models for optimal path planning
Fazlollahtabar, Hamed
2015-01-01
This book provides readers with extensive information on path planning optimization for both single and multiple Autonomous Guided Vehicles (AGVs), and discusses practical issues involved in advanced industrial applications of AGVs. After discussing previously published research in the field and highlighting the current gaps, it introduces new models developed by the authors with the goal of reducing costs and increasing productivity and effectiveness in the manufacturing industry. The new models address the increasing complexity of manufacturing networks, due for example to the adoption of flexible manufacturing systems that involve automated material handling systems, robots, numerically controlled machine tools, and automated inspection stations, while also considering the uncertainty and stochastic nature of automated equipment such as AGVs. The book discusses and provides solutions to important issues concerning the use of AGVs in the manufacturing industry, including material flow optimization with A...
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 hedged asset and among themselves. We nd that the minimum variance hedge for assets driven by the CVAR, depends strongly on the portfolio holding period. The hedge is dened as a function of correlation and cointegration parameters. For short holding periods the correlation impact is predominant. For long...... horizons, the hedge ratio should overweight the cointegration parameters rather then short-run correlation information. In the innite horizon, the hedge ratios shall be equal to the cointegrating vector. The hedge ratios for any intermediate portfolio holding period should be based on the weighted average...
Irrigation Optimization by Modeling of Plant-Soil Interaction
2011-01-01
Irrigation scheduling is an important issue for crop management, in a general context of limited water resources and increasing concern about agricultural productivity. Methods to optimize crop irrigation should take into account the impact of water stress on plant growth and the water balance in the plant-soil-atmosphere system. In this article, we propose a methodology to solve the irrigation scheduling problem. For this purpose, a plant-soil interaction model is used to simulate the struct...
Optimization of Multipurpose Reservoir Systems Using Power Market Models
DEFF Research Database (Denmark)
Pereira-Cardenal, S. J.; Mo, B.; Riegels, N.
2015-01-01
optimal operation rules that maximize current and expected future benefits as a function of reservoir level, week of the year, and inflow state. The method was tested on the Iberian Peninsula and performed better than traditional approaches that use exogenous prices: resulting operation rules were more...... realistic and sensitive to hydrological variability. Internally calculated hydropower prices provided better results than exogenous hydropower prices and can therefore improve the representation of hydropower benefits in hydroeconomic models. (C) 2014 American Society of Civil Engineers....
Optimizing ZigBee Security using Stochastic Model Checking
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming; Fruth, Matthias; Kwiatkowska, Marta
2012-01-01
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, 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 checki...
Software tool for the prosthetic foot modeling and stiffness optimization.
Strbac, Matija; Popović, Dejan B
2012-01-01
We present the procedure for the optimization of the stiffness of the prosthetic foot. The procedure allows the selection of the elements of the foot and the materials used for the design. The procedure is based on the optimization where the cost function is the minimization of the difference between the knee joint torques of healthy walking and the walking with the transfemural prosthesis. We present a simulation environment that allows the user to interactively vary the foot geometry and track the changes in the knee torque that arise from these adjustments. The software allows the estimation of the optimal prosthetic foot elasticity and geometry. We show that altering model attributes such as the length of the elastic foot segment or its elasticity leads to significant changes in the estimated knee torque required for a given trajectory.
Optimization model of vaccination strategy for dengue transmission
Widayani, H.; Kallista, M.; Nuraini, N.; Sari, M. Y.
2014-02-01
Dengue fever is emerging tropical and subtropical disease caused by dengue virus infection. The vaccination should be done as a prevention of epidemic in population. The host-vector model are modified with consider a vaccination factor to prevent the occurrence of epidemic dengue in a population. An optimal vaccination strategy using non-linear objective function was proposed. The genetic algorithm programming techniques are combined with fourth-order Runge-Kutta method to construct the optimal vaccination. In this paper, the appropriate vaccination strategy by using the optimal minimum cost function which can reduce the number of epidemic was analyzed. The numerical simulation for some specific cases of vaccination strategy is shown.
Combustion optimization and HCCI modeling for ultra low emission
Energy Technology Data Exchange (ETDEWEB)
Koten, Hasan; Yilmaz, Mustafa; Zafer Gul, M. [Marmara University Mechanical Engineering Department (Turkey)], E-mail: hasan.koten@marmara.edu.tr
2011-07-01
With the coming shortage of fossil fuels and the rising concerns over the environment it is important to develop new technologies both to reduce energy consumption and pollution at the same time. In the transportation sector, new combustion processes are under development to provide clean diesel combustion with no particulate or NOx emissions. However, these processes have issues such as limited power output, high levels of unburned hydrocarbons, and carbon monoxide emissions. The aim of this paper is to present a methodology for optimizing combustion performance. The methodology consists of the use of a multi-objective genetic algorithm optimization tool; homogeneous charge compression ignition engine cases were studied with the ECFM-3Z combustion model. Results showed that injected fuel mass led to a decrease in power output, a finding which is in keeping with previous research. This paper presented on optimization tool which can be useful in improving the combustion process.
Software Tool for the Prosthetic Foot Modeling and Stiffness Optimization
Directory of Open Access Journals (Sweden)
Matija Štrbac
2012-01-01
Full Text Available We present the procedure for the optimization of the stiffness of the prosthetic foot. The procedure allows the selection of the elements of the foot and the materials used for the design. The procedure is based on the optimization where the cost function is the minimization of the difference between the knee joint torques of healthy walking and the walking with the transfemural prosthesis. We present a simulation environment that allows the user to interactively vary the foot geometry and track the changes in the knee torque that arise from these adjustments. The software allows the estimation of the optimal prosthetic foot elasticity and geometry. We show that altering model attributes such as the length of the elastic foot segment or its elasticity leads to significant changes in the estimated knee torque required for a given trajectory.
Mathematical model of the metal mould surface temperature optimization
Energy Technology Data Exchange (ETDEWEB)
Mlynek, Jaroslav, E-mail: jaroslav.mlynek@tul.cz; Knobloch, Roman, E-mail: roman.knobloch@tul.cz [Department of Mathematics, FP Technical University of Liberec, Studentska 2, 461 17 Liberec, The Czech Republic (Czech Republic); Srb, Radek, E-mail: radek.srb@tul.cz [Institute of Mechatronics and Computer Engineering Technical University of Liberec, Studentska 2, 461 17 Liberec, The Czech Republic (Czech Republic)
2015-11-30
The article is focused on the problem of generating a uniform temperature field on the inner surface of shell metal moulds. Such moulds are used e.g. in the automotive industry for artificial leather production. To produce artificial leather with uniform surface structure and colour shade the temperature on the inner surface of the mould has to be as homogeneous as possible. The heating of the mould is realized by infrared heaters located above the outer mould surface. The conceived mathematical model allows us to optimize the locations of infrared heaters over the mould, so that approximately uniform heat radiation intensity is generated. A version of differential evolution algorithm programmed in Matlab development environment was created by the authors for the optimization process. For temperate calculations software system ANSYS was used. A practical example of optimization of heaters locations and calculation of the temperature of the mould is included at the end of the article.
Modeling, simulation and optimization for science and technology
Kuznetsov, Yuri; Neittaanmäki, Pekka; Pironneau, Olivier
2014-01-01
This volume contains thirteen articles on advances in applied mathematics and computing methods for engineering problems. Six papers are on optimization methods and algorithms with emphasis on problems with multiple criteria; four articles are on numerical methods for applied problems modeled with nonlinear PDEs; two contributions are on abstract estimates for error analysis; finally one paper deals with rare events in the context of uncertainty quantification. Applications include aerospace, glaciology and nonlinear elasticity. Herein is a selection of contributions from speakers at two conferences on applied mathematics held in June 2012 at the University of Jyväskylä, Finland. The first conference, “Optimization and PDEs with Industrial Applications” celebrated the seventieth birthday of Professor Jacques Périaux of the University of Jyväskylä and Polytechnic University of Catalonia (Barcelona Tech), and the second conference, “Optimization and PDEs with Applications” celebrated the seventy-fi...
Influence of model errors in optimal sensor placement
Vincenzi, Loris; Simonini, Laura
2017-02-01
The paper investigates the role of model errors and parametric uncertainties in optimal or near optimal sensor placements for structural health monitoring (SHM) and modal testing. The near optimal set of measurement locations is obtained by the Information Entropy theory; the results of placement process considerably depend on the so-called covariance matrix of prediction error as well as on the definition of the correlation function. A constant and an exponential correlation function depending on the distance between sensors are firstly assumed; then a proposal depending on both distance and modal vectors is presented. With reference to a simple case-study, the effect of model uncertainties on results is described and the reliability and the robustness of the proposed correlation function in the case of model errors are tested with reference to 2D and 3D benchmark case studies. A measure of the quality of the obtained sensor configuration is considered through the use of independent assessment criteria. In conclusion, the results obtained by applying the proposed procedure on a real 5-spans steel footbridge are described. The proposed method also allows to better estimate higher modes when the number of sensors is greater than the number of modes of interest. In addition, the results show a smaller variation in the sensor position when uncertainties occur.
Model-based optimization of tapered free-electron lasers
Directory of Open Access Journals (Sweden)
Alan Mak
2015-04-01
Full Text Available The energy extraction efficiency is a figure of merit for a free-electron laser (FEL. It can be enhanced by the technique of undulator tapering, which enables the sustained growth of radiation power beyond the initial saturation point. In the development of a single-pass x-ray FEL, it is important to exploit the full potential of this technique and optimize the taper profile a_{w}(z. Our approach to the optimization is based on the theoretical model by Kroll, Morton, and Rosenbluth, whereby the taper profile a_{w}(z is not a predetermined function (such as linear or exponential but is determined by the physics of a resonant particle. For further enhancement of the energy extraction efficiency, we propose a modification to the model, which involves manipulations of the resonant particle’s phase. Using the numerical simulation code GENESIS, we apply our model-based optimization methods to a case of the future FEL at the MAX IV Laboratory (Lund, Sweden, as well as a case of the LCLS-II facility (Stanford, USA.
Optimization of a semiconductor manufacturing process using a reentrant model
Directory of Open Access Journals (Sweden)
Sarah Abuhab Valente
2015-01-01
Full Text Available The scope of this work is the simulation of a semiconductor manufacturing model in Arena® software and subsequent optimization and sensitivity analysis of this model. The process is considered extremely complex given the amount of steps, machinery, parameters, and highly reentrant characteristics, which makes it difficult to reach stability of production process. The production model used was the Intel Five-Machine Six-Step Mini-fab developed by Karl Kempf (1994. It was programmed in Arena® and optimized by OptQuest®, an add-on. We concluded that variation in the number of machines and operators reflects on cycle time only if there is an increase of one unit of resource more than obtained in the optimization. As a result, we highlighted the scenario where a reduction in cycle time stood out, in which one extra unit was added in the second machine group, representing a 7.41% reduction in cycle time.
An optimization model for the US Air-Traffic System
Mulvey, J. M.
1986-01-01
A systematic approach for monitoring U.S. air traffic was developed in the context of system-wide planning and control. Towards this end, a network optimization model with nonlinear objectives was chosen as the central element in the planning/control system. The network representation was selected because: (1) it provides a comprehensive structure for depicting essential aspects of the air traffic system, (2) it can be solved efficiently for large scale problems, and (3) the design can be easily communicated to non-technical users through computer graphics. Briefly, the network planning models consider the flow of traffic through a graph as the basic structure. Nodes depict locations and time periods for either individual planes or for aggregated groups of airplanes. Arcs define variables as actual airplanes flying through space or as delays across time periods. As such, a special case of the network can be used to model the so called flow control problem. Due to the large number of interacting variables and the difficulty in subdividing the problem into relatively independent subproblems, an integrated model was designed which will depict the entire high level (above 29000 feet) jet route system for the 48 contiguous states in the U.S. As a first step in demonstrating the concept's feasibility a nonlinear risk/cost model was developed for the Indianapolis Airspace. The nonlinear network program --NLPNETG-- was employed in solving the resulting test cases. This optimization program uses the Truncated-Newton method (quadratic approximation) for determining the search direction at each iteration in the nonlinear algorithm. It was shown that aircraft could be re-routed in an optimal fashion whenever traffic congestion increased beyond an acceptable level, as measured by the nonlinear risk function.
NWP model forecast skill optimization via closure parameter variations
Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.
2012-04-01
We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.
Leardini, Alberto; Belvedere, Claudio; Nardini, Fabrizio; Sancisi, Nicola; Conconi, Michele; Parenti-Castelli, Vincenzo
2017-05-22
Kinematic models of lower limb joints have several potential applications in musculoskeletal modelling of the locomotion apparatus, including the reproduction of the natural joint motion. These models have recently revealed their value also for in vivo motion analysis experiments, where the soft-tissue artefact is a critical known problem. This arises at the interface between the skin markers and the underlying bone, and can be reduced by defining multibody kinematic models of the lower limb and by running optimization processes aimed at obtaining estimates of position and orientation of relevant bones. With respect to standard methods based on the separate optimization of each single body segment, this technique makes it also possible to respect joint kinematic constraints. Whereas the hip joint is traditionally assumed as a 3 degrees of freedom ball and socket articulation, many previous studies have proposed a number of different kinematic models for the knee and ankle joints. Some of these are rigid, while others have compliant elements. Some models have clear anatomical correspondences and include real joint constraints; other models are more kinematically oriented, these being mainly aimed at reproducing joint kinematics. This paper provides a critical review of the kinematic models reported in literature for the major lower limb joints and used for the reduction of soft-tissue artefact. Advantages and disadvantages of these models are discussed, considering their anatomical significance, accuracy of predictions, computational costs, feasibility of personalization, and other features. Their use in the optimization process is also addressed, both in normal and pathological subjects. Copyright © 2017 Elsevier Ltd. All rights reserved.
Maintenance modeling and optimization integrating human and material resources
Energy Technology Data Exchange (ETDEWEB)
Martorell, S., E-mail: smartore@iqn.upv.e [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Villamizar, M.; Carlos, S. [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Sanchez, A. [Dpto. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica Valencia (Spain)
2010-12-15
Maintenance planning is a subject of concern to many industrial sectors as plant safety and business depend on it. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization (MOP) problem where reliability, availability, maintainability and cost (RAM+C) act as decision criteria and maintenance strategies (i.e. maintenance tasks intervals) act as the only decision variables. However the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources (human and material) available to implement such strategies. Thus, the effect of the necessary resources on RAM+C needs to be modeled and accounted for in formulating the MOP affecting the set of objectives and constraints. In this paper RAM+C models to explicitly address the effect of human resources and material resources (spare parts) on RAM+C criteria are proposed. This extended model allows accounting for explicitly how the above decision criteria depends on the basic model parameters representing the type of strategies, maintenance intervals, durations, human resources and material resources. Finally, an application case is performed to optimize the maintenance plan of a motor-driven pump equipment considering as decision variables maintenance and test intervals and human and material resources.
Parallelism and optimization of numerical ocean forecasting model
Xu, Jianliang; Pang, Renbo; Teng, Junhua; Liang, Hongtao; Yang, Dandan
2016-10-01
According to the characteristics of Chinese marginal seas, the Marginal Sea Model of China (MSMC) has been developed independently in China. Because the model requires long simulation time, as a routine forecasting model, the parallelism of MSMC becomes necessary to be introduced to improve the performance of it. However, some methods used in MSMC, such as Successive Over Relaxation (SOR) algorithm, are not suitable for parallelism. In this paper, methods are developedto solve the parallel problem of the SOR algorithm following the steps as below. First, based on a 3D computing grid system, an automatic data partition method is implemented to dynamically divide the computing grid according to computing resources. Next, based on the characteristics of the numerical forecasting model, a parallel method is designed to solve the parallel problem of the SOR algorithm. Lastly, a communication optimization method is provided to avoid the cost of communication. In the communication optimization method, the non-blocking communication of Message Passing Interface (MPI) is used to implement the parallelism of MSMC with complex physical equations, and the process of communication is overlapped with the computations for improving the performance of parallel MSMC. The experiments show that the parallel MSMC runs 97.2 times faster than the serial MSMC, and root mean square error between the parallel MSMC and the serial MSMC is less than 0.01 for a 30-day simulation (172800 time steps), which meets the requirements of timeliness and accuracy for numerical ocean forecasting products.
MENENTUKAN PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL CONDITIONAL MEAN VARIANCE
Directory of Open Access Journals (Sweden)
I GEDE ERY NISCAHYANA
2016-08-01
Full Text Available When the returns of stock prices show the existence of autocorrelation and heteroscedasticity, then conditional mean variance models are suitable method to model the behavior of the stocks. In this thesis, the implementation of the conditional mean variance model to the autocorrelated and heteroscedastic return was discussed. The aim of this thesis was to assess the effect of the autocorrelated and heteroscedastic returns to the optimal solution of a portfolio. The margin of four stocks, Fortune Mate Indonesia Tbk (FMII.JK, Bank Permata Tbk (BNLI.JK, Suryamas Dutamakmur Tbk (SMDM.JK dan Semen Gresik Indonesia Tbk (SMGR.JK were estimated by GARCH(1,1 model with standard innovations following the standard normal distribution and the t-distribution. The estimations were used to construct a portfolio. The portfolio optimal was found when the standard innovation used was t-distribution with the standard deviation of 1.4532 and the mean of 0.8023 consisting of 0.9429 (94% of FMII stock, 0.0473 (5% of BNLI stock, 0% of SMDM stock, 1% of SMGR stock.
Optimization of atmospheric transport models on HPC platforms
de la Cruz, Raúl; Folch, Arnau; Farré, Pau; Cabezas, Javier; Navarro, Nacho; Cela, José María
2016-12-01
The performance and scalability of atmospheric transport models on high performance computing environments is often far from optimal for multiple reasons including, for example, sequential input and output, synchronous communications, work unbalance, memory access latency or lack of task overlapping. We investigate how different software optimizations and porting to non general-purpose hardware architectures improve code scalability and execution times considering, as an example, the FALL3D volcanic ash transport model. To this purpose, we implement the FALL3D model equations in the WARIS framework, a software designed from scratch to solve in a parallel and efficient way different geoscience problems on a wide variety of architectures. In addition, we consider further improvements in WARIS such as hybrid MPI-OMP parallelization, spatial blocking, auto-tuning and thread affinity. Considering all these aspects together, the FALL3D execution times for a realistic test case running on general-purpose cluster architectures (Intel Sandy Bridge) decrease by a factor between 7 and 40 depending on the grid resolution. Finally, we port the application to Intel Xeon Phi (MIC) and NVIDIA GPUs (CUDA) accelerator-based architectures and compare performance, cost and power consumption on all the architectures. Implications on time-constrained operational model configurations are discussed.
Validation, Optimization and Simulation of a Solar Thermoelectric Generator Model
Madkhali, Hadi Ali; Hamil, Ali; Lee, HoSung
2017-08-01
This study explores thermoelectrics as a viable option for small-scale solar thermal applications. Thermoelectric technology is based on the Seebeck effect, which states that a voltage is induced when a temperature gradient is applied to the junctions of two differing materials. This research proposes to analyze, validate, simulate, and optimize a prototype solar thermoelectric generator (STEG) model in order to increase efficiency. The intent is to further develop STEGs as a viable and productive energy source that limits pollution and reduces the cost of energy production. An empirical study (Kraemer et al. in Nat Mater 10:532, 2011) on the solar thermoelectric generator reported a high efficiency performance of 4.6%. The system had a vacuum glass enclosure, a flat panel (absorber), thermoelectric generator and water circulation for the cold side. The theoretical and numerical approach of this current study validated the experimental results from Kraemer's study to a high degree. The numerical simulation process utilizes a two-stage approach in ANSYS software for Fluent and Thermal-Electric Systems. The solar load model technique uses solar radiation under AM 1.5G conditions in Fluent. This analytical model applies Dr. Ho Sung Lee's theory of optimal design to improve the performance of the STEG system by using dimensionless parameters. Applying this theory, using two cover glasses and radiation shields, the STEG model can achieve a highest efficiency of 7%.
Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations
Hanson, Andrea; Reed, Erik; Cavanagh, Peter
2011-01-01
Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.
Dendritic Immunotherapy Improvement for an Optimal Control Murine Model
Directory of Open Access Journals (Sweden)
J. C. Rangel-Reyes
2017-01-01
Full Text Available Therapeutic protocols in immunotherapy are usually proposed following the intuition and experience of the therapist. In order to deduce such protocols mathematical modeling, optimal control and simulations are used instead of the therapist’s experience. Clinical efficacy of dendritic cell (DC vaccines to cancer treatment is still unclear, since dendritic cells face several obstacles in the host environment, such as immunosuppression and poor transference to the lymph nodes reducing the vaccine effect. In view of that, we have created a mathematical murine model to measure the effects of dendritic cell injections admitting such obstacles. In addition, the model considers a therapy given by bolus injections of small duration as opposed to a continual dose. Doses timing defines the therapeutic protocols, which in turn are improved to minimize the tumor mass by an optimal control algorithm. We intend to supplement therapist’s experience and intuition in the protocol’s implementation. Experimental results made on mice infected with melanoma with and without therapy agree with the model. It is shown that the dendritic cells’ percentage that manages to reach the lymph nodes has a crucial impact on the therapy outcome. This suggests that efforts in finding better methods to deliver DC vaccines should be pursued.
Optimal control of CPR procedure using hemodynamic circulation model
Lenhart, Suzanne M.; Protopopescu, Vladimir A.; Jung, Eunok
2007-12-25
A method for determining a chest pressure profile for cardiopulmonary resuscitation (CPR) includes the steps of representing a hemodynamic circulation model based on a plurality of difference equations for a patient, applying an optimal control (OC) algorithm to the circulation model, and determining a chest pressure profile. The chest pressure profile defines a timing pattern of externally applied pressure to a chest of the patient to maximize blood flow through the patient. A CPR device includes a chest compressor, a controller communicably connected to the chest compressor, and a computer communicably connected to the controller. The computer determines the chest pressure profile by applying an OC algorithm to a hemodynamic circulation model based on the plurality of difference equations.
Fractional and multivariable calculus model building and optimization problems
Mathai, A M
2017-01-01
This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...
An improved model for TPV performance predictions and optimization
Schroeder, K. L.; Rose, M. F.; Burkhalter, J. E.
1997-03-01
Previously a model has been presented for calculating the performance of a TPV system. This model has been revised into a general purpose algorithm, improved in fidelity, and is presented here. The basic model is an energy based formulation and evaluates both the radiant and heat source elements of a combustion based system. Improvements in the radiant calculations include the use of ray tracking formulations and view factors for evaluating various flat plate and cylindrical configurations. Calculation of photocell temperature and performance parameters as a function of position and incident power have also been incorporated. Heat source calculations have been fully integrated into the code by the incorporation of a modified version of the NASA Complex Chemical Equilibrium Compositions and Applications (CEA) code. Additionally, coding has been incorporated to allow optimization of various system parameters and configurations. Several examples cases are presented and compared, and an optimum flat plate emitter/filter/photovoltaic configuration is also described.
Modeling and optimization of rotary kiln treating EAF dust
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Electric arc furnace (EAF) dust from steel industries is listed by the United Sates EPA as a hazardous waste under the regulations of the Resource Conservation and Recovery Act due to the presence of lead, cadmium and chlorine. The disposal of the approximately 650000 t of EAF dust per year in the U.S. and Canada is an expensive and unresolved problem for the majority of steel companies. The Waelz process has been considered as the best process for treating the EAF dust. A process model, combined thermodynamic modeling with heat transfer calculations, has been developed to simulate the chemical reactions, mass and heat transfer and heat balance in the kiln. The injection of air into the slag and the temperature profile along the kiln have been modeled. The effect of (CaO+MgO)/SiO2 on the solidus temperature of slag has also been predicted and discussed. Some optimized results have been presented.
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-05-01
Full Text Available Physical parameterizations in General Circulation Models (GCMs, having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.
A. Zerga; B. Benyoucef; J.-P. Charles
1998-01-01
Single and double exponential models are confronted to determine the most adapted model for optimization of solar cells efficiency. It is shown that the single exponential model (SEM) presents some insufficiencies for efficiency optimization. The interest of the double exponential model to optimize the efficiency and to achieve an adequate simulation of the operation of solar cells is demonstrated by means of I-V characteristics plotting.
H2-optimal control with generalized state-space models for use in control-structure optimization
Wette, Matt
1991-01-01
Several advances are provided solving combined control-structure optimization problems. The author has extended solutions from H2 optimal control theory to the use of generalized state space models. The generalized state space models preserve the sparsity inherent in finite element models and hence provide some promise for handling very large problems. Also, expressions for the gradient of the optimal control cost are derived which use the generalized state space models.
Optimization Model of Subjectivity Formation in Educational Environment
Directory of Open Access Journals (Sweden)
￼I. V. Vorobyeva
2012-01-01
Full Text Available The paper is devoted to the issues of the subjectivity formation in the educational environment, as well as the application of the relative psycho-technologies of facilitation and optimization of the process. The correlation between the notions of personality and subject is considered, though their scientific and semantic distinctions still remain obscure. In the frame- work of the competence approach, based on the concepts of accepted in education and psychology functional paradigm, the above terms are widely spread. Therefore, finding out the specifics of their genesis is both relevant and up-to-date. The authors conclude that the personality and subject are not equivalents, the same as the personality growth is not equivalent to the subjectivity formation. By means of theoretical analyses of the domestic approaches to the phenomena of subject and subjectivity, the four-stage process of the subject-genesis in the educa- tional environment has been modeled. At every stage of subjectivity formation in the particular conditions, it is necessary to use psycho-technologies relating to the actual targets of subject-genesis, which makes it possible to optimize the educational process in the context of the competence approach. The level-based description of the given process is proposed along with the model of its optimization in implementing the target psycho-technologies. The model can be applied both as a methodology basis for the complex development and optimization of psycho-pedagogical facilitation of the sub- jectivity formation, and as a theoretical basis for the educational strategy development and correction in a particular educational establishment.
Optimization Model of Subjectivity Formation in Educational Environment
Directory of Open Access Journals (Sweden)
￼I. V. Vorobyeva
2015-02-01
Full Text Available The paper is devoted to the issues of the subjectivity formation in the educational environment, as well as the application of the relative psycho-technologies of facilitation and optimization of the process. The correlation between the notions of personality and subject is considered, though their scientific and semantic distinctions still remain obscure. In the frame- work of the competence approach, based on the concepts of accepted in education and psychology functional paradigm, the above terms are widely spread. Therefore, finding out the specifics of their genesis is both relevant and up-to-date. The authors conclude that the personality and subject are not equivalents, the same as the personality growth is not equivalent to the subjectivity formation. By means of theoretical analyses of the domestic approaches to the phenomena of subject and subjectivity, the four-stage process of the subject-genesis in the educa- tional environment has been modeled. At every stage of subjectivity formation in the particular conditions, it is necessary to use psycho-technologies relating to the actual targets of subject-genesis, which makes it possible to optimize the educational process in the context of the competence approach. The level-based description of the given process is proposed along with the model of its optimization in implementing the target psycho-technologies. The model can be applied both as a methodology basis for the complex development and optimization of psycho-pedagogical facilitation of the sub- jectivity formation, and as a theoretical basis for the educational strategy development and correction in a particular educational establishment.
Optimal Designs for Discriminating Between some Extensions of the Michaelis-Menten Model
Jesus Lopez Fidalgo; Chiara Tommasi; Camelia Trandafir
2005-01-01
In this paper some results on the problem of computing optimal designs for discriminating between rival models are provided. Using T-optimality for two rival models a compound criterion is developed to discriminate between more than two models. Surprising results arise when T-optimal designs are compared with classical c-optimal designs for nonlinear models. In particular, some practical deviations of the Michaelis-Menten model are considered in order to measure and compare efficiencies of di...
DEFF Research Database (Denmark)
Juel-Christiansen, Carsten
2005-01-01
Artiklen fremhæver den visuelle rotation - billeder, tegninger, modeller, værker - som det privilligerede medium i kommunikationen af ideer imellem skabende arkitekter......Artiklen fremhæver den visuelle rotation - billeder, tegninger, modeller, værker - som det privilligerede medium i kommunikationen af ideer imellem skabende arkitekter...
Numerical modeling and optimization of machining duplex stainless steels
Directory of Open Access Journals (Sweden)
Rastee D. Koyee
2015-01-01
Full Text Available The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also
WE-D-BRE-04: Modeling Optimal Concurrent Chemotherapy Schedules
Energy Technology Data Exchange (ETDEWEB)
Jeong, J; Deasy, J O [Memorial Sloan Kettering Cancer Center, New York, NY (United States)
2014-06-15
Purpose: Concurrent chemo-radiation therapy (CCRT) has become a more common cancer treatment option with a better tumor control rate for several tumor sites, including head and neck and lung cancer. In this work, possible optimal chemotherapy schedules were investigated by implementing chemotherapy cell-kill into a tumor response model of RT. Methods: The chemotherapy effect has been added into a published model (Jeong et al., PMB (2013) 58:4897), in which the tumor response to RT can be simulated with the effects of hypoxia and proliferation. Based on the two-compartment pharmacokinetic model, the temporal concentration of chemotherapy agent was estimated. Log cell-kill was assumed and the cell-kill constant was estimated from the observed increase in local control due to concurrent chemotherapy. For a simplified two cycle CCRT regime, several different starting times and intervals were simulated with conventional RT regime (2Gy/fx, 5fx/wk). The effectiveness of CCRT was evaluated in terms of reduction in radiation dose required for 50% of control to find the optimal chemotherapy schedule. Results: Assuming the typical slope of dose response curve (γ50=2), the observed 10% increase in local control rate was evaluated to be equivalent to an extra RT dose of about 4 Gy, from which the cell-kill rate of chemotherapy was derived to be about 0.35. Best response was obtained when chemotherapy was started at about 3 weeks after RT began. As the interval between two cycles decreases, the efficacy of chemotherapy increases with broader range of optimal starting times. Conclusion: The effect of chemotherapy has been implemented into the resource-conservation tumor response model to investigate CCRT. The results suggest that the concurrent chemotherapy might be more effective when delayed for about 3 weeks, due to lower tumor burden and a larger fraction of proliferating cells after reoxygenation.
Equivalent Models and Exact Linearization by the Optimal Control of Monod Kinetics Models
Directory of Open Access Journals (Sweden)
Krassimira Ljakova
2004-10-01
Full Text Available The well-known global biotechnological models are non-linear and nonstationary. In addition the process variables are difficult to measure, the model parameters are time varying, the measurement noise and measurement delay increase the control problems, etc. One possible way to solve some of these problems is to determine the most simple and easy for use equivalent models. The differential geometric approach [DGA] and especially the exact linearization permit an easy application of the optimal control. The approach and its application in the control of the biotechnological process are discussed in the paper. The optimization technique is used for fed-batch and continuos biotechnological processes when the specific growth rate is described by the Monod kinetics.
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.
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.
Modeling, hybridization, and optimal charging of electrical energy storage systems
Parvini, Yasha
The rising rate of global energy demand alongside the dwindling fossil fuel resources has motivated research for alternative and sustainable solutions. Within this area of research, electrical energy storage systems are pivotal in applications including electrified vehicles, renewable power generation, and electronic devices. The approach of this dissertation is to elucidate the bottlenecks of integrating supercapacitors and batteries in energy systems and propose solutions by the means of modeling, control, and experimental techniques. In the first step, the supercapacitor cell is modeled in order to gain fundamental understanding of its electrical and thermal dynamics. The dependence of electrical parameters on state of charge (SOC), current direction and magnitude (20-200 A), and temperatures ranging from -40°C to 60°C was embedded in this computationally efficient model. The coupled electro-thermal model was parameterized using specifically designed temporal experiments and then validated by the application of real world duty cycles. Driving range is one of the major challenges of electric vehicles compared to combustion vehicles. In order to shed light on the benefits of hybridizing a lead-acid driven electric vehicle via supercapacitors, a model was parameterized for the lead-acid battery and combined with the model already developed for the supercapacitor, to build the hybrid battery-supercapacitor model. A hardware in the loop (HIL) setup consisting of a custom built DC/DC converter, micro-controller (muC) to implement the power management strategy, 12V lead-acid battery, and a 16.2V supercapacitor module was built to perform the validation experiments. Charging electrical energy storage systems in an efficient and quick manner, motivated to solve an optimal control problem with the objective of maximizing the charging efficiency for supercapacitors, lead-acid, and lithium ion batteries. Pontryagins minimum principle was used to solve the problems
Production Cost Optimization Model Based on CODP in Mass Customization
Directory of Open Access Journals (Sweden)
Yanhong Qin
2013-01-01
Full Text Available The key for enterprises to implement the postponement strategy is the right decision on the location of Customer Order Decoupling Point (CODP so as to achieve the scope economics of mass customization and scale economics of mass production fully. To deal with production cost optimization problem of postponement system based on various situation of CODP, a basic model of production cost and its M/M/1 extended model are proposed and compared so as to optimize the overall production cost of the postponement system. The production modes can be classified as MTS (make to stock, ATO (assemble to order, MTO (make to order and ETO (engineering to order according to the inventory location, and the postponed production system considered here includes manufacturing cost, semi-finished inventory cost and customer waiting cost caused by delaying delivery. By Matlab simulation, we can compute the optimal location of CODP in each production mode, which can provide some management insight for the manufacturer to decide the right production mode and utilize the resources efficiently.
Optimizing nanomedicine pharmacokinetics using physiologically based pharmacokinetics modelling.
Moss, Darren Michael; Siccardi, Marco
2014-09-01
The delivery of therapeutic agents is characterized by numerous challenges including poor absorption, low penetration in target tissues and non-specific dissemination in organs, leading to toxicity or poor drug exposure. Several nanomedicine strategies have emerged as an advanced approach to enhance drug delivery and improve the treatment of several diseases. Numerous processes mediate the pharmacokinetics of nanoformulations, with the absorption, distribution, metabolism and elimination (ADME) being poorly understood and often differing substantially from traditional formulations. Understanding how nanoformulation composition and physicochemical properties influence drug distribution in the human body is of central importance when developing future treatment strategies. A helpful pharmacological tool to simulate the distribution of nanoformulations is represented by physiologically based pharmacokinetics (PBPK) modelling, which integrates system data describing a population of interest with drug/nanoparticle in vitro data through a mathematical description of ADME. The application of PBPK models for nanomedicine is in its infancy and characterized by several challenges. The integration of property-distribution relationships in PBPK models may benefit nanomedicine research, giving opportunities for innovative development of nanotechnologies. PBPK modelling has the potential to improve our understanding of the mechanisms underpinning nanoformulation disposition and allow for more rapid and accurate determination of their kinetics. This review provides an overview of the current knowledge of nanomedicine distribution and the use of PBPK modelling in the characterization of nanoformulations with optimal pharmacokinetics.
Phenology as a strategy for carbon optimality: a global model
Directory of Open Access Journals (Sweden)
S. Caldararu
2013-09-01
Full Text Available Phenology is essential to our understanding of biogeochemical cycles and the climate system. We develop a global mechanistic model of leaf phenology based on the hypothesis that phenology is a strategy for optimal carbon gain at the canopy level so that trees adjust leaf gains and losses in response to environmental factors such as light, temperature and soil moisture, to achieve maximum carbon assimilation. We fit this model to five years of satellite observations of leaf area index (LAI using a Bayesian fitting algorithm. We show that our model is able to reproduce phenological patterns for all vegetation types and use it to explore variations in growing season length and the climate factors that limit leaf growth for different biomes. Phenology in wet tropical areas is limited by leaf age physiological constraints while at higher latitude leaf seasonality is limited by low temperature and light availability. Leaf growth in grassland regions is limited by water availability but often in combination with other factors. This model will advance the current understanding of phenology for ecosystem carbon models and our ability to predict future phenological behaviour.
Martian Radiative Transfer Modeling Using the Optimal Spectral Sampling Method
Eluszkiewicz, J.; Cady-Pereira, K.; Uymin, G.; Moncet, J.-L.
2005-01-01
The large volume of existing and planned infrared observations of Mars have prompted the development of a new martian radiative transfer model that could be used in the retrievals of atmospheric and surface properties. The model is based on the Optimal Spectral Sampling (OSS) method [1]. The method is a fast and accurate monochromatic technique applicable to a wide range of remote sensing platforms (from microwave to UV) and was originally developed for the real-time processing of infrared and microwave data acquired by instruments aboard the satellites forming part of the next-generation global weather satellite system NPOESS (National Polarorbiting Operational Satellite System) [2]. As part of our on-going research related to the radiative properties of the martian polar caps, we have begun the development of a martian OSS model with the goal of using it to perform self-consistent atmospheric corrections necessary to retrieve caps emissivity from the Thermal Emission Spectrometer (TES) spectra. While the caps will provide the initial focus area for applying the new model, it is hoped that the model will be of interest to the wider Mars remote sensing community.
Monte Carlo modeling and optimization of buffer gas positron traps
Marjanović, Srđan; Petrović, Zoran Lj
2017-02-01
Buffer gas positron traps have been used for over two decades as the prime source of slow positrons enabling a wide range of experiments. While their performance has been well understood through empirical studies, no theoretical attempt has been made to quantitatively describe their operation. In this paper we apply standard models as developed for physics of low temperature collision dominated plasmas, or physics of swarms to model basic performance and principles of operation of gas filled positron traps. The Monte Carlo model is equipped with the best available set of cross sections that were mostly derived experimentally by using the same type of traps that are being studied. Our model represents in realistic geometry and fields the development of the positron ensemble from the initial beam provided by the solid neon moderator through voltage drops between the stages of the trap and through different pressures of the buffer gas. The first two stages employ excitation of N2 with acceleration of the order of 10 eV so that the trap operates under conditions when excitation of the nitrogen reduces the energy of the initial beam to trap the positrons without giving them a chance to become annihilated following positronium formation. The energy distribution function develops from the assumed distribution leaving the moderator, it is accelerated by the voltage drops and forms beams at several distinct energies. In final stages the low energy loss collisions (vibrational excitation of CF4 and rotational excitation of N2) control the approach of the distribution function to a Maxwellian at room temperature but multiple non-Maxwellian groups persist throughout most of the thermalization. Optimization of the efficiency of the trap may be achieved by changing the pressure and voltage drops and also by selecting to operate in a two stage mode. The model allows quantitative comparisons and test of optimization as well as development of other properties.
Traveling waves in an optimal velocity model of freeway traffic
Berg, Peter; Woods, Andrew
2001-03-01
Car-following models provide both a tool to describe traffic flow and algorithms for autonomous cruise control systems. Recently developed optimal velocity models contain a relaxation term that assigns a desirable speed to each headway and a response time over which drivers adjust to optimal velocity conditions. These models predict traffic breakdown phenomena analogous to real traffic instabilities. In order to deepen our understanding of these models, in this paper, we examine the transition from a linear stable stream of cars of one headway into a linear stable stream of a second headway. Numerical results of the governing equations identify a range of transition phenomena, including monotonic and oscillating travelling waves and a time- dependent dispersive adjustment wave. However, for certain conditions, we find that the adjustment takes the form of a nonlinear traveling wave from the upstream headway to a third, intermediate headway, followed by either another traveling wave or a dispersive wave further downstream matching the downstream headway. This intermediate value of the headway is selected such that the nonlinear traveling wave is the fastest stable traveling wave which is observed to develop in the numerical calculations. The development of these nonlinear waves, connecting linear stable flows of two different headways, is somewhat reminiscent of stop-start waves in congested flow on freeways. The different types of adjustments are classified in a phase diagram depending on the upstream and downstream headway and the response time of the model. The results have profound consequences for autonomous cruise control systems. For an autocade of both identical and different vehicles, the control system itself may trigger formations of nonlinear, steep wave transitions. Further information is available [Y. Sugiyama, Traffic and Granular Flow (World Scientific, Singapore, 1995), p. 137].
Considerations for parameter optimization and sensitivity in climate models.
Neelin, J David; Bracco, Annalisa; Luo, Hao; McWilliams, James C; Meyerson, Joyce E
2010-12-14
Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention--here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.
Optimization models using fuzzy sets and possibility theory
Orlovski, S
1987-01-01
Optimization is of central concern to a number of discip lines. Operations Research and Decision Theory are often consi dered to be identical with optimizationo But also in other areas such as engineering design, regional policy, logistics and many others, the search for optimal solutions is one of the prime goals. The methods and models which have been used over the last decades in these areas have primarily been "hard" or "crisp", i. e. the solutions were considered to be either fea sible or unfeasible, either above a certain aspiration level or below. This dichotomous structure of methods very often forced the modeller to approximate real problem situations of the more-or-less type by yes-or-no-type models, the solutions of which might turn out not to be the solutions to the real prob lems. This is particularly true if the problem under considera tion includes vaguely defined relationships, human evaluations, uncertainty due to inconsistent or incomplete evidence, if na tural language has to be...
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.
The role of optimization in structural model refinement
Lehman, L. L.
1984-01-01
To evaluate the role that optimization can play in structural model refinement, it is necessary to examine the existing environment for the structural design/structural modification process. The traditional approach to design, analysis, and modification is illustrated. Typically, a cyclical path is followed in evaluating and refining a structural system, with parallel paths existing between the real system and the analytical model of the system. The major failing of the existing approach is the rather weak link of communication between the cycle for the real system and the cycle for the analytical model. Only at the expense of much human effort can data sharing and comparative evaluation be enhanced for the two parallel cycles. Much of the difficulty can be traced to the lack of a user-friendly, rapidly reconfigurable engineering software environment for facilitating data and information exchange. Until this type of software environment becomes readily available to the majority of the engineering community, the role of optimization will not be able to reach its full potential and engineering productivity will continue to suffer. A key issue in current engineering design, analysis, and test is the definition and development of an integrated engineering software support capability. The data and solution flow for this type of integrated engineering analysis/refinement system is shown.
Evolutionary optimization of a hierarchical object recognition model.
Schneider, Georg; Wersing, Heiko; Sendhoff, Bernhard; Körner, Edgar
2005-06-01
A major problem in designing artificial neural networks is the proper choice of the network architecture. Especially for vision networks classifying three-dimensional (3-D) objects this problem is very challenging, as these networks are necessarily large and therefore the search space for defining the needed networks is of a very high dimensionality. This strongly increases the chances of obtaining only suboptimal structures from standard optimization algorithms. We tackle this problem in two ways. First, we use biologically inspired hierarchical vision models to narrow the space of possible architectures and to reduce the dimensionality of the search space. Second, we employ evolutionary optimization techniques to determine optimal features and nonlinearities of the visual hierarchy. Here, we especially focus on higher order complex features in higher hierarchical stages. We compare two different approaches to perform an evolutionary optimization of these features. In the first setting, we directly code the features into the genome. In the second setting, in analogy to an ontogenetical development process, we suggest the new method of an indirect coding of the features via an unsupervised learning process, which is embedded into the evolutionary optimization. In both cases the processing nonlinearities are encoded directly into the genome and are thus subject to optimization. The fitness of the individuals for the evolutionary selection process is computed by measuring the network classification performance on a benchmark image database. Here, we use a nearest-neighbor classification approach, based on the hierarchical feature output. We compare the found solutions with respect to their ability to generalize. We differentiate between a first- and a second-order generalization. The first-order generalization denotes how well the vision system, after evolutionary optimization of the features and nonlinearities using a database A, can classify previously unseen test
Multi-model groundwater-management optimization: reconciling disparate conceptual models
Timani, Bassel; Peralta, Richard
2015-09-01
Disagreement among policymakers often involves policy issues and differences between the decision makers' implicit utility functions. Significant disagreement can also exist concerning conceptual models of the physical system. Disagreement on the validity of a single simulation model delays discussion on policy issues and prevents the adoption of consensus management strategies. For such a contentious situation, the proposed multi-conceptual model optimization (MCMO) can help stakeholders reach a compromise strategy. MCMO computes mathematically optimal strategies that simultaneously satisfy analogous constraints and bounds in multiple numerical models that differ in boundary conditions, hydrogeologic stratigraphy, and discretization. Shadow prices and trade-offs guide the process of refining the first MCMO-developed `multi-model strategy into a realistic compromise management strategy. By employing automated cycling, MCMO is practical for linear and nonlinear aquifer systems. In this reconnaissance study, MCMO application to the multilayer Cache Valley (Utah and Idaho, USA) river-aquifer system employs two simulation models with analogous background conditions but different vertical discretization and boundary conditions. The objective is to maximize additional safe pumping (beyond current pumping), subject to constraints on groundwater head and seepage from the aquifer to surface waters. MCMO application reveals that in order to protect the local ecosystem, increased groundwater pumping can satisfy only 40 % of projected water demand increase. To explore the possibility of increasing that pumping while protecting the ecosystem, MCMO clearly identifies localities requiring additional field data. MCMO is applicable to other areas and optimization problems than used here. Steps to prepare comparable sub-models for MCMO use are area-dependent.
Optimization of benzoxazinones as natural herbicide models by lipophilicity enhancement.
Macías, Francisco A; Marín, David; Oliveros-Bastidas, Alberto; Molinillo, José M G
2006-12-13
Benzoxazinones are plant allelochemicals well-known for their phytotoxic activity and for taking part in the defense strategies of Gramineae, Ranunculaceae, and Scrophulariceae plants. These properties, in addition to the recently optimized methodologies for their large-scale isolation and synthesis, have made some derivatives of natural products, 2,4-dihydroxy-(2H)-1,4-benzoxazin-3-(4H)-one (DIBOA) and its 7-methoxy analogue (DIMBOA), successful templates in the search for natural herbicide models. These new chemicals should be part of integrated methodologies for weed control. In ongoing research about the structure-activity relationships of benzoxazinones and the structural requirements for their phytotoxicity enhancement and after characterization of the optimal structural features, a new generation of chemicals with enhanced lipophilicity was developed. They were tested on selected standard target species and weeds in the search for the optimal aqueous solubility-lipophilicity rate for phytotoxicity. This physical parameter is known to be crucial in modern drug and agrochemical design strategies. The new compounds obtained in this way had interesting phytotoxicity profiles, empowering the phytotoxic effect of the starting benzoxazinone template in some cases. Quantitative structure-activity relationships were obtained by bioactivity-molecular parameters correlations. Because optimal lipophilicity values for phytotoxicity vary with the tested plant, these new derivatives constitute a more selective way to take advantage of benzoxazinone phytotoxic capabilities.
Redundancy Level Optimization in Modular Software System Models using ABC
Directory of Open Access Journals (Sweden)
Tarun Kumar Sharma
2014-03-01
Full Text Available The performance of optimization algorithms is problem dependent and as per no free lunch theorem, there exists no such algorithm which can be efficiently applied to every type of problem(s. However, we can modify the algorithm/ technique in a manner such that it is able to deal with a maximum type of problems. In this study we have modified the structure of basic Artificial Bee Colony (ABC, a recently proposed metaheuristic algorithm based on the concept of swarm intelligence to optimize the models of software reliability. The modified variant of ABC is termed as balanced ABC (B-ABC. The simulated results show the efficiency and capability of the variant to solve such type of the problems.
Error Modeling and Design Optimization of Parallel Manipulators
DEFF Research Database (Denmark)
Wu, Guanglei
challenges due to their highly nonlinear behaviors, thus, the parameter and performance analysis, especially the accuracy and stiness, are particularly important. Toward the requirements of robotic technology such as light weight, compactness, high accuracy and low energy consumption, utilizing optimization...... technique in the design procedure is a suitable approach to handle these complex tasks. As there is no unied design guideline for the parallel manipulators, the study described in this thesis aims to provide a systematic analysis for this type of mechanisms in the early design stage, focusing on accuracy...... analysis and design optimization. The proposed approach is illustrated with the planar and spherical parallel manipulators. The geometric design, kinematic and dynamic analysis, kinetostatic modeling and stiness analysis are also presented. Firstly, the study on the geometric architecture and kinematic...
On the synthesis of the pilot optimal control model
Directory of Open Access Journals (Sweden)
Adrian TOADER
2011-09-01
Full Text Available The study continues some work of the authors, this time performing a synthesis of optimal control model of the human pilot in systems with input delay, by removing the Padé or Hess approximations characterizing the pilot structural central nervous block and their introduction as a pure delay block. On the one hand, the method ensures a better accuracy of synthesis and on the other hand is advantageous with respect to general results to date for time delay systems since: a the optimal control law is given explicitly and b the Riccati equations for the gain matrices do not contain any time advanced or delayed arguments. The approach is stimulated by recent works of M. Basin and his collaborators.
Power Consumption in Refrigeration Systems - Modeling for Optimization
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Larsen, Lars F. S.; Skovrup, Morten Juel
2011-01-01
Refrigeration systems consume a substantial amount of energy. Taking for instance supermarket refrigeration systems as an example they can account for up to 50−80% of the total energy consumption in the supermarket. Due to the thermal capacity made up by the refrigerated goods in the system...... there is a possibility for optimizing the power consumption by utilizing load shifting strategies. This paper describes the dynamics and the modeling of a vapor compression refrigeration system needed for sufficiently realistic estimation of the power consumption and its minimization. This leads to a non-convex function...... with possibly multiple extrema. Such a function can not directly be optimized by standard methods and a qualitative analysis of the system’s constraints is presented. The description of power consumption contains nonlinear terms which are approximated by linear functions in the control variables and the error...
Optimal dividends in the Brownian motion risk model with interest
Fang, Ying; Wu, Rong
2009-07-01
In this paper, we consider a Brownian motion risk model, and in addition, the surplus earns investment income at a constant force of interest. The objective is to find a dividend policy so as to maximize the expected discounted value of dividend payments. It is well known that optimality is achieved by using a barrier strategy for unrestricted dividend rate. However, ultimate ruin of the company is certain if a barrier strategy is applied. In many circumstances this is not desirable. This consideration leads us to impose a restriction on the dividend stream. We assume that dividends are paid to the shareholders according to admissible strategies whose dividend rate is bounded by a constant. Under this additional constraint, we show that the optimal dividend strategy is formed by a threshold strategy.
Using Optimization Models for Scheduling in Enterprise Resource Planning Systems
Directory of Open Access Journals (Sweden)
Frank Herrmann
2016-03-01
Full Text Available Companies often use specially-designed production systems and change them from time to time. They produce small batches in order to satisfy specific demands with the least tardiness. This imposes high demands on high-performance scheduling algorithms which can be rapidly adapted to changes in the production system. As a solution, this paper proposes a generic approach: solutions were obtained using a widely-used commercially-available tool for solving linear optimization models, which is available in an Enterprise Resource Planning System (in the SAP system for example or can be connected to it. In a real-world application of a flow shop with special restrictions this approach is successfully used on a standard personal computer. Thus, the main implication is that optimal scheduling with a commercially-available tool, incorporated in an Enterprise Resource Planning System, may be the best approach.
Vehicle Propulsion Systems Introduction to Modeling and Optimization
Guzzella, Lino
2013-01-01
This text provides an introduction to the mathematical modeling and subsequent optimization of vehicle propulsion systems and their supervisory control algorithms. Automobiles are responsible for a substantial part of the world's consumption of primary energy, mostly fossil liquid hydrocarbons and the reduction of the fuel consumption of these vehicles has become a top priority. Increasing concerns over fossil fuel consumption and the associated environmental impacts have motivated many groups in industry and academia to propose new propulsion systems and to explore new optimization methodologies. This third edition has been prepared to include many of these developments. In the third edition, exercises are included at the end of each chapter and the solutions are available on the web.
Application of Dual Model to Animal Feed Formulation Optimizing System
Institute of Scientific and Technical Information of China (English)
XIONG Ben-hai; LUO Qing-yao; PANG Zhi-hong
2003-01-01
This study introduced a dual model on an original linear programming to obtain those shadowprices of resources that take part in optimizing. Of feed formulation, the shadow prices of nutrient resourcesshow their influencing degree on a diet last cost when increasing or decreasing expected diet nutrient values.The higher the shadow price of one nutrient resource, the more obvious its influencing action on a diet lastcost. When the shadow price of a kind of resource equals "zero", it means that reaching of this nutrient valuedoes not have influence on a special diet last cost within a particular value range. At the same time, this paperdiscussed the future development direction of feed formulation optimizing techniques in China.
Optimal Control of Drug Therapy in a Hepatitis B Model
Directory of Open Access Journals (Sweden)
Jonathan E. Forde
2016-08-01
Full Text Available Combination antiviral drug therapy improves the survival rates of patients chronically infected with hepatitis B virus by controlling viral replication and enhancing immune responses. Some of these drugs have side effects that make them unsuitable for long-term administration. To address the trade-off between the positive and negative effects of the combination therapy, we investigated an optimal control problem for a delay differential equation model of immune responses to hepatitis virus B infection. Our optimal control problem investigates the interplay between virological and immunomodulatory effects of therapy, the control of viremia and the administration of the minimal dosage over a short period of time. Our numerical results show that the high drug levels that induce immune modulation rather than suppression of virological factors are essential for the clearance of hepatitis B virus.
Dynamics of underactuated multibody systems modeling, control and optimal design
Seifried, Robert
2014-01-01
Underactuated multibody systems are intriguing mechatronic systems, as they possess fewer control inputs than degrees of freedom. Some examples are modern light-weight flexible robots and articulated manipulators with passive joints. This book investigates such underactuated multibody systems from an integrated perspective. This includes all major steps from the modeling of rigid and flexible multibody systems, through nonlinear control theory, to optimal system design. The underlying theories and techniques from these different fields are presented using a self-contained and unified approach and notation system. Subsequently, the book focuses on applications to large multibody systems with multiple degrees of freedom, which require a combination of symbolical and numerical procedures. Finally, an integrated, optimization-based design procedure is proposed, whereby both structural and control design are considered concurrently. Each chapter is supplemented by illustrated examples.
Models and Algorithms for Container Vessel Stowage Optimization
DEFF Research Database (Denmark)
Delgado-Ortegon, Alberto
Containerized seaborne trade has played a key role in the transformation of the global economy in the last 50 years. In liner shipping companies, at the heart of this operation, several planning decisions are made based on the stowage capabilities of container vessels, from strategic decisions (e.......g., selection of vessels to buy that satisfy specific demands), through to operational decisions (e.g., selection of containers that optimize revenue, and stowing those containers into a vessel). This thesis addresses the question of whether it is possible to formulate stowage optimization models...... container of those to be loaded in a port should be placed in a vessel, i.e., to generate stowage plans. This thesis explores two different approaches to solve this problem, both follow a 2-phase decomposition that assigns containers to vessel sections in the first phase, i.e., master planning...
Using Cotton Model Simulations to Estimate Optimally Profitable Irrigation Strategies
Mauget, S. A.; Leiker, G.; Sapkota, P.; Johnson, J.; Maas, S.
2011-12-01
In recent decades irrigation pumping from the Ogallala Aquifer has led to declines in saturated thickness that have not been compensated for by natural recharge, which has led to questions about the long-term viability of agriculture in the cotton producing areas of west Texas. Adopting irrigation management strategies that optimize profitability while reducing irrigation waste is one way of conserving the aquifer's water resource. Here, a database of modeled cotton yields generated under drip and center pivot irrigated and dryland production scenarios is used in a stochastic dominance analysis that identifies such strategies under varying commodity price and pumping cost conditions. This database and analysis approach will serve as the foundation for a web-based decision support tool that will help producers identify optimal irrigation treatments under specified cotton price, electricity cost, and depth to water table conditions.
Modeling and Structural Optimization of Solid Oxide Fuel Cells
DEFF Research Database (Denmark)
Panagakos, Grigorios
The research conducted in the context of this PhD, lies on the cross section between multi-scale modeling of flow in porous media, electrochemical diffusion and reaction, in combination with Shape and Structural Optimization techniques. More specifi-cally, we have followed two lines of action...... requirements. On the one hand, it needs to secure the intake of fuel into the cell, fact that would require an as low hydraulic resistance as possible, i.e. ideally an open channel and on the other hand to exhibit an as high as possible electronic conductance, which in the ideal case would mean an area blocked...... completely by a material with high conductivity such as coated stainless steel. The balance between these two competing, oppositely driving forces, indicate that there should be a design that satisfies in the best way both. Similar problems have been successfully dealt by structural-topology optimization...
Design Oriented Structural Modeling for Airplane Conceptual Design Optimization
Livne, Eli
1999-01-01
The main goal for research conducted with the support of this grant was to develop design oriented structural optimization methods for the conceptual design of airplanes. Traditionally in conceptual design airframe weight is estimated based on statistical equations developed over years of fitting airplane weight data in data bases of similar existing air- planes. Utilization of such regression equations for the design of new airplanes can be justified only if the new air-planes use structural technology similar to the technology on the airplanes in those weight data bases. If any new structural technology is to be pursued or any new unconventional configurations designed the statistical weight equations cannot be used. In such cases any structural weight estimation must be based on rigorous "physics based" structural analysis and optimization of the airframes under consideration. Work under this grant progressed to explore airframe design-oriented structural optimization techniques along two lines of research: methods based on "fast" design oriented finite element technology and methods based on equivalent plate / equivalent shell models of airframes, in which the vehicle is modelled as an assembly of plate and shell components, each simulating a lifting surface or nacelle / fuselage pieces. Since response to changes in geometry are essential in conceptual design of airplanes, as well as the capability to optimize the shape itself, research supported by this grant sought to develop efficient techniques for parametrization of airplane shape and sensitivity analysis with respect to shape design variables. Towards the end of the grant period a prototype automated structural analysis code designed to work with the NASA Aircraft Synthesis conceptual design code ACS= was delivered to NASA Ames.
Generalized PSF modeling for optimized quantitation in PET imaging
Ashrafinia, Saeed; Mohy-ud-Din, Hassan; Karakatsanis, Nicolas A.; Jha, Abhinav K.; Casey, Michael E.; Kadrmas, Dan J.; Rahmim, Arman
2017-06-01
modeling does not offer optimized PET quantitation, and that PSF overestimation may provide enhanced SUV quantitation. Furthermore, generalized PSF modeling may provide a valuable approach for quantitative tasks such as treatment-response assessment and prognostication.
Modeling and optimization of parallel and distributed embedded systems
Munir, Arslan; Ranka, Sanjay
2016-01-01
This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability.
Model-based optimization of phased array ultrasonic testing
Institute of Scientific and Technical Information of China (English)
Sung-Jin; Song; Hak-Joon; Kim; Suk-Chull; Kang; Sung-Sik; Kang; Kyungcho; Kim; Myung-Ho; Song
2010-01-01
Simulation of phased array beams in dovetail and austenitic welds is conducted to optimize the setup of phased array ultrasonic testing(PAUT).To simulate the beam in such material with complex geometry or with characteristic of anisotropy and inhomogeneity, firstly,linear phased multi-Gaussian beam(LPMGB) models are introduced and discussed. Then,in the case of dovetail,wedge is designed to maximize the stable amplitude of the beam along the steering path;in the case of austenitic weld,modified focal law...
An Optimization Model for A Proposed Trigeneration System
Directory of Open Access Journals (Sweden)
Bulut Kezban
2016-11-01
Full Text Available The combined cooling, heating, and power (CCHP systems play an important role in the reduction of carbon emissions and the increase of energy efficiency for businesses and social organizations. Because of its potentials, tri-generation system has become a preference during the last decade. In this paper a hybrid trigeneration system is proposed for a university campus. The system is also important because it uses renewable energy sources as well as non-renewable energy sources. The objective of this paper is to propose an optimization model for this new Tri-generation system
Off-road vehicle dynamics analysis, modelling and optimization
Taghavifar, Hamid
2017-01-01
This book deals with the analysis of off-road vehicle dynamics from kinetics and kinematics perspectives and the performance of vehicle traversing over rough and irregular terrain. The authors consider the wheel performance, soil-tire interactions and their interface, tractive performance of the vehicle, ride comfort, stability over maneuvering, transient and steady state conditions of the vehicle traversing, modeling the aforementioned aspects and optimization from energetic and vehicle mobility perspectives. This book brings novel figures for the transient dynamics and original wheel terrain dynamics at on-the-go condition.
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
Modelling, design, and optimization of net-zero energy buildings
Athienitis, Andreas
2015-01-01
Building energy design is currently going through a period of major changes. One key factor of this is the adoption of net-zero energy as a long term goal for new buildings in most developed countries. To achieve this goal a lot of research is needed to accumulate knowledge and to utilize it in practical applications. In this book, accomplished international experts present advanced modeling techniques as well as in-depth case studies in order to aid designers in optimally using simulation tools for net-zero energy building design. The strategies and technologies discussed in this book are, ho
Power Consumption in Refrigeration Systems - Modeling for Optimization
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Larsen, Lars F. S.; Skovrup, Morten Juel
2011-01-01
Refrigeration systems consume a substantial amount of energy. Taking for instance supermarket refrigeration systems as an example they can account for up to 50−80% of the total energy consumption in the supermarket. Due to the thermal capacity made up by the refrigerated goods in the system...... there is a possibility for optimizing the power consumption by utilizing load shifting strategies. This paper describes the dynamics and the modeling of a vapor compression refrigeration system needed for sufficiently realistic estimation of the power consumption and its minimization. This leads to a non-convex function...
Particle Swarm Optimization with Watts-Strogatz Model
Zhu, Zhuanghua
Particle swarm optimization (PSO) is a popular swarm intelligent methodology by simulating the animal social behaviors. Recent study shows that this type of social behaviors is a complex system, however, for most variants of PSO, all individuals lie in a fixed topology, and conflict this natural phenomenon. Therefore, in this paper, a new variant of PSO combined with Watts-Strogatz small-world topology model, called WSPSO, is proposed. In WSPSO, the topology is changed according to Watts-Strogatz rules within the whole evolutionary process. Simulation results show the proposed algorithm is effective and efficient.
An Optimal Capacity Planning Model for General Cargo Seaport
Directory of Open Access Journals (Sweden)
Čedomir Dundović
2012-10-01
Full Text Available In this paper the application of the queuing the01y in optimalcapacity planning for general cargo seaport is presented.The seaport as a queuing syslem is defined and tlws, on the basisof the arrival and serviced number of ships in an obsen•edtime unit, the appropriate operating indicators of a port systemare calculated. Using the model of total port costs, the munberof berths and cranes on the berth can be determined wherebythe optimal port system functioning is achieved.
Modeling Microinverters and DC Power Optimizers in PVWatts
Energy Technology Data Exchange (ETDEWEB)
MacAlpine, S. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Deline, C. [National Renewable Energy Lab. (NREL), Golden, CO (United States)
2015-02-01
Module-level distributed power electronics including microinverters and DC power optimizers are increasingly popular in residential and commercial PV systems. Consumers are realizing their potential to increase design flexibility, monitor system performance, and improve energy capture. It is becoming increasingly important to accurately model PV systems employing these devices. This document summarizes existing published documents to provide uniform, impartial recommendations for how the performance of distributed power electronics can be reflected in NREL's PVWatts calculator (http://pvwatts.nrel.gov/).
An Optimization Model for Product Placement on Product Listing Pages
Directory of Open Access Journals (Sweden)
Yan-Kwang Chen
2014-01-01
Full Text Available The design of product listing pages is a key component of Website design because it has significant influence on the sales volume on a Website. This study focuses on product placement in designing product listing pages. Product placement concerns how venders of online stores place their products over the product listing pages for maximization of profit. This problem is very similar to the offline shelf management problem. Since product information sources on a Web page are typically communicated through the text and image, visual stimuli such as color, shape, size, and spatial arrangement often have an effect on the visual attention of online shoppers and, in turn, influence their eventual purchase decisions. In view of the above, this study synthesizes the visual attention literature and theory of shelf-space allocation to develop a mathematical programming model with genetic algorithms for finding optimal solutions to the focused issue. The validity of the model is illustrated with example problems.
Semantic Enterprise Optimizer and Coexistence of Data Models
Directory of Open Access Journals (Sweden)
P. A. Sundararajan
2012-09-01
Full Text Available The authors propose a semantic ontology–driven enterprise data–model architecture for interoperability, integration, and adaptability for evolution, by autonomic agent-driven intelligent design of logical as well as physical data models in a heterogeneous distributed enterprise through its life cycle. An enterprise-standard ontology (in Web Ontology Language [OWL] and Semantic Web Rule Language [SWRL] for data is required to enable an automated data platform that adds life-cycle activities to the current Microsoft Enterprise Search and extend Microsoft SQL Server through various engines for unstructured data types, as well as many domain types that are configurable by users through a Semantic- query optimizer, and using Microsoft Office SharePoint Server (MOSS as a content and metadata repository to tie all these components together.
Mathematical Modelling and Parameter Optimization of Pulsating Heat Pipes
Yang, Xin-She; Luan, Tao; Koziel, Slawomir
2014-01-01
Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, two-phase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.
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.
Performance optimization of Jatropha biodiesel engine model using Taguchi approach
Energy Technology Data Exchange (ETDEWEB)
Ganapathy, T.; Murugesan, K.; Gakkhar, R.P. [Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee 247 667 (India)
2009-11-15
This paper proposes a methodology for thermodynamic model analysis of Jatropha biodiesel engine in combination with Taguchi's optimization approach to determine the optimum engine design and operating parameters. A thermodynamic model based on two-zone Weibe's heat release function has been employed to simulate the Jatropha biodiesel engine performance. Among the important engine design and operating parameters 10 critical parameters were selected assuming interactions between the pair of parameters. Using linear graph theory and Taguchi method an L{sub 16} orthogonal array has been utilized to determine the engine test trials layout. In order to maximize the performance of Jatropha biodiesel engine the signal to noise ratio (SNR) related to higher-the-better (HTB) quality characteristics has been used. The present methodology correctly predicted the compression ratio, Weibe's heat release constants and combustion zone duration as the critical parameters that affect the performance of the engine compared to other parameters. (author)
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Directory of Open Access Journals (Sweden)
Feipeng Guo
2013-10-01
Full Text Available With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic method for attributes reduction based on rough set theory and principal component analysis was proposed which can reduce multiple attributes into some principal components, yet retaining effective evaluation information. Finally, it used improved BP neural network which has self-learning function to select partners. The empirical analysis on an agricultural enterprise shows that this model is effective and feasible for practical partner selection.
Computational modeling, optimization and manufacturing simulation of advanced engineering materials
2016-01-01
This volume presents recent research work focused in the development of adequate theoretical and numerical formulations to describe the behavior of advanced engineering materials. Particular emphasis is devoted to applications in the fields of biological tissues, phase changing and porous materials, polymers and to micro/nano scale modeling. Sensitivity analysis, gradient and non-gradient based optimization procedures are involved in many of the chapters, aiming at the solution of constitutive inverse problems and parameter identification. All these relevant topics are exposed by experienced international and inter institutional research teams resulting in a high level compilation. The book is a valuable research reference for scientists, senior undergraduate and graduate students, as well as for engineers acting in the area of computational material modeling.
Resonant cavity light-emitting diodes: modeling, design, and optimization
Dumitrescu, Mihail M.; Sipila, Pekko; Vilokkinen, Ville; Toikkanen, L.; Melanen, Petri; Saarinen, Mika J.; Orsila, Seppo; Savolainen, Pekka; Toivonen, Mika; Pessa, Markus
2000-02-01
Monolithic top emitting resonant cavity light-emitting diodes operating in the 650 and 880 nm ranges have been prepared using solid-source molecular beam epitaxy growth. Transfer matrix based modeling together with a self- consistent model have been sued to optimize the devices' performances. The design of the layer structure and doping profile was assisted by computer simulations that enabled many device improvements. Among the most significant ones intermediate-composition barrier-reduction layers were introduced in the DBR mirrors for improving the I-V characteristics and the cavity and mirrors were detuned aiming at maximum extraction efficiency. The fabricated devices showed line widths below 15 nm, CW light power output of 8 and 22.5 mW, and external quantum efficiencies of 3 percent and 14.1 percent in the 650 nm and 880 nm ranges, respectively - while the simulations indicate significant performance improvement possibilities.
Three essays on multi-level optimization models and applications
Rahdar, Mohammad
The general form of a multi-level mathematical programming problem is a set of nested optimization problems, in which each level controls a series of decision variables independently. However, the value of decision variables may also impact the objective function of other levels. A two-level model is called a bilevel model and can be considered as a Stackelberg game with a leader and a follower. The leader anticipates the response of the follower and optimizes its objective function, and then the follower reacts to the leader's action. The multi-level decision-making model has many real-world applications such as government decisions, energy policies, market economy, network design, etc. However, there is a lack of capable algorithms to solve medium and large scale these types of problems. The dissertation is devoted to both theoretical research and applications of multi-level mathematical programming models, which consists of three parts, each in a paper format. The first part studies the renewable energy portfolio under two major renewable energy policies. The potential competition for biomass for the growth of the renewable energy portfolio in the United States and other interactions between two policies over the next twenty years are investigated. This problem mainly has two levels of decision makers: the government/policy makers and biofuel producers/electricity generators/farmers. We focus on the lower-level problem to predict the amount of capacity expansions, fuel production, and power generation. In the second part, we address uncertainty over demand and lead time in a multi-stage mathematical programming problem. We propose a two-stage tri-level optimization model in the concept of rolling horizon approach to reducing the dimensionality of the multi-stage problem. In the third part of the dissertation, we introduce a new branch and bound algorithm to solve bilevel linear programming problems. The total time is reduced by solving a smaller relaxation
Optimization Model for Refinery Hydrogen Networks Part I
Directory of Open Access Journals (Sweden)
Enrique E. Tarifa
2016-10-01
Full Text Available Petroleum refineries have many process units that consume hydrogen.These process units are distributed in different places everywhere in the refinery.In order to feed them, it is necessary to have sources capable of supplying, in amount and quality, the hydrogen that every consuming unit needs.It is also needed to have a distribution network that it is correctly designed and which operation is adjusted in an optimal manner to the changing conditions of the refinery.This involves the minimization of the cost of installation and operation of the hydrogen network.The installation cost is dominated by the amount of pipelines, compressors and purifying units; while the cost of operation is dominated by the amount of fresh hydrogen that the plant consumes.In this work a mathematical model is developed for a hydrogen network,which is adapted to the different information levels available in the different stages of design of that system.The model is currently in use in the YPFLuján de Cuyo refinery (Mendoza, Argentina. In this first part, the basic model is presented; whereas in a second part, the model is enlarged to accommodate the incorporation of purifying units and new compressors
Optimal vibration control of curved beams using distributed parameter models
Liu, Fushou; Jin, Dongping; Wen, Hao
2016-12-01
The design of linear quadratic optimal controller using spectral factorization method is studied for vibration suppression of curved beam structures modeled as distributed parameter models. The equations of motion for active control of the in-plane vibration of a curved beam are developed firstly considering its shear deformation and rotary inertia, and then the state space model of the curved beam is established directly using the partial differential equations of motion. The functional gains for the distributed parameter model of curved beam are calculated by extending the spectral factorization method. Moreover, the response of the closed-loop control system is derived explicitly in frequency domain. Finally, the suppression of the vibration at the free end of a cantilevered curved beam by point control moment is studied through numerical case studies, in which the benefit of the presented method is shown by comparison with a constant gain velocity feedback control law, and the performance of the presented method on avoidance of control spillover is demonstrated.
Kolossváry, István
2012-01-01
We propose a new way of looking at global optimization of off-lattice protein models. We present a dual optimization concept of predicting optimal sequences as well as optimal folds. We validate the utility of the recently introduced hidden-force Monte Carlo optimization algorithm by finding significantly lower energy folds for minimalist protein models than previously reported. Further, we also find the protein sequence that yields the lowest energy fold amongst all sequences for a given chain length and residue mixture. In particular, for protein models with a binary sequence, we show that the sequence-optimized folds form more compact cores than the lowest energy folds of the historically fixed, Fibonacci-series sequences of chain lengths of 13, 21, 34, 55, and 89. We emphasize that while the protein model we used is minimalist, the methodology is applicable to detailed protein models, and sequence optimization may yield novel folds and aid de novo protein design.
Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory
Dees, Patrick D.; Zwack, Matthew R.; Edwards, Stephen; Steffens, Michael
2016-01-01
It has been well documented that decisions made in the early stages of Conceptual and Pre-Conceptual design commit up to 80% of total Life-Cycle Cost (LCC) while engineers know the least about the product they are designing [1]. Once within Preliminary and Detailed design however, making changes to the design becomes far more difficult to enact in both cost and schedule. Primarily this has been due to a lack of detailed data usually uncovered later during the Preliminary and Detailed design phases. In our current budget-constrained environment, making decisions within Conceptual and Pre-Conceptual design which minimize LCC while meeting requirements is paramount to a program's success. Within the arena of launch vehicle design, optimizing the ascent trajectory is critical for minimizing the costs present within such concerns as propellant, aerodynamic, aeroheating, and acceleration loads while meeting requirements such as payload delivered to a desired orbit. In order to optimize the vehicle design its constraints and requirements must be known, however as the design cycle proceeds it is all but inevitable that the conditions will change. Upon that change, the previously optimized trajectory may no longer be optimal, or meet design requirements. The current paradigm for adjusting to these updates is generating point solutions for every change in the design's requirements [2]. This can be a tedious, time-consuming task as changes in virtually any piece of a launch vehicle's design can have a disproportionately large effect on the ascent trajectory, as the solution space of the trajectory optimization problem is both non-linear and multimodal [3]. In addition, an industry standard tool, Program to Optimize Simulated Trajectories (POST), requires an expert analyst to produce simulated trajectories that are feasible and optimal [4]. In a previous publication the authors presented a method for combatting these challenges [5]. In order to bring more detailed information
Performance Optimization of NEMO Oceanic Model at High Resolution
Epicoco, Italo; Mocavero, Silvia; Aloisio, Giovanni
2014-05-01
The NEMO oceanic model is based on the Navier-Stokes equations along with a nonlinear equation of state, which couples the two active tracers (temperature and salinity) to the fluid velocity. The code is written in Fortan 90 and parallelized using MPI. The resolution of the global ocean models used today for climate change studies limits the prediction accuracy. To overcome this limit, a new high-resolution global model, based on NEMO, simulating at 1/16° and 100 vertical levels has been developed at CMCC. The model is computational and memory intensive, so it requires many resources to be run. An optimization activity is needed. The strategy requires a preliminary analysis to highlight scalability bottlenecks. It has been performed on a SandyBridge architecture at CMCC. An efficiency of 48% on 7K cores (the maximum available) has been achieved. The analysis has been also carried out at routine level, so that the improvement actions could be designed for the entire code or for the single kernel. The analysis highlighted for example a loss of performance due to the routine used to implement the north fold algorithm (i.e. handling the points at the north pole of the 3-poles Grids): indeed an optimization of the routine implementation is needed. The folding is achieved considering only the last 4 rows on the top of the global domain and by applying a rotation pivoting on the point in the middle. During the folding, the point on the top left is updated with the value of the point on bottom right and so on. The current version of the parallel algorithm is based on the domain decomposition. Each MPI process takes care of a block of points. Each process can update its points using values belonging to the symmetric process. In the current implementation, each received message is placed in a buffer with a number of elements equal to the total dimension of the global domain. Each process sweeps the entire buffer, but only a part of that computation is really useful for the
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2016-03-01
Full Text Available The performance of rapid prototyping (RP processes is often measured in terms of build time, product quality, dimensional accuracy, cost of production, mechanical and tribological properties of the models and energy consumed in the process. The success of any RP process in terms of these performance measures entails selection of the optimum combination of the influential process parameters. Thus, in this work the single-objective and multi-objective optimization problems of a widely used RP process, namely, fused deposition modeling (FDM, are formulated, and the same are solved using the teaching-learning-based optimization (TLBO algorithm and non-dominated Sorting TLBO (NSTLBO algorithm, respectively. The results of the TLBO algorithm are compared with those obtained using genetic algorithm (GA, and quantum behaved particle swarm optimization (QPSO algorithm. The TLBO algorithm showed better performance as compared to GA and QPSO algorithms. The NSTLBO algorithm proposed to solve the multi-objective optimization problems of the FDM process in this work is a posteriori version of the TLBO algorithm. The NSTLBO algorithm is incorporated with non-dominated sorting concept and crowding distance assignment mechanism to obtain a dense set of Pareto optimal solutions in a single simulation run. The results of the NSTLBO algorithm are compared with those obtained using non-dominated sorting genetic algorithm (NSGA-II and the desirability function approach. The Pareto-optimal set of solutions for each problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for the FDM process.
Proton Exchange Membrane Fuel Cell Modeling Based on Seeker Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
LI Qi; DAI Chao-hua; Chen Wei-rong; JIA Jun-bo; HAN Ming
2008-01-01
Seeker optimization algorithm (SOA) has applications in continuous space of swarm intelligence. In the fields of proton ex-change membrane fuel cell (PEMFC) modeling, SOA was proposed to research a set of optimized parameters in PEMFC polariza-tion curve model. Experimental result showed that the mean square error of the optimization modeling strategy was only 6.9 × 10-23. Hence, the optimization model could fit the experiment data with high precision.
Spädtke, P
2013-01-01
Modeling of technical machines became a standard technique since computer became powerful enough to handle the amount of data relevant to the specific system. Simulation of an existing physical device requires the knowledge of all relevant quantities. Electric fields given by the surrounding boundary as well as magnetic fields caused by coils or permanent magnets have to be known. Internal sources for both fields are sometimes taken into account, such as space charge forces or the internal magnetic field of a moving bunch of charged particles. Used solver routines are briefly described and some bench-marking is shown to estimate necessary computing times for different problems. Different types of charged particle sources will be shown together with a suitable model to describe the physical model. Electron guns are covered as well as different ion sources (volume ion sources, laser ion sources, Penning ion sources, electron resonance ion sources, and H$^-$-sources) together with some remarks on beam transport.
A Novel Model for Optimized GSM Network Design
de Aguiar, Alexei Barbosa; Neto, Alvaro de Menezes S; Cunha, Ruddy P P; Pinheiro, Rebecca F
2009-01-01
GSM networks are very expensive. The network design process requires too many decisions in a combinatorial explosion. For this reason, the larger is the network, the harder is to achieve a totally human based optimized solution. The BSC (Base Station Control) nodes have to be geographically well allocated to reduce the transmission costs. There are decisions of association between BTS and BSC those impacts in the correct dimensioning of these BSC. The choice of BSC quantity and model capable of carrying the cumulated traffic of its affiliated BTS nodes in turn reflects on the total cost. In addition, the last component of the total cost is due to transmission for linking BSC nodes to MSC. These trunks have a major significance since the number of required E1 lines is larger than BTS to BSC link. This work presents an integer programming model and a computational tool for designing GSM (Global System for Mobile Communications) networks, regarding BSS (Base Station Subsystem) with optimized cost.
Modeling marine surface microplastic transport to assess optimal removal locations
Sherman, Peter; van Sebille, Erik
2016-01-01
Marine plastic pollution is an ever-increasing problem that demands immediate mitigation and reduction plans. Here, a model based on satellite-tracked buoy observations and scaled to a large data set of observations on microplastic from surface trawls was used to simulate the transport of plastics floating on the ocean surface from 2015 to 2025, with the goal to assess the optimal marine microplastic removal locations for two scenarios: removing the most surface microplastic and reducing the impact on ecosystems, using plankton growth as a proxy. The simulations show that the optimal removal locations are primarily located off the coast of China and in the Indonesian Archipelago for both scenarios. Our estimates show that 31% of the modeled microplastic mass can be removed by 2025 using 29 plastic collectors operating at a 45% capture efficiency from these locations, compared to only 17% when the 29 plastic collectors are moored in the North Pacific garbage patch, between Hawaii and California. The overlap of ocean surface microplastics and phytoplankton growth can be reduced by 46% at our proposed locations, while sinks in the North Pacific can only reduce the overlap by 14%. These results are an indication that oceanic plastic removal might be more effective in removing a greater microplastic mass and in reducing potential harm to marine life when closer to shore than inside the plastic accumulation zones in the centers of the gyres.
Optimizing Crawler4j using MapReduce Programming Model
Siddesh, G. M.; Suresh, Kavya; Madhuri, K. Y.; Nijagal, Madhushree; Rakshitha, B. R.; Srinivasa, K. G.
2016-08-01
World wide web is a decentralized system that consists of a repository of information on the basis of web pages. These web pages act as a source of information or data in the present analytics world. Web crawlers are used for extracting useful information from web pages for different purposes. Firstly, it is used in web search engines where the web pages are indexed to form a corpus of information and allows the users to query on the web pages. Secondly, it is used for web archiving where the web pages are stored for later analysis phases. Thirdly, it can be used for web mining where the web pages are monitored for copyright purposes. The amount of information processed by the web crawler needs to be improved by using the capabilities of modern parallel processing technologies. In order to solve the problem of parallelism and the throughput of crawling this work proposes to optimize the Crawler4j using the Hadoop MapReduce programming model by parallelizing the processing of large input data. Crawler4j is a web crawler that retrieves useful information about the pages that it visits. The crawler Crawler4j coupled with data and computational parallelism of Hadoop MapReduce programming model improves the throughput and accuracy of web crawling. The experimental results demonstrate that the proposed solution achieves significant improvements with respect to performance and throughput. Hence the proposed approach intends to carve out a new methodology towards optimizing web crawling by achieving significant performance gain.
Optimizing Crawler4j using MapReduce Programming Model
Siddesh, G. M.; Suresh, Kavya; Madhuri, K. Y.; Nijagal, Madhushree; Rakshitha, B. R.; Srinivasa, K. G.
2017-06-01
World wide web is a decentralized system that consists of a repository of information on the basis of web pages. These web pages act as a source of information or data in the present analytics world. Web crawlers are used for extracting useful information from web pages for different purposes. Firstly, it is used in web search engines where the web pages are indexed to form a corpus of information and allows the users to query on the web pages. Secondly, it is used for web archiving where the web pages are stored for later analysis phases. Thirdly, it can be used for web mining where the web pages are monitored for copyright purposes. The amount of information processed by the web crawler needs to be improved by using the capabilities of modern parallel processing technologies. In order to solve the problem of parallelism and the throughput of crawling this work proposes to optimize the Crawler4j using the Hadoop MapReduce programming model by parallelizing the processing of large input data. Crawler4j is a web crawler that retrieves useful information about the pages that it visits. The crawler Crawler4j coupled with data and computational parallelism of Hadoop MapReduce programming model improves the throughput and accuracy of web crawling. The experimental results demonstrate that the proposed solution achieves significant improvements with respect to performance and throughput. Hence the proposed approach intends to carve out a new methodology towards optimizing web crawling by achieving significant performance gain.
Stochastic Modeling and Optimization in a Microgrid: A Survey
Directory of Open Access Journals (Sweden)
Hao Liang
2014-03-01
Full Text Available The future smart grid is expected to be an interconnected network of small-scale and self-contained microgrids, in addition to a large-scale electric power backbone. By utilizing microsources, such as renewable energy sources and combined heat and power plants, microgrids can supply electrical and heat loads in local areas in an economic and environment friendly way. To better adopt the intermittent and weather-dependent renewable power generation, energy storage devices, such as batteries, heat buffers and plug-in electric vehicles (PEVs with vehicle-to-grid systems can be integrated in microgrids. However, significant technical challenges arise in the planning, operation and control of microgrids, due to the randomness in renewable power generation, the buffering effect of energy storage devices and the high mobility of PEVs. The two-way communication functionalities of the future smart grid provide an opportunity to address these challenges, by offering the communication links for microgrid status information collection. However, how to utilize stochastic modeling and optimization tools for efficient, reliable and economic planning, operation and control of microgrids remains an open issue. In this paper, we investigate the key features of microgrids and provide a comprehensive literature survey on the stochastic modeling and optimization tools for a microgrid. Future research directions are also identified.
Modeling and multidimensional optimization of a tapered free electron laser
Directory of Open Access Journals (Sweden)
Y. Jiao
2012-05-01
Full Text Available Energy extraction efficiency of a free electron laser (FEL can be greatly increased using a tapered undulator and self-seeding. However, the extraction rate is limited by various effects that eventually lead to saturation of the peak intensity and power. To better understand these effects, we develop a model extending the Kroll-Morton-Rosenbluth, one-dimensional theory to include the physics of diffraction, optical guiding, and radially resolved particle trapping. The predictions of the model agree well with that of the GENESIS single-frequency numerical simulations. In particular, we discuss the evolution of the electron-radiation interaction along the tapered undulator and show that the decreasing of refractive guiding is the major cause of the efficiency reduction, particle detrapping, and then saturation of the radiation power. With this understanding, we develop a multidimensional optimization scheme based on GENESIS simulations to increase the energy extraction efficiency via an improved taper profile and variation in electron beam radius. We present optimization results for hard x-ray tapered FELs, and the dependence of the maximum extractable radiation power on various parameters of the initial electron beam, radiation field, and the undulator system. We also study the effect of the sideband growth in a tapered FEL. Such growth induces increased particle detrapping and thus decreased refractive guiding that together strongly limit the overall energy extraction efficiency.
Directory of Open Access Journals (Sweden)
V. Sharma
2011-08-01
Full Text Available This study demonstrates the use of a high-performance feedback neural network optimizer based on a new idea of successive approximation for finding the hourly optimal release schedules of interconnected multi-reservoir power system in such a way to minimize the overall cost of thermal generations spanned over the planning period. The main advantages of the proposed neural network optimizer over the existing neural network optimization models are that no dual variables, penalty parameters or lagrange multipliers are required. This network uses a simple structure with the least number of state variables and has better asymptotic stability. For an arbitrarily chosen initial point, the trajectory of the network converges to an optimal solution of the convex nonlinear programming problem. The proposed optimizer has been tested on a nonlinear practical system consisting of a multi-chain cascade of four linked reservoir type hydro-plants and a number of thermal units represented by a single equivalent thermal power plant and so obtained results have been validated using conventional conjugate gradient method and genetic algorithm based approach.
Use of advanced modeling techniques to optimize thermal packaging designs.
Formato, Richard M; Potami, Raffaele; Ahmed, Iftekhar
2010-01-01
Through a detailed case study the authors demonstrate, for the first time, the capability of using advanced modeling techniques to correctly simulate the transient temperature response of a convective flow-based thermal shipper design. The objective of this case study was to demonstrate that simulation could be utilized to design a 2-inch-wall polyurethane (PUR) shipper to hold its product box temperature between 2 and 8 °C over the prescribed 96-h summer profile (product box is the portion of the shipper that is occupied by the payload). Results obtained from numerical simulation are in excellent agreement with empirical chamber data (within ±1 °C at all times), and geometrical locations of simulation maximum and minimum temperature match well with the corresponding chamber temperature measurements. Furthermore, a control simulation test case was run (results taken from identical product box locations) to compare the coupled conduction-convection model with a conduction-only model, which to date has been the state-of-the-art method. For the conduction-only simulation, all fluid elements were replaced with "solid" elements of identical size and assigned thermal properties of air. While results from the coupled thermal/fluid model closely correlated with the empirical data (±1 °C), the conduction-only model was unable to correctly capture the payload temperature trends, showing a sizeable error compared to empirical values (ΔT > 6 °C). A modeling technique capable of correctly capturing the thermal behavior of passively refrigerated shippers can be used to quickly evaluate and optimize new packaging designs. Such a capability provides a means to reduce the cost and required design time of shippers while simultaneously improving their performance. Another advantage comes from using thermal modeling (assuming a validated model is available) to predict the temperature distribution in a shipper that is exposed to ambient temperatures which were not bracketed
Optimization of Forward Wave Modeling on Contemporary HPC Architectures
Energy Technology Data Exchange (ETDEWEB)
Krueger, Jens [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Micikevicius, Paulius [NVIDIA, Santa Clara, CA (United States); Williams, Samuel [Fraunhofer ITWM, Kaiserslautern (Germany)
2012-07-20
Reverse Time Migration (RTM) is one of the main approaches in the seismic processing industry for imaging the subsurface structure of the Earth. While RTM provides qualitative advantages over its predecessors, it has a high computational cost warranting implementation on HPC architectures. We focus on three progressively more complex kernels extracted from RTM: for isotropic (ISO), vertical transverse isotropic (VTI) and tilted transverse isotropic (TTI) media. In this work, we examine performance optimization of forward wave modeling, which describes the computational kernels used in RTM, on emerging multi- and manycore processors and introduce a novel common subexpression elimination optimization for TTI kernels. We compare attained performance and energy efficiency in both the single-node and distributed memory environments in order to satisfy industry’s demands for fidelity, performance, and energy efficiency. Moreover, we discuss the interplay between architecture (chip and system) and optimizations (both on-node computation) highlighting the importance of NUMA-aware approaches to MPI communication. Ultimately, our results show we can improve CPU energy efficiency by more than 10× on Magny Cours nodes while acceleration via multiple GPUs can surpass the energy-efficient Intel Sandy Bridge by as much as 3.6×.
Multi-level systems modeling and optimization for novel aircraft
Subramanian, Shreyas Vathul
This research combines the disciplines of system-of-systems (SoS) modeling, platform-based design, optimization and evolving design spaces to achieve a novel capability for designing solutions to key aeronautical mission challenges. A central innovation in this approach is the confluence of multi-level modeling (from sub-systems to the aircraft system to aeronautical system-of-systems) in a way that coordinates the appropriate problem formulations at each level and enables parametric search in design libraries for solutions that satisfy level-specific objectives. The work here addresses the topic of SoS optimization and discusses problem formulation, solution strategy, the need for new algorithms that address special features of this problem type, and also demonstrates these concepts using two example application problems - a surveillance UAV swarm problem, and the design of noise optimal aircraft and approach procedures. This topic is critical since most new capabilities in aeronautics will be provided not just by a single air vehicle, but by aeronautical Systems of Systems (SoS). At the same time, many new aircraft concepts are pressing the boundaries of cyber-physical complexity through the myriad of dynamic and adaptive sub-systems that are rising up the TRL (Technology Readiness Level) scale. This compositional approach is envisioned to be active at three levels: validated sub-systems are integrated to form conceptual aircraft, which are further connected with others to perform a challenging mission capability at the SoS level. While these multiple levels represent layers of physical abstraction, each discipline is associated with tools of varying fidelity forming strata of 'analysis abstraction'. Further, the design (composition) will be guided by a suitable hierarchical complexity metric formulated for the management of complexity in both the problem (as part of the generative procedure and selection of fidelity level) and the product (i.e., is the mission
Tool Steel Heat Treatment Optimization Using Neural Network Modeling
Podgornik, Bojan; Belič, Igor; Leskovšek, Vojteh; Godec, Matjaz
2016-11-01
Optimization of tool steel properties and corresponding heat treatment is mainly based on trial and error approach, which requires tremendous experimental work and resources. Therefore, there is a huge need for tools allowing prediction of mechanical properties of tool steels as a function of composition and heat treatment process variables. The aim of the present work was to explore the potential and possibilities of artificial neural network-based modeling to select and optimize vacuum heat treatment conditions depending on the hot work tool steel composition and required properties. In the current case training of the feedforward neural network with error backpropagation training scheme and four layers of neurons (8-20-20-2) scheme was based on the experimentally obtained tempering diagrams for ten different hot work tool steel compositions and at least two austenitizing temperatures. Results show that this type of modeling can be successfully used for detailed and multifunctional analysis of different influential parameters as well as to optimize heat treatment process of hot work tool steels depending on the composition. In terms of composition, V was found as the most beneficial alloying element increasing hardness and fracture toughness of hot work tool steel; Si, Mn, and Cr increase hardness but lead to reduced fracture toughness, while Mo has the opposite effect. Optimum concentration providing high KIc/HRC ratios would include 0.75 pct Si, 0.4 pct Mn, 5.1 pct Cr, 1.5 pct Mo, and 0.5 pct V, with the optimum heat treatment performed at lower austenitizing and intermediate tempering temperatures.
Optimization of Glioblastoma Mouse Orthotopic Xenograft Models for Translational Research.
Irtenkauf, Susan M; Sobiechowski, Susan; Hasselbach, Laura A; Nelson, Kevin K; Transou, Andrea D; Carlton, Enoch T; Mikkelsen, Tom; deCarvalho, Ana C
2017-08-01
Glioblastoma is an aggressive primary brain tumor predominantly localized to the cerebral cortex. We developed a panel of patient-derived mouse orthotopic xenografts (PDOX) for preclinical drug studies by implanting cancer stem cells (CSC) cultured from fresh surgical specimens intracranially into 8-wk-old female athymic nude mice. Here we optimize the glioblastoma PDOX model by assessing the effect of implantation location on tumor growth, survival, and histologic characteristics. To trace the distribution of intracranial injections, toluidine blue dye was injected at 4 locations with defined mediolateral, anterioposterior, and dorsoventral coordinates within the cerebral cortex. Glioblastoma CSC from 4 patients and a glioblastoma nonstem-cell line were then implanted by using the same coordinates for evaluation of tumor location, growth rate, and morphologic and histologic features. Dye injections into one of the defined locations resulted in dye dissemination throughout the ventricles, whereas tumor cell implantation at the same location resulted in a much higher percentage of small multifocal ventricular tumors than did the other 3 locations tested. Ventricular tumors were associated with a lower tumor growth rate, as measured by in vivo bioluminescence imaging, and decreased survival in 4 of 5 cell lines. In addition, tissue oxygenation, vasculature, and the expression of astrocytic markers were altered in ventricular tumors compared with nonventricular tumors. Based on this information, we identified an optimal implantation location that avoided the ventricles and favored cortical tumor growth. To assess the effects of stress from oral drug administration, mice that underwent daily gavage were compared with stress-positive and -negative control groups. Oral gavage procedures did not significantly affect the survival of the implanted mice or physiologic measurements of stress. Our findings document the importance of optimization of the implantation site for
OPTIMIZATION OF TETRANDRINE TREATMENT IN RAT HEPATIC FIBROSIS MODEL
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Objective To optimize the therapeutic dosage of tetrandrine (Tet) in rat hepatic fibrosis model. Methods 50 Wistar rats were divided into 5 groups at random including normal control, model control, Tettreated model groups of l0mg·kg-1 ·d-1, 5mg·kg-1 ·d-1 and 2.5mg·kg-1 ·d-1 ( n =10 in each group). All rats,except for the normal controls, were injected with axenic porcine serum (0. 5ml each time, twice a week) intraperitoneally for 8 weeks to establish hepatic fibrosis. After the 8th week, rats of Tet-treated model groups were given by gavage once a day with different doses of Tet for another 8 weeks. Then the liver function, serum levels of hyaluronic acid (HA) , laminin ( LM) , and procollagen type Ⅲ (PCⅢ) were tested. Collagen type Ⅰ and Ⅲ, pathological changes in liver tissue were also assessed. Results Most indices of liver function including alanine minotransferase (ALT), aspartate aminotransferase ( AST), albumin (ALB), albumin/globulin ratio (A/G) and alkaline phosphatase (ALP) improved significantly in Tet-treated groups with the exception of γ-glutamyl transpeptidase (γ-GT) and total bilirubin (TBIL). Secondly, markedly lowered levels of HA, LM and collagen type Ⅰ, Ⅲ were also detected by radioimmunology and immunohistochemistry in the 5 mg · kg- 1 · d- 1 Tet-treated model group. Moreover, pathological findings confirmed the statistically significant improvement in hepatofibrotic degree resulted from the treatment of 5mg · kg- 1 · d-1 rather than other doses of Tet. Conclusion For experimental Wistar rats, Tet exhibited an anti-hepatofibrotic action in doses within the range of 2.5mg·kg-1 ·d-1 to 10mg·kg 1 ·d-1, and 5mg·kg-1 ·d-1 may be theoptimum one among all doses.
A Suitable Artificial Intelligence Model for Inventory Level Optimization
Directory of Open Access Journals (Sweden)
Tereza Sustrova
2016-05-01
Full Text Available Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings of the research show that artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction.. Conclusions resulting from the paper are beneficial for further research. It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise.
A Swarm Optimization Based Method for Urban Growth Modelling
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Sassan Mohammady
2014-10-01
Full Text Available Land use activity is a major issue and challenge for town and country planners. Urban planners must be able to allocate urban land area to different applications with a special focus on the role and function of the city, its economy, and the ability to simulate the effect of user interaction with each other. Continuing migration of rural population to cities and population increases has caused many problems of today's cities including the expansion of urban areas, lack of infrastructure and urban services as well as environmental pollution. Local governments that implement urban growth boundaries need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban activities. Urban growth is a complex process that encounters a number of sophisticated parameters that interact to produce the urban growth pattern. Urban growth modelling aims to understand the dynamic processes. Therefore, interpretability of models is becoming increasingly important. Different approaches have been applied in spatial modelling. In this study, Particle Swarm Optimization (PSO has been used for modelling of urban growth in Qazvin city area (Iran during 2005 to 2011. Landsat imageries, taken in 2005 and 2011 have been used in the study. Main parameters in this study are distance to residential area, distance to industrial area, slope, accessibility, land price and number of urban cell in a 3*3 neighbourhood. Figure of Merit and Kappa statistics have been used for estimating accuracy of the proposed model. DOI: http://dx.doi.org/10.5755/j01.erem.69.3.6653
Modeling and optimization of energy storage system for microgrid
Qiu, Xin
The vanadium redox flow battery (VRB) is well suited for the applications of microgrid and renewable energy. This thesis will have a practical analysis of the battery itself and its application in microgrid systems. The first paper analyzes the VRB use in a microgrid system. The first part of the paper develops a reduced order circuit model of the VRB and analyzes its experimental performance efficiency during deployment. The statistical methods and neural network approximation are used to estimate the system parameters. The second part of the paper addresses the implementation issues of the VRB application in a photovoltaic-based microgrid system. A new dc-dc converter was proposed to provide improved charging performance. The paper was published on IEEE Transactions on Smart Grid, Vol. 5, No. 4, July 2014. The second paper studies VRB use within a microgrid system from a practical perspective. A reduced order circuit model of the VRB is introduced that includes the losses from the balance of plant including system and environmental controls. The proposed model includes the circulation pumps and the HVAC system that regulates the environment of the VRB enclosure. In this paper, the VRB model is extended to include the ESS environmental controls to provide a model that provides a more realistic efficiency profile. The paper was submitted to IEEE Transactions on Sustainable Energy. Third paper discussed the optimal control strategy when VRB works with other type of battery in a microgird system. The work in first paper is extended. A high level control strategy is developed to coordinate a lead acid battery and a VRB with reinforcement learning. The paper is to be submitted to IEEE Transactions on Smart Grid.
African Journals Online (AJOL)
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Gradient-based Kriging approximate model and its application research to optimization design
Institute of Scientific and Technical Information of China (English)
XUAN Ying; XIANG JunHua; ZHANG WeiHua; ZHANG YuLin
2009-01-01
In the process of multidisciplinary design optimization, there exits the calculation complexity problem due to frequently calling high fidelity system analysis models. The high fidelity system analysis models can be surrogated by approximate models. The sensitivity analysis and numerical noise filtering can be done easily by coupling approximate models to optimization. Approximate models can reduce the number of executions of the problem's simulation code during optimization, so the solution efficiency of the multidisciplinary design optimization problem can be improved. Most optimization methods are based on gradient. The gradients of the objective and constrain functions are gained easily. The gradient-based Kriging (GBK) approximate model can be constructed by using system response value and its gradients. The gradients can greatly improve prediction precision of system response. The hybrid optimization method is constructed by coupling GBK approximate models to gradient-based optimization methods. An aircraft aerodynamics shape optimization design example indicates that the methods of this paper can achieve good feasibility and validity.
Irregular Shaped Building Design Optimization with Building Information Modelling
Directory of Open Access Journals (Sweden)
Lee Xia Sheng
2016-01-01
Full Text Available This research is to recognise the function of Building Information Modelling (BIM in design optimization for irregular shaped buildings. The study focuses on a conceptual irregular shaped “twisted” building design similar to some existing sculpture-like architectures. Form and function are the two most important aspects of new buildings, which are becoming more sophisticated as parts of equally sophisticated “systems” that we are living in. Nowadays, it is common to have irregular shaped or sculpture-like buildings which are very different when compared to regular buildings. Construction industry stakeholders are facing stiff challenges in many aspects such as buildability, cost effectiveness, delivery time and facility management when dealing with irregular shaped building projects. Building Information Modelling (BIM is being utilized to enable architects, engineers and constructors to gain improved visualization for irregular shaped buildings; this has a purpose of identifying critical issues before initiating physical construction work. In this study, three variations of design options differing in rotating angle: 30 degrees, 60 degrees and 90 degrees are created to conduct quantifiable comparisons. Discussions are focused on three major aspects including structural planning, usable building space, and structural constructability. This research concludes that Building Information Modelling is instrumental in facilitating design optimization for irregular shaped building. In the process of comparing different design variations, instead of just giving “yes or no” type of response, stakeholders can now easily visualize, evaluate and decide to achieve the right balance based on their own criteria. Therefore, construction project stakeholders are empowered with superior evaluation and decision making capability.
Sherry, Lance; Ferguson, John; Hoffman, Karla; Donohue, George; Beradino, Frank
2012-01-01
This report describes the Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM) that is designed to provide insights into airline decision-making with regards to markets served, schedule of flights on these markets, the type of aircraft assigned to each scheduled flight, load factors, airfares, and airline profits. The main inputs to the model are hedged fuel prices, airport capacity limits, and candidate markets. Embedded in the model are aircraft performance and associated cost factors, and willingness-to-pay (i.e. demand vs. airfare curves). Case studies demonstrate the application of the model for analysis of the effects of increased capacity and changes in operating costs (e.g. fuel prices). Although there are differences between airports (due to differences in the magnitude of travel demand and sensitivity to airfare), the system is more sensitive to changes in fuel prices than capacity. Further, the benefits of modernization in the form of increased capacity could be undermined by increases in hedged fuel prices
Productivity simulation model for optimization of maritime container terminals
Directory of Open Access Journals (Sweden)
Elen TWRDY
2009-01-01
Full Text Available This article describes a proposed productivity simulation model enabling container terminal operators to find optimization possibilities. A research of more than forty terminals has been done, in order to provide a helping tool for maritime container terminals. By applying an adequate simulation model, it is possible to measure and increase the productivity in all subsystem of the maritime container terminal. Management of a maritime container terminal includes a vast number of different financial and operational decisions. Financial decisions are often in a direct connection with investments in infrastructure and handling equipment. Such investments are very expensive. Therefore, they must give back the invested money as soon as possible. On the other hand, some terminals are limited by the physical extension and are forced to increase annual throughput only with sophisticated equipment on the berth side and on the yard as well. Considering all these important facts in container and shipping industry, the proposed simulation model gives a helping tool for checking the productivity and its time variation and monitoring competitiveness of a certain maritime terminal with terminals from the same group.
Proper Orthogonal Decomposition as Surrogate Model for Aerodynamic Optimization
Directory of Open Access Journals (Sweden)
Valentina Dolci
2016-01-01
Full Text Available A surrogate model based on the proper orthogonal decomposition is developed in order to enable fast and reliable evaluations of aerodynamic fields. The proposed method is applied to subsonic turbulent flows and the proper orthogonal decomposition is based on an ensemble of high-fidelity computations. For the construction of the ensemble, fractional and full factorial planes together with central composite design-of-experiment strategies are applied. For the continuous representation of the projection coefficients in the parameter space, response surface methods are employed. Three case studies are presented. In the first case, the boundary shape of the problem is deformed and the flow past a backward facing step with variable step slope is studied. In the second case, a two-dimensional flow past a NACA 0012 airfoil is considered and the surrogate model is constructed in the (Mach, angle of attack parameter space. In the last case, the aerodynamic optimization of an automotive shape is considered. The results demonstrate how a reduced-order model based on the proper orthogonal decomposition applied to a small number of high-fidelity solutions can be used to generate aerodynamic data with good accuracy at a low cost.
Results of Satellite Brightness Modeling Using Kringing Optimized Interpolation
Weeden, C.; Hejduk, M.
At the 2005 AMOS conference, Kriging Optimized Interpolation (KOI) was presented as a tool to model satellite brightness as a function of phase angle and solar declination angle (J.M Okada and M.D. Hejduk). Since November 2005, this method has been used to support the tasking algorithm for all optical sensors in the Space Surveillance Network (SSN). The satellite brightness maps generated by the KOI program are compared to each sensor's ability to detect an object as a function of the brightness of the background sky and angular rate of the object. This will determine if the sensor can technically detect an object based on an explicit calculation of the object's probability of detection. In addition, recent upgrades at Ground-Based Electro Optical Deep Space Surveillance Sites (GEODSS) sites have increased the amount and quality of brightness data collected and therefore available for analysis. This in turn has provided enough data to study the modeling process in more detail in order to obtain the most accurate brightness prediction of satellites. Analysis of two years of brightness data gathered from optical sensors and modeled via KOI solutions are outlined in this paper. By comparison, geo-stationary objects (GEO) were tracked less than non-GEO objects but had higher density tracking in phase angle due to artifices of scheduling. A statistically-significant fit to a deterministic model was possible less than half the time in both GEO and non-GEO tracks, showing that a stochastic model must often be used alone to produce brightness results, but such results are nonetheless serviceable. Within the Kriging solution, the exponential variogram model was the most frequently employed in both GEO and non-GEO tracks, indicating that monotonic brightness variation with both phase and solar declination angle is common and testifying to the suitability to the application of regionalized variable theory to this particular problem. Finally, the average nugget value, or
Optimization of precipitation inputs for SWAT modeling in mountainous catchment
Tuo, Ye; Chiogna, Gabriele; Disse, Markus
2016-04-01
Precipitation is often the most important input data in hydrological models when simulating streamflow in mountainous catchment. The Soil and Water Assessment Tool (SWAT), a widely used hydrological model, only makes use of data from one precipitation gauging station which is nearest to the centroid of each subcatchment, eventually corrected using the band elevation method. This leads in general to inaccurate subcatchment precipitation representation, which results in unreliable simulation results in mountainous catchment. To investigate the impact of the precipitation inputs and consider the high spatial and temporal variability of precipitation, we first interpolated 21 years (1990-2010) of daily measured data using the Inverse Distance Weighting (IDW) method. Averaged IDW daily values have been calculated at the subcatchment scale to be further supplied as optimized precipitation inputs for SWAT. Both datasets (Measured data and IDW data) are applied to three Alpine subcatchments of the Adige catchment (North-eastern Italy, 12100 km2) as precipitation inputs. Based on the calibration and validation results, model performances are evaluated according to the Nash Sutchliffe Efficiency (NSE) and Coefficient of Determination (R2). For all three subcatchments, the simulation results with IDW inputs are better than the original method which uses measured inputs from the nearest station. This suggests that IDW method could improve the model performance in Alpine catchments to some extent. By taking into account and weighting the distance between precipitation records, IDW supplies more accurate precipitation inputs for each individual Alpine subcatchment, which would as a whole lead to an improved description of the hydrological behavior of the entire Adige catchment.
An optimization model to agroindustrial sector in antioquia (Colombia, South America)
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.
Yang, Guoxiang; Best, Elly P H
2015-09-15
Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions. Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
MILP model for energy optimization in EIP water networks
Energy Technology Data Exchange (ETDEWEB)
Taskhiri, Mohammad Sadegh [De La Salle University, Industrial Engineering Department, Manila (Philippines); Tan, Raymond R. [De La Salle University, Center for Engineering and Sustainable Development Research, Manila (Philippines); Chiu, Anthony S.F. [De La Salle University, Industrial Engineering Department, Manila (Philippines); De La Salle University, Center for Engineering and Sustainable Development Research, Manila (Philippines)
2011-10-15
The eco-industrial park (EIP) concept provides a framework in which several plants can cooperate with each other and exchange their wastewater to minimize total freshwater consumption. Energy analysis is a methodology that considers the total, cumulative energy which has been consumed within a system; thus, by minimizing energy, an environmentally optimal EIP can be designed. This article presents a mixed-integer linear programming (MILP) model for minimizing energy of an interplant water network in an EIP. The methodology accounts for the environmental impacts of water use, energy consumption, and capital goods within the EIP in a balanced manner. The proposed technique is then demonstrated by solving a case study from literature. (orig.)
Optimal, scalable forward models for computing gravity anomalies
May, Dave A
2011-01-01
We describe three approaches for computing a gravity signal from a density anomaly. The first approach consists of the classical "summation" technique, whilst the remaining two methods solve the Poisson problem for the gravitational potential using either a Finite Element (FE) discretization employing a multilevel preconditioner, or a Green's function evaluated with the Fast Multipole Method (FMM). The methods utilizing the PDE formulation described here differ from previously published approaches used in gravity modeling in that they are optimal, implying that both the memory and computational time required scale linearly with respect to the number of unknowns in the potential field. Additionally, all of the implementations presented here are developed such that the computations can be performed in a massively parallel, distributed memory computing environment. Through numerical experiments, we compare the methods on the basis of their discretization error, CPU time and parallel scalability. We demonstrate t...
The spa as a model of an optimal healing environment.
Frost, Gary J
2004-01-01
"Spa" is an acronym for salus per aqua, or health through water. There currently are approximately 10,000 spas of all types in the United States. Most now focus on eating and weight programs with subcategories of sports activities and nutrition most prominent. The main reasons stated by clients for their use are stress reduction, specific medical or other health issues, eating and weight loss, rest and relaxation, fitness and exercise, and pampering and beauty. A detailed description of the Canyon Ranch, a spa facility in Tucson, AZ, is presented as a case study in this paper. It appears that the three most critical factors in creating an optimal healing environment in a spa venue are (1) a dedicated caring staff at all levels, (2) a mission driven organization that will not compromise, and (3) a sound business model and leadership that will ensure permanency.
High voltage direct current modelling in optimal power flows
Energy Technology Data Exchange (ETDEWEB)
Ambriz-Perez, H. [Comision Federal de Electricidad, Mexico, Unidad de Ingenieria Especializada, Rio Rodano No. 14 - Piso 10, Sala 1002, Col. Cuauhtemoc, C.P. 06598, Mexico, D.F. (Mexico); Acha, E. [Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow G128LT, Scotland (United Kingdom); Fuerte-Esquivel, C.R. [Faculty of Electrical Engineering, Universidad Michoacana de San Nicolas de Hidalgo, Morelia 58030, Michoacan (Mexico)
2008-03-15
Two-terminal high voltage direct current (HVDC) transmission links are in operation throughout the world. They are key elements in electrical power networks; their representation is oversimplified or ignored in most power system studies. This is particularly the case in Optima Power Flow (OPF) studies. Hence, an OPF program has been extended to incorporate HVDC links, taking due account of overlapping and power transfer control characteristics. This is a new development in Newton Optimal Power Flows, where the converter equations are included directly in the matrix W. The method is indeed a unified one since the solution vector is extended to accommodate the DC variables. The HVDC link model correctly takes into account the relevant DC limit variables. The impact of HVDC links on OPF studies is illustrated by numeric examples, which includes a 5-node system, the AEP 14-node and a 166-node system. (author)
Open source Modeling and optimization tools for Planning
Energy Technology Data Exchange (ETDEWEB)
Peles, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-02-10
Open source modeling and optimization tools for planning The existing tools and software used for planning and analysis in California are either expensive, difficult to use, or not generally accessible to a large number of participants. These limitations restrict the availability of participants for larger scale energy and grid studies in the state. The proposed initiative would build upon federal and state investments in open source software, and create and improve open source tools for use in the state planning and analysis activities. Computational analysis and simulation frameworks in development at national labs and universities can be brought forward to complement existing tools. An open source platform would provide a path for novel techniques and strategies to be brought into the larger community and reviewed by a broad set of stakeholders.
Simulation and optimization models for emergency medical systems planning.
Bettinelli, Andrea; Cordone, Roberto; Ficarelli, Federico; Righini, Giovanni
2014-01-01
The authors address strategic planning problems for emergency medical systems (EMS). In particular, the three following critical decisions are considered: i) how many ambulances to deploy in a given territory at any given point in time, to meet the forecasted demand, yielding an appropriate response time; ii) when ambulances should be used for serving nonurgent requests and when they should better be kept idle for possible incoming urgent requests; iii) how to define an optimal mix of contracts for renting ambulances from private associations to meet the forecasted demand at minimum cost. In particular, analytical models for decision support, based on queuing theory, discrete-event simulation, and integer linear programming were presented. Computational experiments have been done on real data from the city of Milan, Italy.
Security Optimization of VTP Model in an Enterprise VLAN
Directory of Open Access Journals (Sweden)
Rajiv O. Verma
2013-05-01
Full Text Available VLANs are extensively used in enterprise network to ease management of hosts to improve scalability and flexibility. Despite their wide usage in enterprise network, VLAN security is a greater concern for the network administrator due to very little attention has been paid on error prone, unsystematic, high risk of misconfiguration in the design and management of enterprise VLAN network. Our paper demonstrates the security optimization techniques in designing VLAN both for Inter-VLAN communication and addressing VTP issues. We proposed various security aspects like access-lists based layer 3 securities in Inter-VLAN routing, deactivating native VLAN 1 to secure Layer 2 traffic in VTP model, Application of authentication on VTP server and non-negotiating Dynamic Trunking Protocol mode to counter the effect of inserting a rogue switch/trunk with higher config revision number. Unless otherwise stated this paper is based upon configuration {&} hardware implementation in a Cisco environment
Comparison of operation optimization methods in energy system modelling
DEFF Research Database (Denmark)
Ommen, Torben Schmidt; Markussen, Wiebke Brix; Elmegaard, Brian
2013-01-01
, possibilities for decoupling production constraints may be valuable. Introduction of heat pumps in the district heating network may pose this ability. In order to evaluate if the introduction of heat pumps is economically viable, we develop calculation methods for the operation patterns of each of the used...... energy technologies. In the paper, three frequently used operation optimization methods are examined with respect to their impact on operation management of the combined technologies. One of the investigated approaches utilises linear programming for optimisation, one uses linear programming with binary...... operation constraints, while the third approach uses nonlinear programming. In the present case the non-linearity occurs in the boiler efficiency of power plants and the cv-value of an extraction plant. The linear programming model is used as a benchmark, as this type is frequently used, and has the lowest...
Multi-model Simulation for Optimal Control of Aeroacoustics.
Energy Technology Data Exchange (ETDEWEB)
Collis, Samuel Scott; Chen, Guoquan
2005-05-01
Flow-generated noise, especially rotorcraft noise has been a serious concern for bothcommercial and military applications. A particular important noise source for rotor-craft is Blade-Vortex-Interaction (BVI)noise, a high amplitude, impulsive sound thatoften dominates other rotorcraft noise sources. Usually BVI noise is caused by theunsteady flow changes around various rotor blades due to interactions with vorticespreviously shed by the blades. A promising approach for reducing the BVI noise isto use on-blade controls, such as suction/blowing, micro-flaps/jets, and smart struc-tures. Because the design and implementation of such experiments to evaluate suchsystems are very expensive, efficient computational tools coupled with optimal con-trol systems are required to explore the relevant physics and evaluate the feasibilityof using various micro-fluidic devices before committing to hardware.In this thesis the research is to formulate and implement efficient computationaltools for the development and study of optimal control and design strategies for com-plex flow and acoustic systems with emphasis on rotorcraft applications, especiallyBVI noise control problem. The main purpose of aeroacoustic computations is todetermine the sound intensity and directivity far away from the noise source. How-ever, the computational cost of using a high-fidelity flow-physics model across thefull domain is usually prohibitive and itmight also be less accurate because of thenumerical diffusion and other problems. Taking advantage of the multi-physics andmulti-scale structure of this aeroacoustic problem, we develop a multi-model, multi-domain (near-field/far-field) method based on a discontinuous Galerkin discretiza-tion. In this approach the coupling of multi-domains and multi-models is achievedby weakly enforcing continuity of normal fluxes across a coupling surface. For ourinterested aeroacoustics control problem, the adjoint equations that determine thesensitivity of the cost
Some meta-modeling and optimization techniques for helicopter pre-sizing.
Tremolet, A.; Basset, P.M.
2012-01-01
Optimization and meta-models are key elements of modern engineering techniques. The Multidisciplinary Design Optimization (MDO) allows solving strongly coupled physical problems aiming at the global system optimization. For these multidisciplinary optimizations, meta-models can be required as surrogates for complex and high computational cost codes. Meta-modeling is also used for catching general trends and underlying relationships between parameters within a database. The application of thes...
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.
Optimizing design of converters using power cycling lifetime models
DEFF Research Database (Denmark)
Nielsen, Rasmus Ørndrup; Munk-Nielsen, Stig
2015-01-01
Converter power cycling lifetime depends heavily on converter operation point. A lifetime model of a single power module switched mode power supply with wide input voltage range is shown. A lifetime model is created using a power loss model, a thermal model and a model for power cycling capability...... with a given mission profile. A method to improve the expected lifetime of the converter is presented, taking into account switching frequency, input voltage and transformer turns ratio....
Modeling, design, and optimization of Mindwalker series elastic joint.
Wang, Shiqian; Meijneke, Cor; van der Kooij, Herman
2013-06-01
Weight and power autonomy are limiting the daily use of wearable exoskeleton. Lightweight, efficient and powerful actuation system are not easy to achieve. Choosing the right combinations of existing technologies, such as battery, gear and motor is not a trivial task. In this paper, we propose an optimization framework by setting up a power-based quasi-static model of the exoskeleton joint drivetrain. The goal is to find the most efficient and lightweight combinations. This framework can be generalized for other similar applications by extending or accommodating the model to their own needs. We also present the Mindwalker exoskeleton joint, for which a novel series elastic actuator, consisting of a ballscrew-driven linear actuator and a double spiral spring, was developed and tested. This linear actuator is capable of outputting 960 W power and the exoskeleton joint can output 100 Nm peak torque continuously. The double spiral spring can sense torque between 0.08Nm and 100 Nm and it exhibits linearity of 99.99%, with no backlash or hysteresis. The series elastic joint can track a chirp torque profile with amplitude of 100 Nm over 6 Hz (large torque bandwidth) and for small torque (2 Nm peak-to-peak), it has a bandwidth over 38 Hz. The integrated exoskeleton joint, including the ballscrew-driven linear actuator, the series spring, electronics and the metal housing which hosts these components, weighs 2.9 kg.
Modeling and optimization for oil well production scheduling☆
Institute of Scientific and Technical Information of China (English)
Jin Lang; Jiao Zhao
2016-01-01
In this paper, an oil wel production scheduling problem for the light load oil wel during petroleum field exploi-tation was studied. The oil well production scheduling was to determine the turn on/off status and oil flow rates of the wel s in a given oil reservoir, subject to a number of constraints such as minimum up/down time limits and well grouping. The problem was formulated as a mixed integer nonlinear programming model that minimized the total production operating cost and start-up cost. Due to the NP-hardness of the problem, an improved par-ticle swarm optimization (PSO) algorithm with a new velocity updating formula was developed to solve the problem approximately. Computational experiments on randomly generated instances were carried out to eval-uate the performance of the model and the algorithm's effectiveness. Compared with the commercial solver CPLEX, the improved PSO can obtain high-quality schedules within a much shorter running time for all the instances.
3D modeling and optimization of the ITER ICRH antenna
Louche, F.; Dumortier, P.; Durodié, F.; Messiaen, A.; Maggiora, R.; Milanesio, D.
2011-12-01
The prediction of the coupling properties of the ITER ICRH antenna necessitates the accurate evaluation of the resistance and reactance matrices. The latter are mostly dependent on the geometry of the array and therefore a model as accurate as possible is needed to precisely compute these matrices. Furthermore simulations have so far neglected the poloidal and toroidal profile of the plasma, and it is expected that the loading by individual straps will vary significantly due to varying strap-plasma distance. To take this curvature into account, some modifications of the alignment of the straps with respect to the toroidal direction are proposed. It is shown with CST Microwave Studio® [1] that considering two segments in the toroidal direction, i.e. a "V-shaped" toroidal antenna, is sufficient. A new CATIA model including this segmentation has been drawn and imported into both MWS and TOPICA [2] codes. Simulations show a good agreement of the impedance matrices in vacuum. Various modifications of the geometry are proposed in order to further optimize the coupling. In particular we study the effect of the strap box parameters and the recess of the vertical septa.
Optimal oppor tunistic maintenance model of multi-unit systems
Institute of Scientific and Technical Information of China (English)
Zhijun Cheng; Zheng Yang; Bo Guo
2013-01-01
An opportunistic maintenance model is presented for a continuously deteriorating series system with economical de-pendence. The system consists of two kinds of units, which are respectively subjected to the deterioration failure described by Gamma process and the random failure described by Poisson process. A two-level opportunistic policy defined by three decision parameters is proposed to coordinate the different maintenance actions and minimize the long-run maintenance cost rate of the system. A computable expression of the average cost rate is es-tablished by using the renewal property of the stochastic process of the maintained system state. The optimal values of three deci-sion parameters are derived by an iteration approach based on the characteristic of Gamma process. The behavior of the proposed policy is il ustrated through a numerical experiment. Comparative study with the widely used corrective maintenance policy demon-strates the advantage of the proposed opportunistic maintenance method in significantly reducing the maintenance cost. Simultane-ously, the applicable area of this opportunistic model is discussed by the sensitivity analysis of the set-up cost and random failure rate.
Accurate energy model for WSN node and its optimal design
Institute of Scientific and Technical Information of China (English)
Kan Baoqiang; Cai Li; Zhu Hongsong; Xu Yongjun
2008-01-01
With the development of CMOS and MEMS technologies, the implementation of a large number of wireless distributed micro-sensors that can be easily and rapidly deployed to form highly redundant, self-configuring, and ad hoc sensor networks. To facilitate ease of deployment, these sensors operate on battery for extended periods of time. A particular challenge in maintaining extended battery lifetime lies in achieving communications with low power. For better understanding of the design tradeoffs of wireless sensor network (WSN), a more accurate energy model for wireless sensor node is proposed, and an optimal design method of energy efficient wireless sensor node is described as well. Different from power models ever shown which assume the power cost of each component in WSN node is constant, the new one takes into account the energy dissipation of circuits in practical physical layer. It shows that there are some parameters, such as data rate, carrier frequency, bandwidth, Tsw, etc, which have a significant effect on the WSN node energy consumption per useful bit (EPUB). For a given quality specification, how energy consumption can be reduced by adjusting one or more of these parameters is shown.
Institute of Scientific and Technical Information of China (English)
杨璐鸿; 刘顺安; 张冠宇; 王春雪
2015-01-01
To improve the operational efficiency of global optimization in engineering, Kriging model was established to simplify the mathematical model for calculations. Ducted coaxial-rotors aircraft was taken as an example and Fluent software was applied to the virtual prototype simulations. Through simulation sample points, the total lift of the ducted coaxial-rotors aircraft was obtained. The Kriging model was then constructed, and the function was fitted. Improved particle swarm optimization (PSO) was also utilized for the global optimization of the Kriging model of the ducted coaxial-rotors aircraft for the determination of optimized global coordinates. Finally, the optimized results were simulated by Fluent. The results show that the Kriging model and the improved PSO algorithm significantly improve the lift performance of ducted coaxial-rotors aircraft and computer operational efficiency.
Energy balance of forage consumption by phyllophagous insects: optimization model
Directory of Open Access Journals (Sweden)
O. V. Tarasova
2015-06-01
Full Text Available The model of optimal food consumption by phytophagous insects proposed, in which the metabolic costs are presented in the form of two components – the cost of food utilization and costs for proper metabolism of the individuals. Two measures were introduced – the «price» of food conversion and the «price» of biomass synthesis of individuals to assess the effectiveness of food consumption by caterpillars. The proposed approach to the description of food consumption by insects provides the exact solutions of the equation of energy balance of food consumption and determining the effectiveness of consumption and the risk of death of the individual. Experiments on larvae’s feeding in laboratory conditions were carried out to verify the model. Caterpillars of Aporia crataegi L. (Lepidoptera, Pieridae were the research subjects. Supplydemand balance, calculated value of the environmental price of consumption and efficiency of food consumption for each individual were determined from experimental data. It was found that the fertility of the female does not depend on the weight of food consumed by it, but is linearly dependent on the food consumption efficiency index. The greater the efficiency of food consumption by an individual, the higher its fertility. The data obtained in the course of experiments on the feeding caterpillars Aporia crataegi were compared with the data presented in the works of other authors and counted in the proposed model of consumption. Calculations allowed estimation of the critical value of food conversion price below which the energy balance is negative and the existence of an individual is not possible.
Essays on Applied Resource Economics Using Bioeconomic Optimization Models
Affuso, Ermanno
With rising demographic growth, there is increasing interest in analytical studies that assess alternative policies to provide an optimal allocation of scarce natural resources while ensuring environmental sustainability. This dissertation consists of three essays in applied resource economics that are interconnected methodologically within the agricultural production sector of Economics. The first chapter examines the sustainability of biofuels by simulating and evaluating an agricultural voluntary program that aims to increase the land use efficiency in the production of biofuels of first generation in the state of Alabama. The results show that participatory decisions may increase the net energy value of biofuels by 208% and reduce emissions by 26%; significantly contributing to the state energy goals. The second chapter tests the hypothesis of overuse of fertilizers and pesticides in U.S. peanut farming with respect to other inputs and address genetic research to reduce the use of the most overused chemical input. The findings suggest that peanut producers overuse fungicide with respect to any other input and that fungi resistant genetically engineered peanuts may increase the producer welfare up to 36.2%. The third chapter implements a bioeconomic model, which consists of a biophysical model and a stochastic dynamic recursive model that is used to measure potential economic and environmental welfare of cotton farmers derived from a rotation scheme that uses peanut as a complementary crop. The results show that the rotation scenario would lower farming costs by 14% due to nitrogen credits from prior peanut land use and reduce non-point source pollution from nitrogen runoff by 6.13% compared to continuous cotton farming.
Energy Technology Data Exchange (ETDEWEB)
Hong, J.H. [Kyungwon University, Songnam (Korea, Republic of)
1995-07-01
This paper describes a method of obtaining transmission network equivalents from the network`s response to a impulse excitation signal. Proposed method is based on the modal decomposition representation for the large-scale interconnected system. For this framework we use Prony analysis to identify the network function of the system and to decompose the large system into a parallel combination of simple first-order systems. As a result, rational network function of optimal low order can be obtained in a direct and simple way. And Thevenin-type of discrete-time filter model can be generated. It can reproduce the driving-point impedance characteristic of the network. Furthermore proposed model can be implemented into the EMTP in a direct manner. The simulation results with the full system representation and the developed equivalent system showed a good agreement. (author). 14 refs., 11 figs.
Hydro-economic Modeling: Reducing the Gap between Large Scale Simulation and Optimization Models
Forni, L.; Medellin-Azuara, J.; Purkey, D.; Joyce, B. A.; Sieber, J.; Howitt, R.
2012-12-01
The integration of hydrological and socio economic components into hydro-economic models has become essential for water resources policy and planning analysis. In this study we integrate the economic value of water in irrigated agricultural production using SWAP (a StateWide Agricultural Production Model for California), and WEAP (Water Evaluation and Planning System) a climate driven hydrological model. The integration of the models is performed using a step function approximation of water demand curves from SWAP, and by relating the demand tranches to the priority scheme in WEAP. In order to do so, a modified version of SWAP was developed called SWEAP that has the Planning Area delimitations of WEAP, a Maximum Entropy Model to estimate evenly sized steps (tranches) of water derived demand functions, and the translation of water tranches into crop land. In addition, a modified version of WEAP was created called ECONWEAP with minor structural changes for the incorporation of land decisions from SWEAP and series of iterations run via an external VBA script. This paper shows the validity of this integration by comparing revenues from WEAP vs. ECONWEAP as well as an assessment of the approximation of tranches. Results show a significant increase in the resulting agricultural revenues for our case study in California's Central Valley using ECONWEAP while maintaining the same hydrology and regional water flows. These results highlight the gains from allocating water based on its economic compared to priority-based water allocation systems. Furthermore, this work shows the potential of integrating optimization and simulation-based hydrologic models like ECONWEAP.ercentage difference in total agricultural revenues (EconWEAP versus WEAP).
Optimal weighted combinatorial forecasting model of QT dispersion of ECGs in Chinese adults.
Wen, Zhang; Miao, Ge; Xinlei, Liu; Minyi, Cen
2016-07-01
This study aims to provide a scientific basis for unifying the reference value standard of QT dispersion of ECGs in Chinese adults. Three predictive models including regression model, principal component model, and artificial neural network model are combined to establish the optimal weighted combination model. The optimal weighted combination model and single model are verified and compared. Optimal weighted combinatorial model can reduce predicting risk of single model and improve the predicting precision. The reference value of geographical distribution of Chinese adults' QT dispersion was precisely made by using kriging methods. When geographical factors of a particular area are obtained, the reference value of QT dispersion of Chinese adults in this area can be estimated by using optimal weighted combinatorial model and reference value of the QT dispersion of Chinese adults anywhere in China can be obtained by using geographical distribution figure as well.
Model reduction for dynamic real-time optimization of chemical processes
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 th
Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
1993-01-01
Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...
Heterogeneous Nuclear Reactor Models for Optimal Xenon Control.
Gondal, Ishtiaq Ahmad
Nuclear reactors are generally modeled as homogeneous mixtures of fuel, control, and other materials while in reality they are heterogeneous-homogeneous configurations comprised of fuel and control rods along with other materials. Similarly, for space-time studies of a nuclear reactor, homogeneous, usually one-group diffusion theory, models are used, and the system equations are solved by either nodal or modal expansion approximations. Study of xenon-induced problems has also been carried out using similar models and with the help of dynamic programming or classical calculus of variations or the minimum principle. In this study a thermal nuclear reactor is modeled as a two-dimensional lattice of fuel and control rods placed in an infinite-moderator in plane geometry. The two-group diffusion theory approximation is used for neutron transport. Space -time neutron balance equations are written for two groups and reduced to one space-time algebraic equation by using the two-dimensional Fourier transform. This equation is written at all fuel and control rod locations. Iodine -xenon and promethium-samarium dynamic equations are also written at fuel rod locations only. These equations are then linearized about an equilibrium point which is determined from the steady-state form of the original nonlinear system equations. After studying poisonless criticality, with and without control, and the stability of the open-loop system and after checking its controllability, a performance criterion is defined for the xenon-induced spatial flux oscillation problem in the form of a functional to be minimized. Linear -quadratic optimal control theory is then applied to solve the problem. To perform a variety of different additional useful studies, this formulation has potential for various extensions and variations; for example, different geometry of the problem, with possible extension to three dimensions, heterogeneous -homogeneous formulation to include, for example, homogeneously
Optimization and evaluation of probabilistic-logic sequence models
DEFF Research Database (Denmark)
Christiansen, Henning; Lassen, Ole Torp
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...... for preprocessing or splitting them into submodels. An evaluation method for approximating models is suggested based on automatic generation of samples. These models and evaluation processes are illustrated in the PRISM system developed by other authors....
Optimal control policies for continuous review production-inventory models
Germs, Remco; Foreest, Nicky D. van
2012-01-01
In this paper, we consider a stochastic version of a single-item production-inventory system in which the demand process is a mixture of a compound Poisson process and a constant demand rate. This model generalizes classical continuous-review single product inventory models with infinite planning horizon such as the EOQ model or production-inventory models with compound Poisson demand. We establish for the first time conditions on the inventory costs and the demand distribution such that the ...
Modeling and optimization of a binary geothermal power plant
2012-01-01
A model is developed for an existing organic Rankine cycle (ORC) utilizing a low temperature geothermal source. The model is implemented in Aspen Plus® and used to simulate the performance of the existing ORC equipped with an air-cooled condensation system. The model includes all the actual characteristics of the components. The model is validated by approximately 5000 measured data in a wide range of ambient temperatures. The net power output of the system is maximized. The results suggest d...
BREAST BIOMECANICAL MODELING FOR COMPRESSION OPTIMIZATION IN DIGITAL BREAST TOMOSYNTHESIS
Anna, Mîra; Carton, Ann-Katherine; Muller, Serge; Payan, Yohan
2016-01-01
International audience; The aim of this work is to develop a biomechanical Finite Element (FE) breast model in order to analyze different breast compression strategies and their impact on image quality. Large breast deformations will be simulated using this FE model. A particular attention will be granted to the computation of the initial stress in the model due to gravity and to boundary conditions imposed by the thorax anatomy. Finally, the model will be validated by comparing the estimated...
Optimal control policies for continuous review production-inventory models
Germs, Remco; Foreest, Nicky D. van
2012-01-01
In this paper, we consider a stochastic version of a single-item production-inventory system in which the demand process is a mixture of a compound Poisson process and a constant demand rate. This model generalizes classical continuous-review single product inventory models with infinite planning horizon such as the EOQ model or production-inventory models with compound Poisson demand. We establish for the first time conditions on the inventory costs and the demand distribution such that the ...
Siade, A. J.; Prommer, H.; Welter, D.
2014-12-01
Groundwater management and remediation requires the implementation of numerical models in order to evaluate the potential anthropogenic impacts on aquifer systems. In many situations, the numerical model must, not only be able to simulate groundwater flow and transport, but also geochemical and biological processes. Each process being simulated carries with it a set of parameters that must be identified, along with differing potential sources of model-structure error. Various data types are often collected in the field and then used to calibrate the numerical model; however, these data types can represent very different processes and can subsequently be sensitive to the model parameters in extremely complex ways. Therefore, developing an appropriate weighting strategy to address the contributions of each data type to the overall least-squares objective function is not straightforward. This is further compounded by the presence of potential sources of model-structure errors that manifest themselves differently for each observation data type. Finally, reactive transport models are highly nonlinear, which can lead to convergence failure for algorithms operating on the assumption of local linearity. In this study, we propose a variation of the popular, particle swarm optimization algorithm to address trade-offs associated with the calibration of one data type over another. This method removes the need to specify weights between observation groups and instead, produces a multi-dimensional Pareto front that illustrates the trade-offs between data types. We use the PEST++ run manager, along with the standard PEST input/output structure, to implement parallel programming across multiple desktop computers using TCP/IP communications. This allows for very large swarms of particles without the need of a supercomputing facility. The method was applied to a case study in which modeling was used to gain insight into the mobilization of arsenic at a deepwell injection site
Optimization and modeling of cellulase protein from Trichoderma ...
African Journals Online (AJOL)
AJB SERVER
2007-01-04
Jan 4, 2007 ... Logistic kinetic model was the best model for the mixed substrates. A conceptual Artificial Neural. Network (ANN) model was well incorporated in the fermentative production of cellulase. ... RSM to evaluate the effects of the medium parameters ... Experiments were performed along the steepest ascent.
An Optimization Model and DPSO-EDA for Document Summarization
Directory of Open Access Journals (Sweden)
Rasim M. Alguliev
2011-11-01
Full Text Available We model document summarization as a nonlinear 0-1 programming problem where an objective function is defined as Heronian mean of the objective functions enforcing the coverage and diversity. The proposed model implemented on a multi-document summarization task. Experiments on DUC2001 and DUC2002 datasets showed that the proposed model outperforms the other summarization methods.
An automatic and effective parameter optimization method for model tuning
Directory of Open Access Journals (Sweden)
T. Zhang
2015-11-01
simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.
Havinga, Gosse Tjipke; van den Boogaard, Antonius H.; Klaseboer, G.
2013-01-01
Surrogate models are used within the sequential optimization strategy for forming processes. A sequential improvement (SI) scheme is used to refine the surrogate model in the optimal region. One of the popular surrogate modeling methods for SI is Kriging. However, the global response of Kriging mode
Optimization of a new flow design for solid oxide cells using computational fluid dynamics modelling
DEFF Research Database (Denmark)
Duhn, Jakob Dragsbæk; Jensen, Anker Degn; Wedel, Stig;
2016-01-01
Design of a gas distributor to distribute gas flow into parallel channels for Solid Oxide Cells (SOC) is optimized, with respect to flow distribution, using Computational Fluid Dynamics (CFD) modelling. The CFD model is based on a 3d geometric model and the optimized structural parameters include...
A novel macroscopic traffic model based on generalized optimal velocity model
Institute of Scientific and Technical Information of China (English)
Zhou Xuan-Hao; Lu Yong-Zai
2011-01-01
In this paper, we adopt the coarse graining method proposed by Lee H K et al. to develop a macroscopic model from the microscopic traffic model-GOVM. The proposed model inherits the pararneter p which considers the influence of next-neareet car introduced in the GOVM model. The simulation results show that the new model is strictly consistent with the former microscopic model. Using this macroscopic model, we can avoid considering the details of each traffic on the road, and build more complex models such as road network model easily in the future.
Energy Technology Data Exchange (ETDEWEB)
Lashkar Ara, A., E-mail: Lashkarara@iust.ac.i [Department of Electrical Engineering, Iran University of Science and Technology, Narmak, Tehran, P.O. Box 1684613114 (Iran, Islamic Republic of); Kazemi, A., E-mail: Kazemi@iust.ac.i [Department of Electrical Engineering, Iran University of Science and Technology, Tehran (Iran, Islamic Republic of); Nabavi Niaki, S.A., E-mail: nabavi.niaki@utoronto.c [Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, M5 S 3G4 (Canada)
2011-02-15
In this paper a hybrid configuration of a FACTS controller called Optimal Unified Power Flow Controller (OUPFC) which is composed of a mechanical phase shifting transformer augmented with a small scale Unified Power Flow Controller (UPFC) is introduced. The steady-state model of OUPFC is developed as a power injection model. This model is used to develop an Optimal Power Flow (OPF) algorithm including OUPFC to find the optimum number, location, and settings of OUPFCs to minimize the total fuel cost and power losses. Simulation results are presented for the IEEE 14-, 30-, and 118-bus systems. The optimization method is numerically solved using Matlab and General Algebraic Modelling System (GAMS) software environments. The results demonstrate the effectiveness of the proposed approach to solve the optimal location and settings of OUPFCs incorporated in OPF problem and improve the power system operation. Furthermore, the ability of OUPFC to optimize the objective functions is compared to that of PST and UPFC.
Dynamic Cell Formation based on Multi-objective Optimization Model
Directory of Open Access Journals (Sweden)
Guozhu Jia
2013-08-01
Full Text Available In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.
Optimization model using Markowitz model approach for reducing the number of dengue cases in Bandung
Yong, Benny; Chin, Liem
2017-05-01
Dengue fever is one of the most serious diseases and this disease can cause death. Currently, Indonesia is a country with the highest cases of dengue disease in Southeast Asia. Bandung is one of the cities in Indonesia that is vulnerable to dengue disease. The sub-districts in Bandung had different levels of relative risk of dengue disease. Dengue disease is transmitted to people by the bite of an Aedesaegypti mosquito that is infected with a dengue virus. Prevention of dengue disease is by controlling the vector mosquito. It can be done by various methods, one of the methods is fogging. The efforts made by the Health Department of Bandung through fogging had constraints in terms of limited funds. This problem causes Health Department selective in fogging, which is only done for certain locations. As a result, many sub-districts are not handled properly by the Health Department because of the unequal distribution of activities to prevent the spread of dengue disease. Thus, it needs the proper allocation of funds to each sub-district in Bandung for preventing dengue transmission optimally. In this research, the optimization model using Markowitz model approach will be applied to determine the allocation of funds should be given to each sub-district in Bandung. Some constraints will be added to this model and the numerical solution will be solved with generalized reduced gradient method using Solver software. The expected result of this research is the proportion of funds given to each sub-district in Bandung correspond to the level of risk of dengue disease in each sub-district in Bandung so that the number of dengue cases in this city can be reduced significantly.
Polymer Electrolyte Membrane (PEM) Fuel Cells Modeling and Optimization
Zhang, Zhuqian; Wang, Xia; Shi, Zhongying; Zhang, Xinxin; Yu, Fan
2006-11-01
Performance of polymer electrolyte membrane (PEM) fuel cells is dependent on operating parameters and designing parameters. Operating parameters mainly include temperature, pressure, humidity and the flow rate of the inlet reactants. Designing parameters include reactants distributor patterns and dimensions, electrodes dimensions, and electrodes properties such as porosity, permeability and so on. This work aims to investigate the effects of various designing parameters on the performance of PEM fuel cells, and the optimum values will be determined under a given operating condition.A three-dimensional steady-state electrochemical mathematical model was established where the mass, fluid and thermal transport processes are considered as well as the electrochemical reaction. A Powell multivariable optimization algorithm will be applied to investigate the optimum values of designing parameters. The objective function is defined as the maximum potential of the electrolyte fluid phase at the membrane/cathode interface at a typical value of the cell voltage. The robustness of the optimum design of the fuel cell under different cell potentials will be investigated using a statistical sensitivity analysis. By comparing with the reference case, the results obtained here provide useful tools for a better design of fuel cells.
Heterogeneous SIS model for directed networks and optimal immunization
Ottaviano, Stefania; Bonaccorsi, Stefano
2016-01-01
We investigate the influence of a contact network structure over the spread of epidemics in an heterogeneous population. Basically the epidemics spreads over a directed weighted graph. We describe the epidemic process as a continuous-time individual-based susceptible-infected-susceptible (SIS) model using a first-order mean-field approximation. First we consider a network without a specific topology, investigating the epidemic threshold and the stability properties of the system. Then we analyze the case of a community network, relying on the graph-theoretical notion of equitable partition, and using a lower-dimensional dynamical system in order to individuate the epidemic threshold. Moreover we prove that the positive steady-state of the original system, that appears above the threshold, can be computed by this lower-dimensional system. In the second part of the paper we treat the important issue of the infectious disease control. Taking into account the connectivity of the network, we provide a cost-optimal...
OPTIMAL TRAINING POLICY FOR PROMOTION - STOCHASTIC MODELS OF MANPOWER SYSTEMS
Directory of Open Access Journals (Sweden)
V.S.S. Yadavalli
2012-01-01
Full Text Available In this paper, the optimal planning of manpower training programmes in a manpower system with two grades is discussed. The planning of manpower training within a given organization involves a trade-off between training costs and expected return. These planning problems are examined through models that reflect the random nature of manpower movement in two grades. To be specific, the system consists of two grades, grade 1 and grade 2. Any number of persons in grade 2 can be sent for training and after the completion of training, they will stay in grade 2 and will be given promotion as and when vacancies arise in grade 1. Vacancies arise in grade 1 only by wastage. A person in grade 1 can leave the system with probability p. Vacancies are filled with persons in grade 2 who have completed the training. It is assumed that there is a perfect passing rate and that the sizes of both grades are fixed. Assuming that the planning horizon is finite and is T, the underlying stochastic process is identified as a finite state Markov chain and using dynamic programming, a policy is evolved to determine how many persons should be sent for training at any time k so as to minimize the total expected cost for the entire planning period T.
Li, Zejing
2012-01-01
This dissertation is mainly devoted to the research of two problems - the continuous-time portfolio optimization in different Wishart models and the effects of discrete rebalancing on portfolio wealth distribution and optimal portfolio strategy.
Bottom friction optimization for barotropic tide modelling using the HYbrid Coordinate Ocean Model
Boutet, Martial; Lathuilière, Cyril; Baraille, Rémy; Son Hoang, Hong; Morel, Yves
2014-05-01
We can list several ways to improve tide modelling at a regional or coastal scale: a more precise and refined bathymetry, better boundary conditions (the way they are implemented and the precision of global tide atlases used) and the representation of the dissipation linked to the bottom friction. Nevertheless, the most promising improvement is the bottom friction representation. Indeed, bathymetric databases, especially in coastal areas, are more and more precise and global tide models performances are better than ever (mean discrepancy between models and tide gauges is about 1 cm for M2 tide). Bottom friction is often parameterized with a quadratic term and a constant coefficient generally taken between 2.5 10-3 and 3.0 10-3. Consequently, we need a more physically consistent approach to improve bottom friction in coastal areas. The first improvement is to enable the computation of a time- and space-dependent friction coefficient. It is obtained by vertical integration of a turbulent horizontal velocity profile. The new parameter to be prescribed for the computation is the bottom roughness, z0, that depends on a large panel of physical properties and processes (sediment properties, existence of ripples and dunes, wave-current interactions, ...). The context of increasing computer resources and data availability enables the possibility to use new methods of data assimilation and optimization. The method used for this study is the simultaneous perturbation stochastic approximation (SPSA) which consists in the approximation of the gradient based on a fixed number of cost function measurements, regardless of the dimension of the vector to be estimated. Indeed, each cost function measurement is obtained by randomly perturbing every component of the parameter vector. An important feature of SPSA is its relative ease of implementation. In particular, the method does not require the development of linear and adjoint version of the circulation model. The algorithm is
Optimal Vaccination in a Stochastic Epidemic Model of Two Non-Interacting Populations
2015-02-17
RESEARCH ARTICLE Optimal Vaccination in a Stochastic Epidemic Model of Two Non-Interacting Populations Edwin C. Yuan1,3, David L. Alderson2, Sean...Infected-Recovered (SIR) model. Based on these results, we determine the optimal alloca- tions of a limited quantity of vaccine between two non-interacting... vaccine , the deterministic model is a poor estimate of the optimal strategy for the more realistic, stochastic case. Introduction As rapid, long-range
Liqiang Liu; Yuntao Dai; Jinyu Gao
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules...
Optimal minimax designs over a prespecified interval in a heteroscedastic polynomial model.
Chen, Ray-Bing; Wong, Weng Kee; Li, Kun-Yu
2008-09-15
Minimax optimal designs can be useful for estimating response surface but they are notoriously difficult to study analytically. We provide formulae for three types of minimax optimal designs over a user-specified region. We focus on polynomial models with various types of heteroscedastic errors but the design strategy is applicable to other types of linear models and optimality criteria. Relationships among the three types of minimax optimal designs are discussed.
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)
Models and Methods for Structural Topology Optimization with Discrete Design Variables
DEFF Research Database (Denmark)
Stolpe, Mathias
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...... or stresses, or fundamental frequencies. The design variables are either continuous or discrete and model dimensions, thicknesses, densities, or material properties. Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures...... 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...
Optimal mutation rates in dynamic environments: The eigen model
Ancliff, Mark; Park, Jeong-Man
2011-03-01
We consider the Eigen quasispecies model with a dynamic environment. For an environment with sharp-peak fitness in which the most-fit sequence moves by k spin-flips each period T we find an asymptotic stationary state in which the quasispecies population changes regularly according to the regular environmental change. From this stationary state we estimate the maximum and the minimum mutation rates for a quasispecies to survive under the changing environment and calculate the optimum mutation rate that maximizes the population growth. Interestingly we find that the optimum mutation rate in the Eigen model is lower than that in the Crow-Kimura model, and at their optimum mutation rates the corresponding mean fitness in the Eigen model is lower than that in the Crow-Kimura model, suggesting that the mutation process which occurs in parallel to the replication process as in the Crow-Kimura model gives an adaptive advantage under changing environment.
Institute of Scientific and Technical Information of China (English)
ZHAO Peng; MU Xin; YAO Jin-hua; WANG Yong; YANG Xiu-tai
2007-01-01
We established an integrated and optimized model of vehicle scheduling problem and vehicle filling problem for solving an extremely complex delivery mode-multi-type vehicles, non-full loads, pickup and delivery in logistics and delivery system. The integrated and optimized model is based on our previous research result-effective space method. An integrated algorithm suitable for the integrated and optimized model was proposed and corresponding computer programs were designed to solve practical problems. The results indicates the programs can work out optimized delivery routes and concrete loading projects. The model and algorithm have many virtues and are valuable in practice.
Dynamic ASE Modeling and Optimization of Aircraft with SpaRibs Project
National Aeronautics and Space Administration — We propose development and demonstration of a dynamic aeroservoelastic modeling and optimization system based on curvilinear internal structural arrangements of...
An epidemic model for cholera with optimal control treatment
Lemos-Paiao, Ana P.; Silva, Cristiana J.; Torres, Delfim F. M.
2016-01-01
We propose a mathematical model for cholera with treatment through quarantine. The model is shown to be both epidemiologically and mathematically well posed. In particular, we prove that all solutions of the model are positive and bounded; and that every solution with initial conditions in a certain meaningful set remains in that set for all time. The existence of unique disease-free and endemic equilibrium points is proved and the basic reproduction number is computed. Then, we study the loc...