Statistical models for optimizing mineral exploration

International Nuclear Information System (INIS)

Wignall, T.K.; DeGeoffroy, J.

1987-01-01

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

Rethinking exchange market models as optimization algorithms

Science.gov (United States)

Luquini, Evandro; Omar, Nizam

2018-02-01

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

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

Science.gov (United States)

Liu, Qiang; Wan, Xiaoxia; Xie, Dehong

2014-06-01

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

Following an Optimal Batch Bioreactor Operations Model

DEFF Research Database (Denmark)

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

2012-01-01

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

Mathematical modeling and optimization of complex structures

CERN Document Server

Repin, Sergey; Tuovinen, Tero

2016-01-01

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

Review: Optimization methods for groundwater modeling and management

Science.gov (United States)

Yeh, William W.-G.

2015-09-01

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

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

Science.gov (United States)

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

2018-03-01

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

Optimal Hedging with the Vector Autoregressive Model

NARCIS (Netherlands)

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

2014-01-01

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

Pareto-Optimal Model Selection via SPRINT-Race.

Science.gov (United States)

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

2018-02-01

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

Optimal velocity difference model for a car-following theory

International Nuclear Information System (INIS)

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

2011-01-01

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

Multipurpose optimization models for high level waste vitrification

International Nuclear Information System (INIS)

Hoza, M.

1994-08-01

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

Intelligent structural optimization: Concept, Model and Methods

International Nuclear Information System (INIS)

Lu, Dagang; Wang, Guangyuan; Peng, Zhang

2002-01-01

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

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...

Computer models for optimizing radiation therapy

International Nuclear Information System (INIS)

Duechting, W.

1998-01-01

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

Vector-model-supported approach in prostate plan optimization

International Nuclear Information System (INIS)

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

2017-01-01

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

Vector-model-supported approach in prostate plan optimization

Energy Technology Data Exchange (ETDEWEB)

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

2017-07-01

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

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.

Pavement maintenance optimization model using Markov Decision Processes

Science.gov (United States)

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

2017-09-01

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

Fuzzy Simulation-Optimization Model for Waste Load Allocation

Directory of Open Access Journals (Sweden)

Motahhare Saadatpour

2006-01-01

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

Optimization and evaluation of probabilistic-logic sequence models

DEFF Research Database (Denmark)

Christiansen, Henning; Lassen, Ole Torp

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

Simplified ejector model for control and optimization

International Nuclear Information System (INIS)

Zhu Yinhai; Cai Wenjian; Wen Changyun; Li Yanzhong

2008-01-01

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

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

International Nuclear Information System (INIS)

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

2015-01-01

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

Hierarchical Swarm Model: A New Approach to Optimization

Directory of Open Access Journals (Sweden)

Hanning Chen

2010-01-01

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

Modeling and optimization of LCD optical performance

CERN Document Server

Yakovlev, Dmitry A; Kwok, Hoi-Sing

2015-01-01

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

A Framework for the Optimization of Discrete-Event Simulation Models

Science.gov (United States)

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

1996-01-01

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

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.

Enhanced index tracking modelling in portfolio optimization

Science.gov (United States)

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

2013-09-01

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

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

International Nuclear Information System (INIS)

Chun, Ji Han; Lee, Un Chul

2008-01-01

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

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

Science.gov (United States)

Aprasoff, Jonathan; Donchin, Opher

2012-04-01

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

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

International Nuclear Information System (INIS)

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

2016-01-01

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

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.

In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data

Science.gov (United States)

Yi, Yeon-Sook

2017-01-01

This study compares five cognitive diagnostic models in search of optimal one(s) for English as a Second Language grammar test data. Using a unified modeling framework that can represent specific models with proper constraints, the article first fit the full model (the log-linear cognitive diagnostic model, LCDM) and investigated which model…

Maintenance Optimization of High Voltage Substation Model

Directory of Open Access Journals (Sweden)

Radim Bris

2008-01-01

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

A study on an optimal movement model

Energy Technology Data Exchange (ETDEWEB)

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

2003-07-11

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

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

Directory of Open Access Journals (Sweden)

Devaraj Jayachandran

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

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

Science.gov (United States)

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

2015-01-01

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

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

Directory of Open Access Journals (Sweden)

Wu Jianhua

2014-03-01

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

Hierarchical models and iterative optimization of hybrid systems

Energy Technology Data Exchange (ETDEWEB)

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

2016-06-08

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

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.

Modeling, simulation and optimization of bipedal walking

CERN Document Server

Berns, Karsten

2013-01-01

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

A useful framework for optimal replacement models

International Nuclear Information System (INIS)

Aven, Terje; Dekker, Rommert

1997-01-01

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

Optimal treatment interruptions control of TB transmission model

Science.gov (United States)

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

2018-03-01

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

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Efficient Iris Localization via Optimization Model

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Qi Wang

2017-01-01

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

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

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.

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

Science.gov (United States)

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

2011-08-25

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

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

Directory of Open Access Journals (Sweden)

Sorribas Albert

2011-08-01

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

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.

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...

Modeling investor optimism with fuzzy connectives

NARCIS (Netherlands)

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

2009-01-01

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

Mathematical model of highways network optimization

Science.gov (United States)

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

2017-12-01

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

Cost optimization model and its heuristic genetic algorithms

International Nuclear Information System (INIS)

Liu Wei; Wang Yongqing; Guo Jilin

1999-01-01

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

An optimization model for metabolic pathways.

Science.gov (United States)

Planes, F J; Beasley, J E

2009-10-15

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

Adaptive surrogate model based multiobjective optimization for coastal aquifer management

Science.gov (United States)

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

2018-06-01

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

An optimization strategy for a biokinetic model of inhaled radionuclides

International Nuclear Information System (INIS)

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

1991-01-01

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

Multiple Surrogate Modeling for Wire-Wrapped Fuel Assembly Optimization

International Nuclear Information System (INIS)

Raza, Wasim; Kim, Kwang-Yong

2007-01-01

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

Multi-objective optimization of GENIE Earth system models.

Science.gov (United States)

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

2009-07-13

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

Model averaging, optimal inference and habit formation

Directory of Open Access Journals (Sweden)

Thomas H B FitzGerald

2014-06-01

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

Development of optimized dosimetric models for HDR brachytherapy

International Nuclear Information System (INIS)

Thayalan, K.; Jagadeesan, M.

2003-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2014-12-15

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

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

International Nuclear Information System (INIS)

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

2014-01-01

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

Analysis and optimization of a camber morphing wing model

Directory of Open Access Journals (Sweden)

Bing Li

2016-09-01

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

Optimal time points sampling in pathway modelling.

Science.gov (United States)

Hu, Shiyan

2004-01-01

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

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

Science.gov (United States)

Fernandez, J.

2015-06-01

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

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

Directory of Open Access Journals (Sweden)

Mo LI,Ping GUO,Liudong ZHANG,Chenglong ZHANG

2016-03-01

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

Groundwater Pollution Source Identification using Linked ANN-Optimization Model

Science.gov (United States)

Ayaz, Md; Srivastava, Rajesh; Jain, Ashu

2014-05-01

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

Computer modeling for optimal placement of gloveboxes

International Nuclear Information System (INIS)

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

1997-08-01

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

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.

Modeling and energy efficiency optimization of belt conveyors

International Nuclear Information System (INIS)

Zhang, Shirong; Xia, Xiaohua

2011-01-01

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

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.

Optimal Model-Based Control in HVAC Systems

DEFF Research Database (Denmark)

Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik

2008-01-01

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

Optimization of A(2)O BNR processes using ASM and EAWAG Bio-P models: model performance.

Science.gov (United States)

El Shorbagy, Walid E; Radif, Nawras N; Droste, Ronald L

2013-12-01

This paper presents the performance of an optimization model for a biological nutrient removal (BNR) system using the anaerobic-anoxic-oxic (A(2)O) process. The formulated model simulates removal of organics, nitrogen, and phosphorus using a reduced International Water Association (IWA) Activated Sludge Model #3 (ASM3) model and a Swiss Federal Institute for Environmental Science and Technology (EAWAG) Bio-P module. Optimal sizing is attained considering capital and operational costs. Process performance is evaluated against the effect of influent conditions, effluent limits, and selected parameters of various optimal solutions with the following results: an increase of influent temperature from 10 degrees C to 25 degrees C decreases the annual cost by about 8.5%, an increase of influent flow from 500 to 2500 m(3)/h triples the annual cost, the A(2)O BNR system is more sensitive to variations in influent ammonia than phosphorus concentration and the maximum growth rate of autotrophic biomass was the most sensitive kinetic parameter in the optimization model.

Multiobjective optimization of an extremal evolution model

International Nuclear Information System (INIS)

Elettreby, M.F.

2004-09-01

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

Model reduction for dynamic real-time optimization of chemical processes

NARCIS (Netherlands)

Van den Berg, J.

2005-01-01

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

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

Directory of Open Access Journals (Sweden)

Adel Taieb

2017-01-01

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

Learning optimal quantum models is NP-hard

Science.gov (United States)

Stark, Cyril J.

2018-02-01

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

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

Science.gov (United States)

Yu, Sen; Lu, Hongwei

2018-04-01

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

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

Science.gov (United States)

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

2009-10-01

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

Empty tracks optimization based on Z-Map model

Science.gov (United States)

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

2017-12-01

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

Modelling, simulating and optimizing boiler heating surfaces and evaporator circuits

DEFF Research Database (Denmark)

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

2003-01-01

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

Optimality models in the age of experimental evolution and genomics.

Science.gov (United States)

Bull, J J; Wang, I-N

2010-09-01

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

Optimality models in the age of experimental evolution and genomics

OpenAIRE

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

2010-01-01

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

Optimization Models and Methods Developed at the Energy Systems Institute

OpenAIRE

N.I. Voropai; V.I. Zorkaltsev

2013-01-01

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

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.

Modelling and Optimizing Mathematics Learning in Children

Science.gov (United States)

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

2013-01-01

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

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.

Robust and fast nonlinear optimization of diffusion MRI microstructure models.

Science.gov (United States)

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

2017-07-15

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

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

Science.gov (United States)

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

2018-01-01

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

AN OPTIMAL MAINTENANCE MANAGEMENT MODEL FOR AIRPORT CONCRETE PAVEMENT

Science.gov (United States)

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

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

Decision Support Model for Optimal Management of Coastal Gate

Science.gov (United States)

Ditthakit, Pakorn; Chittaladakorn, Suwatana

2010-05-01

The coastal areas are intensely settled by human beings owing to their fertility of natural resources. However, at present those areas are facing with water scarcity problems: inadequate water and poor water quality as a result of saltwater intrusion and inappropriate land-use management. To solve these problems, several measures have been exploited. The coastal gate construction is a structural measure widely performed in several countries. This manner requires the plan for suitably operating coastal gates. Coastal gate operation is a complicated task and usually concerns with the management of multiple purposes, which are generally conflicted one another. This paper delineates the methodology and used theories for developing decision support modeling for coastal gate operation scheduling. The developed model was based on coupling simulation and optimization model. The weighting optimization technique based on Differential Evolution (DE) was selected herein for solving multiple objective problems. The hydrodynamic and water quality models were repeatedly invoked during searching the optimal gate operations. In addition, two forecasting models:- Auto Regressive model (AR model) and Harmonic Analysis model (HA model) were applied for forecasting water levels and tide levels, respectively. To demonstrate the applicability of the developed model, it was applied to plan the operations for hypothetical system of Pak Phanang coastal gate system, located in Nakhon Si Thammarat province, southern part of Thailand. It was found that the proposed model could satisfyingly assist decision-makers for operating coastal gates under various environmental, ecological and hydraulic conditions.

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

An optimal control model of crop thinning in viticulture

OpenAIRE

Schamel Guenter H.; Schubert Stefan F.

2016-01-01

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

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

Science.gov (United States)

Aguilo, Miguel A.; Warner, James E.

2017-01-01

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

Fuzzy multiobjective models for optimal operation of a hydropower system

Science.gov (United States)

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

2013-06-01

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

Optimal control of information epidemics modeled as Maki Thompson rumors

Science.gov (United States)

Kandhway, Kundan; Kuri, Joy

2014-12-01

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

Models and Methods for Structural Topology Optimization with Discrete Design Variables

DEFF Research Database (Denmark)

Stolpe, Mathias

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

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

Science.gov (United States)

Oremland, Matthew; Laubenbacher, Reinhard

2015-03-01

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

Method to determine the optimal constitutive model from spherical indentation tests

Directory of Open Access Journals (Sweden)

Tairui Zhang

2018-03-01

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

Multi-objective optimization for generating a weighted multi-model ensemble

Science.gov (United States)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

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

Directory of Open Access Journals (Sweden)

Bambang Wahono

2014-01-01

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

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

Directory of Open Access Journals (Sweden)

Christopher eDiMattina

2013-06-01

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

Optimization of large-scale heterogeneous system-of-systems models.

Energy Technology Data Exchange (ETDEWEB)

Parekh, Ojas; Watson, Jean-Paul; Phillips, Cynthia Ann; Siirola, John; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Lee, Herbert K. H. (University of California, Santa Cruz, Santa Cruz, CA); Hart, William Eugene; Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Woodruff, David L. (University of California, Davis, Davis, CA)

2012-01-01

Decision makers increasingly rely on large-scale computational models to simulate and analyze complex man-made systems. For example, computational models of national infrastructures are being used to inform government policy, assess economic and national security risks, evaluate infrastructure interdependencies, and plan for the growth and evolution of infrastructure capabilities. A major challenge for decision makers is the analysis of national-scale models that are composed of interacting systems: effective integration of system models is difficult, there are many parameters to analyze in these systems, and fundamental modeling uncertainties complicate analysis. This project is developing optimization methods to effectively represent and analyze large-scale heterogeneous system of systems (HSoS) models, which have emerged as a promising approach for describing such complex man-made systems. These optimization methods enable decision makers to predict future system behavior, manage system risk, assess tradeoffs between system criteria, and identify critical modeling uncertainties.

Optimal transportation networks models and theory

CERN Document Server

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.

A tutorial on fundamental model structures for railway timetable optimization

DEFF Research Database (Denmark)

Harrod, Steven

2012-01-01

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

Constrained optimization via simulation models for new product innovation

Science.gov (United States)

Pujowidianto, Nugroho A.

2017-11-01

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

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

DEFF Research Database (Denmark)

McClary, Dan; Syrotiuk, Violet; Kulahci, Murat

2010-01-01

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

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.

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.

Modeling of biological intelligence for SCM system optimization.

Science.gov (United States)

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

2012-01-01

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

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.

Modeling of Biological Intelligence for SCM System Optimization

Science.gov (United States)

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

2012-01-01

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

A stochastic discrete optimization model for designing container terminal facilities

Science.gov (United States)

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

2017-11-01

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

An optimal control model of crop thinning in viticulture

Directory of Open Access Journals (Sweden)

Schamel Guenter H.

2016-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2001-06-01

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

Optimal modeling and forecasting of the energy consumption and production in China

International Nuclear Information System (INIS)

Xiong, Ping-ping; Dang, Yao-guo; Yao, Tian-xiang; Wang, Zheng-xin

2014-01-01

Energy is of fundamental importance to a nation's economy. Accurate prediction of the energy consumption and production in China can play a guiding role in making the energy consumption plan, and facilitate timely and effective decision making of energy policy. This article proposes a novel GM (gray model) (1,1) model based on optimizing initial condition according to the principle of new information priority. The optimized model and five other GM (1,1) models are applied in the modeling of China's energy consumption and production. Both the simulation and prediction accuracy of the models are compared and analyzed. We obtain the result that the optimized model has higher prediction accuracy than the other five models. Therefore, the presented optimized model is further utilized to predict China's energy consumption and production from 2013 to 2017. The result indicates that China's energy consumption and production will keep increasing and the gap between the energy production and consumption will also be increasing. Finally, we predict Iran's and Argentina's energy consumption to further prove the effectiveness of the proposed model. - Highlights: • We proposed a novel GM (1,1) model based on optimizing initial condition. • The prediction accuracy of the proposed model is better than the other models. • We used the proposed model to predict China's energy consumption and production. • The proposed model can be used to predict other countries' energy consumption

Space engineering modeling and optimization with case studies

CERN Document Server

Pintér, János

2016-01-01

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

Modeling, Analysis, and Optimization Issues for Large Space Structures

Science.gov (United States)

Pinson, L. D. (Compiler); Amos, A. K. (Compiler); Venkayya, V. B. (Compiler)

1983-01-01

Topics concerning the modeling, analysis, and optimization of large space structures are discussed including structure-control interaction, structural and structural dynamics modeling, thermal analysis, testing, and design.

PEM fuel cell model suitable for energy optimization purposes

International Nuclear Information System (INIS)

Caux, S.; Hankache, W.; Fadel, M.; Hissel, D.

2010-01-01

Many fuel cell stack models or fuel cell system models exist. A model must be built with a main objective, sometimes for accurate electro-chemical behavior description, sometimes for optimization procedure at a system level. In this paper, based on the fundamental reactions present in a fuel cell stack, an accurate model and identification procedure is presented for future energy management in a Hybrid Electrical Vehicle (HEV). The proposed approach extracts all important state variables in such a system and based on the control of the fuel cell's gas flows and temperature, simplification arises to a simple electrical model. Assumptions verified due to the control of the stack allow simplifying the relationships within keeping accuracy in the description of a global fuel cell stack behavior from current demand to voltage. Modeled voltage and current dynamic behaviors are compared with actual measurements. The obtained accuracy is sufficient and less time-consuming (versus other previously published system-oriented models) leading to a suitable model for optimization iterative off-line algorithms.

PEM fuel cell model suitable for energy optimization purposes

Energy Technology Data Exchange (ETDEWEB)

Caux, S.; Hankache, W.; Fadel, M. [LAPLACE/CODIASE: UMR CNRS 5213, Universite de Toulouse - INPT, UPS, - ENSEEIHT: 2 rue Camichel BP7122, 31071 Toulouse (France); CNRS, LAPLACE, F-31071 Toulouse (France); Hissel, D. [FEMTO-ST ENISYS/FCLAB, UMR CNRS 6174, University of Franche-Comte, Rue Thierry Mieg, 90010 Belfort (France)

2010-02-15

Many fuel cell stack models or fuel cell system models exist. A model must be built with a main objective, sometimes for accurate electro-chemical behavior description, sometimes for optimization procedure at a system level. In this paper, based on the fundamental reactions present in a fuel cell stack, an accurate model and identification procedure is presented for future energy management in a Hybrid Electrical Vehicle (HEV). The proposed approach extracts all important state variables in such a system and based on the control of the fuel cell's gas flows and temperature, simplification arises to a simple electrical model. Assumptions verified due to the control of the stack allow simplifying the relationships within keeping accuracy in the description of a global fuel cell stack behavior from current demand to voltage. Modeled voltage and current dynamic behaviors are compared with actual measurements. The obtained accuracy is sufficient and less time-consuming (versus other previously published system-oriented models) leading to a suitable model for optimization iterative off-line algorithms. (author)

Nuclear-fuel-cycle optimization: methods and modelling techniques

International Nuclear Information System (INIS)

Silvennoinen, P.

1982-01-01

This book present methods applicable to analyzing fuel-cycle logistics and optimization as well as in evaluating the economics of different reactor strategies. After an introduction to the phases of a fuel cycle, uranium cost trends are assessed in a global perspective. Subsequent chapters deal with the fuel-cycle problems faced by a power utility. The fuel-cycle models cover the entire cycle from the supply of uranium to the disposition of spent fuel. The chapter headings are: Nuclear Fuel Cycle, Uranium Supply and Demand, Basic Model of the LWR (light water reactor) Fuel Cycle, Resolution of Uncertainties, Assessment of Proliferation Risks, Multigoal Optimization, Generalized Fuel-Cycle Models, Reactor Strategy Calculations, and Interface with Energy Strategies. 47 references, 34 figures, 25 tables

Global Optimization Ensemble Model for Classification Methods

Science.gov (United States)

Anwar, Hina; Qamar, Usman; Muzaffar Qureshi, Abdul Wahab

2014-01-01

Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC) that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity. PMID:24883382

Global Optimization Ensemble Model for Classification Methods

Directory of Open Access Journals (Sweden)

Hina Anwar

2014-01-01

Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

Optimization of hybrid model on hajj travel

Science.gov (United States)

Cahyandari, R.; Ariany, R. L.; Sukono

2018-03-01

Hajj travel insurance is an insurance product offered by the insurance company in preparing funds to perform the pilgrimage. This insurance product helps would-be pilgrims to set aside a fund of saving hajj with regularly, but also provides funds of profit sharing (mudharabah) and insurance protection. Scheme of insurance product fund management is largely using the hybrid model, which is the fund from would-be pilgrims will be divided into three account management, that is personal account, tabarru’, and ujrah. Scheme of hybrid model on hajj travel insurance was already discussed at the earlier paper with titled “The Hybrid Model Algorithm on Sharia Insurance”, taking the example case of Mitra Mabrur Plus product from Bumiputera company. On these advanced paper, will be made the previous optimization model design, with partition of benefit the tabarru’ account. Benefits such as compensation for 40 critical illness which initially only for participants of insurance only, on optimization is intended for participants of the insurance and his heir, also to benefit the hospital bills. Meanwhile, the benefits of death benefit is given if the participant is fixed die.

Web malware spread modelling and optimal control strategies

Science.gov (United States)

Liu, Wanping; Zhong, Shouming

2017-02-01

The popularity of the Web improves the growth of web threats. Formulating mathematical models for accurate prediction of malicious propagation over networks is of great importance. The aim of this paper is to understand the propagation mechanisms of web malware and the impact of human intervention on the spread of malicious hyperlinks. Considering the characteristics of web malware, a new differential epidemic model which extends the traditional SIR model by adding another delitescent compartment is proposed to address the spreading behavior of malicious links over networks. The spreading threshold of the model system is calculated, and the dynamics of the model is theoretically analyzed. Moreover, the optimal control theory is employed to study malware immunization strategies, aiming to keep the total economic loss of security investment and infection loss as low as possible. The existence and uniqueness of the results concerning the optimality system are confirmed. Finally, numerical simulations show that the spread of malware links can be controlled effectively with proper control strategy of specific parameter choice.

Runtime Optimizations for Tree-Based Machine Learning Models

NARCIS (Netherlands)

N. Asadi; J.J.P. Lin (Jimmy); A.P. de Vries (Arjen)

2014-01-01

htmlabstractTree-based models have proven to be an effective solution for web ranking as well as other machine learning problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, specifically using gradient-boosted regression

Discounted cost model for condition-based maintenance optimization

International Nuclear Information System (INIS)

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

2010-01-01

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

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

Directory of Open Access Journals (Sweden)

Fei Wang

2017-07-01

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

Optimization models and techniques for implementation and pricing of electricity markets

International Nuclear Information System (INIS)

Madrigal Martinez, M.

2001-01-01

The operation and planning of vertically integrated electric power systems can be optimized using models that simulate solutions to problems. As the electric power industry is going through a period of restructuring, there is a need for new optimization tools. This thesis describes the importance of optimization tools and presents techniques for implementing them. It also presents methods for pricing primary electricity markets. Three modeling groups are studied. The first considers a simplified continuous and discrete model for power pool auctions. The second considers the unit commitment problem, and the third makes use of a new type of linear network-constrained clearing system model for daily markets for power and spinning reserve. The newly proposed model considers bids for supply and demand and bilateral contracts. It is a direct current model for the transmission network

Metroplex Optimization Model Expansion and Analysis: The Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM)

Science.gov (United States)

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

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

Directory of Open Access Journals (Sweden)

Mirosław Targosz

2018-01-01

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

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

Directory of Open Access Journals (Sweden)

Cheng Wang

2013-09-01

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

A model of optimal voluntary muscular control.

Science.gov (United States)

FitzHugh, R

1977-07-19

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

Fuzzy Stochastic Optimization Theory, Models and Applications

CERN Document Server

Wang, Shuming

2012-01-01

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

Optimal inventory management and order book modeling

KAUST Repository

Baradel, Nicolas

2018-02-16

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

Desiccant wheel thermal performance modeling for indoor humidity optimal control

International Nuclear Information System (INIS)

Wang, Nan; Zhang, Jiangfeng; Xia, Xiaohua

2013-01-01

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

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

Science.gov (United States)

Yang, Qidong; Zuo, Hongchao; Li, Weidong

2016-01-01

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

Method to determine the optimal constitutive model from spherical indentation tests

Science.gov (United States)

Zhang, Tairui; Wang, Shang; Wang, Weiqiang

2018-03-01

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

A model for optimization of process integration investments under uncertainty

International Nuclear Information System (INIS)

Svensson, Elin; Stroemberg, Ann-Brith; Patriksson, Michael

2011-01-01

The long-term economic outcome of energy-related industrial investment projects is difficult to evaluate because of uncertain energy market conditions. In this article, a general, multistage, stochastic programming model for the optimization of investments in process integration and industrial energy technologies is proposed. The problem is formulated as a mixed-binary linear programming model where uncertainties are modelled using a scenario-based approach. The objective is to maximize the expected net present value of the investments which enables heat savings and decreased energy imports or increased energy exports at an industrial plant. The proposed modelling approach enables a long-term planning of industrial, energy-related investments through the simultaneous optimization of immediate and later decisions. The stochastic programming approach is also suitable for modelling what is possibly complex process integration constraints. The general model formulation presented here is a suitable basis for more specialized case studies dealing with optimization of investments in energy efficiency. -- Highlights: → Stochastic programming approach to long-term planning of process integration investments. → Extensive mathematical model formulation. → Multi-stage investment decisions and scenario-based modelling of uncertain energy prices. → Results illustrate how investments made now affect later investment and operation opportunities. → Approach for evaluation of robustness with respect to variations in probability distribution.

An aircraft noise pollution model for trajectory optimization

Science.gov (United States)

Barkana, A.; Cook, G.

1976-01-01

A mathematical model describing the generation of aircraft noise is developed with the ultimate purpose of reducing noise (noise-optimizing landing trajectories) in terminal areas. While the model is for a specific aircraft (Boeing 737), the methodology would be applicable to a wide variety of aircraft. The model is used to obtain a footprint on the ground inside of which the noise level is at or above 70 dB.

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...

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.

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

Czech Academy of Sciences Publication Activity Database

Pilát, Martin; Neruda, Roman

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

Polymer models with optimal good-solvent behavior

Science.gov (United States)

D'Adamo, Giuseppe; Pelissetto, Andrea

2017-11-01

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

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

Directory of Open Access Journals (Sweden)

Hediyeh Karimi

2013-01-01

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

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

DEFF Research Database (Denmark)

Visser, Andre W.; Fiksen, Ø.

2013-01-01

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

Optimization of single photon detection model based on GM-APD

Science.gov (United States)

Chen, Yu; Yang, Yi; Hao, Peiyu

2017-11-01

One hundred kilometers high precision laser ranging hopes the detector has very strong detection ability for very weak light. At present, Geiger-Mode of Avalanche Photodiode has more use. It has high sensitivity and high photoelectric conversion efficiency. Selecting and designing the detector parameters according to the system index is of great importance to the improvement of photon detection efficiency. Design optimization requires a good model. In this paper, we research the existing Poisson distribution model, and consider the important detector parameters of dark count rate, dead time, quantum efficiency and so on. We improve the optimization of detection model, select the appropriate parameters to achieve optimal photon detection efficiency. The simulation is carried out by using Matlab and compared with the actual test results. The rationality of the model is verified. It has certain reference value in engineering applications.

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

Directory of Open Access Journals (Sweden)

Baoshou Zhang

2017-03-01

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

GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS

International Nuclear Information System (INIS)

Rogers, Adam; Fiege, Jason D.

2011-01-01

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

Optimal dimensioning model of water distribution systems | Gomes ...

African Journals Online (AJOL)

This study is aimed at developing a pipe-sizing model for a water distribution system. The optimal solution minimises the system's total cost, which comprises the hydraulic network capital cost, plus the capitalised cost of pumping energy. The developed model, called Lenhsnet, may also be used for economical design when ...

Modeling the optimal management of spent nuclear fuel

International Nuclear Information System (INIS)

Nachlas, J.A.; Kurstedt, H.A. Jr.; Swindle, D.W. Jr.; Korcz, K.O.

1977-01-01

Recent governmental policy decisions dictate that strategies for managing spent nuclear fuel be developed. Two models are constructed to investigate the optimum residence time and the optimal inventory withdrawal policy for fuel material that presently must be stored. The mutual utility of the models is demonstrated through reference case application

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

Science.gov (United States)

Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

2017-01-01

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

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

OpenAIRE

Aprasoff, Jonathan; Donchin, Opher

2011-01-01

Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedb...

Modeling and optimization of an electric power distribution network ...

African Journals Online (AJOL)

Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...

Reduced order modeling in topology optimization of vibroacoustic problems

DEFF Research Database (Denmark)

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

2017-01-01

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

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

Science.gov (United States)

Tong, Cao; Gong, Haili

2018-03-01

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

Ground Vehicle System Integration (GVSI) and Design Optimization Model

National Research Council Canada - National Science Library

Horton, William

1996-01-01

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

A Fuzzy Optimization Model for High-Speed Railway Timetable Rescheduling

Directory of Open Access Journals (Sweden)

Li Wang

2012-01-01

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

Portfolio optimization by using linear programing models based on genetic algorithm

Science.gov (United States)

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

2018-01-01

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

A trust region approach with multivariate Padé model for optimal circuit design

Science.gov (United States)

Abdel-Malek, Hany L.; Ebid, Shaimaa E. K.; Mohamed, Ahmed S. A.

2017-11-01

Since the optimization process requires a significant number of consecutive function evaluations, it is recommended to replace the function by an easily evaluated approximation model during the optimization process. The model suggested in this article is based on a multivariate Padé approximation. This model is constructed using data points of ?, where ? is the number of parameters. The model is updated over a sequence of trust regions. This model avoids the slow convergence of linear models of ? and has features of quadratic models that need interpolation data points of ?. The proposed approach is tested by applying it to several benchmark problems. Yield optimization using such a direct method is applied to some practical circuit examples. Minimax solution leads to a suitable initial point to carry out the yield optimization process. The yield is optimized by the proposed derivative-free method for active and passive filter examples.

Modeling, estimation and optimal filtration in signal processing

CERN Document Server

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

Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.

Science.gov (United States)

Patri, Jean-François; Diard, Julien; Perrier, Pascal

2015-12-01

The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

Science.gov (United States)

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

2017-07-01

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

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

Directory of Open Access Journals (Sweden)

Mark N Read

2016-09-01

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

CREATION OF OPTIMIZATION MODEL OF STEAM BOILER RECUPERATIVE AIR HEATER

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N. B. Carnickiy

2006-01-01

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

Image-optimized Coronal Magnetic Field Models

Energy Technology Data Exchange (ETDEWEB)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M., E-mail: shaela.i.jones-mecholsky@nasa.gov, E-mail: shaela.i.jonesmecholsky@nasa.gov [NASA Goddard Space Flight Center, Code 670, Greenbelt, MD 20771 (United States)

2017-08-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work, we presented early tests of the method, which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper, we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane and the effect on the outcome of the optimization of errors in the localization of constraints. We find that substantial improvement in the model field can be achieved with these types of constraints, even when magnetic features in the images are located outside of the image plane.

Image-Optimized Coronal Magnetic Field Models

Science.gov (United States)

Jones, Shaela I.; Uritsky, Vadim; Davila, Joseph M.

2017-01-01

We have reported previously on a new method we are developing for using image-based information to improve global coronal magnetic field models. In that work we presented early tests of the method which proved its capability to improve global models based on flawed synoptic magnetograms, given excellent constraints on the field in the model volume. In this follow-up paper we present the results of similar tests given field constraints of a nature that could realistically be obtained from quality white-light coronagraph images of the lower corona. We pay particular attention to difficulties associated with the line-of-sight projection of features outside of the assumed coronagraph image plane, and the effect on the outcome of the optimization of errors in localization of constraints. We find that substantial improvement in the model field can be achieved with this type of constraints, even when magnetic features in the images are located outside of the image plane.

Optimization of a Two-Fluid Hydrodynamic Model of Churn-Turbulent Flow

Energy Technology Data Exchange (ETDEWEB)

Donna Post Guillen

2009-07-01

A hydrodynamic model of two-phase, churn-turbulent flows is being developed using the computational multiphase fluid dynamics (CMFD) code, NPHASE-CMFD. The numerical solutions obtained by this model are compared with experimental data obtained at the TOPFLOW facility of the Institute of Safety Research at the Forschungszentrum Dresden-Rossendorf. The TOPFLOW data is a high quality experimental database of upward, co-current air-water flows in a vertical pipe suitable for validation of computational fluid dynamics (CFD) codes. A five-field CMFD model was developed for the continuous liquid phase and four bubble size groups using mechanistic closure models for the ensemble-averaged Navier-Stokes equations. Mechanistic models for the drag and non-drag interfacial forces are implemented to include the governing physics to describe the hydrodynamic forces controlling the gas distribution. The closure models provide the functional form of the interfacial forces, with user defined coefficients to adjust the force magnitude. An optimization strategy was devised for these coefficients using commercial design optimization software. This paper demonstrates an approach to optimizing CMFD model parameters using a design optimization approach. Computed radial void fraction profiles predicted by the NPHASE-CMFD code are compared to experimental data for four bubble size groups.

Derivation of potential model for LiAlO2 by simple and effective optimization of model parameters

International Nuclear Information System (INIS)

Tsuchihira, H.; Oda, T.; Tanaka, S.

2009-01-01

Interatomic potentials of LiAlO 2 were constructed by a simple and effective method. In this method, the model function consists of multiple inverse polynomial functions with an exponential truncation function, and parameters in the potential model can be optimized as a solution of simultaneous linear equations. Potential energies obtained by ab initio calculation are used as fitting targets for model parameter optimization. Lattice constants, elastic properties, defect-formation energy, thermal expansions and the melting point were calculated under the constructed potential models. The results showed good agreement with experimental values and ab initio calculation results, which underscores the validity of the presented method.

The PDB_REDO server for macromolecular structure model optimization

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Robbie P. Joosten

2014-07-01

Full Text Available The refinement and validation of a crystallographic structure model is the last step before the coordinates and the associated data are submitted to the Protein Data Bank (PDB. The success of the refinement procedure is typically assessed by validating the models against geometrical criteria and the diffraction data, and is an important step in ensuring the quality of the PDB public archive [Read et al. (2011, Structure, 19, 1395–1412]. The PDB_REDO procedure aims for `constructive validation', aspiring to consistent and optimal refinement parameterization and pro-active model rebuilding, not only correcting errors but striving for optimal interpretation of the electron density. A web server for PDB_REDO has been implemented, allowing thorough, consistent and fully automated optimization of the refinement procedure in REFMAC and partial model rebuilding. The goal of the web server is to help practicing crystallographers to improve their model prior to submission to the PDB. For this, additional steps were implemented in the PDB_REDO pipeline, both in the refinement procedure, e.g. testing of resolution limits and k-fold cross-validation for small test sets, and as new validation criteria, e.g. the density-fit metrics implemented in EDSTATS and ligand validation as implemented in YASARA. Innovative ways to present the refinement and validation results to the user are also described, which together with auto-generated Coot scripts can guide users to subsequent model inspection and improvement. It is demonstrated that using the server can lead to substantial improvement of structure models before they are submitted to the PDB.

Linear versus quadratic portfolio optimization model with transaction cost

Science.gov (United States)

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.

Optimal model-free prediction from multivariate time series

Science.gov (United States)

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

2015-05-01

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

Modeling groundwater vulnerability to pollution using Optimized DRASTIC model

International Nuclear Information System (INIS)

Mogaji, Kehinde Anthony; Lim, Hwee San; Abdullar, Khiruddin

2014-01-01

The prediction accuracy of the conventional DRASTIC model (CDM) algorithm for groundwater vulnerability assessment is severely limited by the inherent subjectivity and uncertainty in the integration of data obtained from various sources. This study attempts to overcome these problems by exploring the potential of the analytic hierarchy process (AHP) technique as a decision support model to optimize the CDM algorithm. The AHP technique was utilized to compute the normalized weights for the seven parameters of the CDM to generate an optimized DRASTIC model (ODM) algorithm. The DRASTIC parameters integrated with the ODM algorithm predicted which among the study areas is more likely to become contaminated as a result of activities at or near the land surface potential. Five vulnerability zones, namely: no vulnerable(NV), very low vulnerable (VLV), low vulnerable (LV), moderate vulnerable (MV) and high vulnerable (HV) were identified based on the vulnerability index values estimated with the ODM algorithm. Results show that more than 50% of the area belongs to both moderate and high vulnerable zones on the account of the spatial analysis of the produced ODM-based groundwater vulnerability prediction map (GVPM).The prediction accuracy of the ODM-based – GVPM with the groundwater pH and manganese (Mn) concentrations established correlation factors (CRs) result of 90 % and 86 % compared to the CRs result of 62 % and 50 % obtained for the validation accuracy of the CDM – based GVPM. The comparative results, indicated that the ODM-based produced GVPM is more reliable than the CDM – based produced GVPM in the study area. The study established the efficacy of AHP as a spatial decision support technique in enhancing environmental decision making with particular reference to future groundwater vulnerability assessment

MENENTUKAN PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL CONDITIONAL MEAN VARIANCE

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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 Regional Geodynamic Models for Mantle Dynamics

Science.gov (United States)

Knepley, M.; Isaac, T.; Jadamec, M. A.

2016-12-01

The SubductionGenerator program is used to construct high resolution, 3D regional thermal structures for mantle convection simulations using a variety of data sources, including sea floor ages and geographically referenced 3D slab locations based on seismic observations. The initial bulk temperature field is constructed using a half-space cooling model or plate cooling model, and related smoothing functions based on a diffusion length-scale analysis. In this work, we seek to improve the 3D thermal model and test different model geometries and dynamically driven flow fields using constraints from observed seismic velocities and plate motions. Through a formal adjoint analysis, we construct the primal-dual version of the multi-objective PDE-constrained optimization problem for the plate motions and seismic misfit. We have efficient, scalable preconditioners for both the forward and adjoint problems based upon a block preconditioning strategy, and a simple gradient update is used to improve the control residual. The full optimal control problem is formulated on a nested hierarchy of grids, allowing a nonlinear multigrid method to accelerate the solution.

Study on network traffic forecast model of SVR optimized by GAFSA

International Nuclear Information System (INIS)

Liu, Yuan; Wang, RuiXue

2016-01-01

There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.

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

CERN Document Server

Walkowiak, Krzysztof

2016-01-01

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

Applied probability models with optimization applications

CERN Document Server

Ross, Sheldon M

1992-01-01

Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.

Deterministic operations research models and methods in linear optimization

CERN Document Server

Rader, David J

2013-01-01

Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear

Optimal Strategy and Business Models

DEFF Research Database (Denmark)

Johnson, Peter; Foss, Nicolai Juul

2016-01-01

This study picks up on earlier suggestions that control theory may further the study of strategy. Strategy can be formally interpreted as an idealized path optimizing heterogeneous resource deployment to produce maximum financial gain. Using standard matrix methods to describe the firm Hamiltonia...... variable of firm path, suggesting in turn that the firm's business model is the codification of the application of investment resources used to control the strategic path of value realization....

A model for HIV/AIDS pandemic with optimal control

Science.gov (United States)

Sule, Amiru; Abdullah, Farah Aini

2015-05-01

Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is pandemic. It has affected nearly 60 million people since the detection of the disease in 1981 to date. In this paper basic deterministic HIV/AIDS model with mass action incidence function are developed. Stability analysis is carried out. And the disease free equilibrium of the basic model was found to be locally asymptotically stable whenever the threshold parameter (RO) value is less than one, and unstable otherwise. The model is extended by introducing two optimal control strategies namely, CD4 counts and treatment for the infective using optimal control theory. Numerical simulation was carried out in order to illustrate the analytic results.

Multi-infill strategy for kriging models used in variable fidelity optimization

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Chao SONG

2018-03-01

Full Text Available In this paper, a computationally efficient optimization method for aerodynamic design has been developed. The low-fidelity model and the multi-infill strategy are utilized in this approach. Low-fidelity data is employed to provide a good global trend for model prediction, and multiple sample points chosen by different infill criteria in each updating cycle are used to enhance the exploitation and exploration ability of the optimization approach. Take the advantages of low-fidelity model and the multi-infill strategy, and no initial sample for the high-fidelity model is needed. This approach is applied to an airfoil design case and a high-dimensional wing design case. It saves a large number of high-fidelity function evaluations for initial model construction. What’s more, faster reduction of an aerodynamic function is achieved, when compared to ordinary kriging using the multi-infill strategy and variable-fidelity model using single infill criterion. The results indicate that the developed approach has a promising application to efficient aerodynamic design when high-fidelity analyses are involved. Keywords: Aerodynamics, Infill criteria, Kriging models, Multi-infill, Optimization

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

International Nuclear Information System (INIS)

Zhou, Z; Folkert, M; Wang, J

2016-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

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

2016-06-15

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

Chaotic System Identification Based on a Fuzzy Wiener Model with Particle Swarm Optimization

International Nuclear Information System (INIS)

Yong, Li; Ying-Gan, Tang

2010-01-01

A fuzzy Wiener model is proposed to identify chaotic systems. The proposed fuzzy Wiener model consists of two parts, one is a linear dynamic subsystem and the other is a static nonlinear part, which is represented by the Takagi–Sugeno fuzzy model. Identification of chaotic systems is converted to find optimal parameters of the fuzzy Wiener model by minimizing the state error between the original chaotic system and the fuzzy Wiener model. Particle swarm optimization algorithm, a global optimizer, is used to search the optimal parameter of the fuzzy Wiener model. The proposed method can identify the parameters of the linear part and nonlinear part simultaneously. Numerical simulations for Henón and Lozi chaotic system identification show the effectiveness of the proposed method

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

Science.gov (United States)

Huang, P. S.; Chiu, Y.

2015-12-01

In 1970's, the agriculture and aquaculture were rapidly developed at Pingtung coastal area in southern Taiwan. The groundwater aquifers were over-pumped and caused the seawater intrusion. In order to remedy the contaminated groundwater and find the best strategies of groundwater usage, a management model to search the optimal groundwater operational strategies is developed in this study. The objective function is to minimize the total amount of injection water and a set of constraints are applied to ensure the groundwater levels and concentrations are satisfied. A three-dimension density-dependent flow and transport simulation model, called SEAWAT developed by U.S. Geological Survey, is selected to simulate the phenomenon of seawater intrusion. The simulation model is well calibrated by the field measurements and replaced by the surrogate model of trained artificial neural networks (ANNs) to reduce the computational time. The ANNs are embedded in the management model to link the simulation and optimization models, and the global optimizer of differential evolution (DE) is applied for solving the management model. The optimal results show that the fully trained ANNs could substitute the original simulation model and reduce much computational time. Under appropriate setting of objective function and constraints, DE can find the optimal injection rates at predefined barriers. The concentrations at the target locations could decrease more than 50 percent within the planning horizon of 20 years. Keywords : Seawater intrusion, groundwater management, numerical model, artificial neural networks, differential evolution

Optimization Research of Generation Investment Based on Linear Programming Model

Science.gov (United States)

Wu, Juan; Ge, Xueqian

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

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

Science.gov (United States)

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

2018-03-01

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

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

Directory of Open Access Journals (Sweden)

Kin Wei Ng

2012-01-01

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

A Convex Optimization Model and Algorithm for Retinex

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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.

Topology optimization of a pseudo 3D thermofluid heat sink model

DEFF Research Database (Denmark)

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

2018-01-01

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

Modeling and Optimal Control of a Class of Warfare Hybrid Dynamic Systems Based on Lanchester (n,1 Attrition Model

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Xiangyong Chen

2014-01-01

hybrid dynamic systems is established based on Lanchester equation in a (n,1 battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation, and discrete event systems (described by variable tactics. Furthermore, an expository discussion is presented on an optimal variable tactics control problem for warfare hybrid dynamic system. The optimal control strategies are designed based on dynamic programming and differential game theory. As an example of the consequences of this optimal control problem, we take the (2, 1 case and solve the optimal strategies in a (2, 1 case. Simulation results show the feasibility of warfare hybrid system model and the effectiveness of the optimal control strategies designed.

Identifying optimal models to represent biochemical systems.

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Mochamad Apri

Full Text Available Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an "optimal" reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model.

Modeling and Optimization : Theory and Applications Conference

CERN Document Server

Terlaky, Tamás

2017-01-01

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

Modeling and Optimization : Theory and Applications Conference

CERN Document Server

Terlaky, Tamás

2015-01-01

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

On meeting capital requirements with a chance-constrained optimization model.

Science.gov (United States)

Atta Mills, Ebenezer Fiifi Emire; Yu, Bo; Gu, Lanlan

2016-01-01

This paper deals with a capital to risk asset ratio chance-constrained optimization model in the presence of loans, treasury bill, fixed assets and non-interest earning assets. To model the dynamics of loans, we introduce a modified CreditMetrics approach. This leads to development of a deterministic convex counterpart of capital to risk asset ratio chance constraint. We pursue the scope of analyzing our model under the worst-case scenario i.e. loan default. The theoretical model is analyzed by applying numerical procedures, in order to administer valuable insights from a financial outlook. Our results suggest that, our capital to risk asset ratio chance-constrained optimization model guarantees banks of meeting capital requirements of Basel III with a likelihood of 95 % irrespective of changes in future market value of assets.

Innovative supply chain optimization models with multiple uncertainty factors

DEFF Research Database (Denmark)

Choi, Tsan Ming; Govindan, Kannan; Li, Xiang

2017-01-01

Uncertainty is an inherent factor that affects all dimensions of supply chain activities. In today’s business environment, initiatives to deal with one specific type of uncertainty might not be effective since other types of uncertainty factors and disruptions may be present. These factors relate...... to supply chain competition and coordination. Thus, to achieve a more efficient and effective supply chain requires the deployment of innovative optimization models and novel methods. This preface provides a concise review of critical research issues regarding innovative supply chain optimization models...

Low Complexity Models to improve Incomplete Sensitivities for Shape Optimization

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Stanciu, Mugurel; Mohammadi, Bijan; Moreau, Stéphane

2003-01-01

The present global platform for simulation and design of multi-model configurations treat shape optimization problems in aerodynamics. Flow solvers are coupled with optimization algorithms based on CAD-free and CAD-connected frameworks. Newton methods together with incomplete expressions of gradients are used. Such incomplete sensitivities are improved using reduced models based on physical assumptions. The validity and the application of this approach in real-life problems are presented. The numerical examples concern shape optimization for an airfoil, a business jet and a car engine cooling axial fan.

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

International Nuclear Information System (INIS)

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

2015-01-01

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

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.

Real-time process optimization based on grey-box neural models

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F. A. Cubillos

2007-09-01

Full Text Available This paper investigates the feasibility of using grey-box neural models (GNM in Real Time Optimization (RTO. These models are based on a suitable combination of fundamental conservation laws and neural networks, being used in at least two different ways: to complement available phenomenological knowledge with empirical information, or to reduce dimensionality of complex rigorous physical models. We have observed that the benefits of using these simple adaptable models are counteracted by some difficulties associated with the solution of the optimization problem. Nonlinear Programming (NLP algorithms failed in finding the global optimum due to the fact that neural networks can introduce multimodal objective functions. One alternative considered to solve this problem was the use of some kind of evolutionary algorithms, like Genetic Algorithms (GA. Although these algorithms produced better results in terms of finding the appropriate region, they took long periods of time to reach the global optimum. It was found that a combination of genetic and nonlinear programming algorithms can be use to fast obtain the optimum solution. The proposed approach was applied to the Williams-Otto reactor, considering three different GNM models of increasing complexity. Results demonstrated that the use of GNM models and mixed GA/NLP optimization algorithms is a promissory approach for solving dynamic RTO problems.

Optimizing incomplete sample designs for item response model parameters

NARCIS (Netherlands)

van der Linden, Willem J.

Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with

Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments.

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Kaplan, David; Lee, Chansoon

2018-01-01

This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model's posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

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

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Yang Jiao

2017-01-01

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

Heat transfer optimization of SCO2 porous flow based on Brinkman model

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Lin David T.W.

2016-01-01

Full Text Available The purpose of this study is to obtain the optimal operating condition in order to find the maximum supercritical CO2 heat extraction in the enhanced geothermal system (EGS. In this study, the heat transfer model conjugated with the Brinkman model is used to evaluate the thermal behavior in the reservoir of the EGS. This numerical model is validated by experiment. Optimization is processed based on the Nelder-Mead approach. The optimal operating conditions are proposed with different pressure, porosity. This study will build the optimal platform of heat source of geothermal power plant.

A Multiobjective Optimization Model in Automotive Supply Chain Networks

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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.

Building Energy Modeling and Control Methods for Optimization and Renewables Integration

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Burger, Eric M.

dynamics within a building by learning from sensor data. Control techniques encompass the application of optimal control theory, model predictive control, and convex distributed optimization to TCLs. First, we present the alternative control trajectory (ACT) representation, a novel method for the approximate optimization of non-convex discrete systems. This approach enables the optimal control of a population of non-convex agents using distributed convex optimization techniques. Second, we present a distributed convex optimization algorithm for the control of a TCL population. Experimental results demonstrate the application of this algorithm to the problem of renewable energy generation following. This dissertation contributes to the development of intelligent energy management systems for buildings by presenting a suite of novel and adaptable modeling and control techniques. Applications focus on optimizing the performance of building operations and on facilitating the integration of renewable energy resources.

Radio-over-fiber linearization with optimized genetic algorithm CPWL model.

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Mateo, Carlos; Carro, Pedro L; García-Dúcar, Paloma; De Mingo, Jesús; Salinas, Íñigo

2017-02-20

This article proposes an optimized version of a canonical piece-wise-linear (CPWL) digital predistorter in order to enhance the linearity of a radio-over-fiber (RoF) LTE mobile fronthaul. In this work, we propose a threshold allocation optimization process carried out by a genetic algorithm (GA) in order to optimize the CPWL model (GA-CPWL). Firstly, experiments show how the CPWL model outperforms the classical memory polynomial DPD in an intensity modulation/direct detection (IM/DD) RoF link. Then, the GA-CPWL predistorter is compared with the CPWL model in several scenarios, in order to verify that the proposed DPD offers better performance in different optical transmission conditions. Experimental results reveal that with a proper threshold allocation, the GA-CPWL predistorter offers very promising outcomes.

Hedging Rules for Water Supply Reservoir Based on the Model of Simulation and Optimization

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Yi Ji

2016-06-01

Full Text Available This study proposes a hedging rule model which is composed of a two-period reservior operation model considering the damage depth and hedging rule parameter optimization model. The former solves hedging rules based on a given poriod’s water supply weighting factor and carryover storage target, while the latter optimization model is used to optimize the weighting factor and carryover storage target based on the hedging rules. The coupling model gives the optimal poriod’s water supply weighting factor and carryover storage target to guide release. The conclusions achieved from this study as follows: (1 the water supply weighting factor and carryover storage target have a direct impact on the three elements of the hedging rule; (2 parameters can guide reservoirs to supply water reasonably after optimization of the simulation and optimization model; and (3 in order to verify the utility of the hedging rule, the Heiquan reservoir is used as a case study and particle swarm optimization algorithm with a simulation model is adopted for optimizing the parameter. The results show that the proposed hedging rule can improve the operation performances of the water supply reservoir.

Model checking optimal finite-horizon control for probabilistic gene regulatory networks.

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Wei, Ou; Guo, Zonghao; Niu, Yun; Liao, Wenyuan

2017-12-14

Probabilistic Boolean networks (PBNs) have been proposed for analyzing external control in gene regulatory networks with incorporation of uncertainty. A context-sensitive PBN with perturbation (CS-PBNp), extending a PBN with context-sensitivity to reflect the inherent biological stability and random perturbations to express the impact of external stimuli, is considered to be more suitable for modeling small biological systems intervened by conditions from the outside. In this paper, we apply probabilistic model checking, a formal verification technique, to optimal control for a CS-PBNp that minimizes the expected cost over a finite control horizon. We first describe a procedure of modeling a CS-PBNp using the language provided by a widely used probabilistic model checker PRISM. We then analyze the reward-based temporal properties and the computation in probabilistic model checking; based on the analysis, we provide a method to formulate the optimal control problem as minimum reachability reward properties. Furthermore, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. We conduct experiments on two examples, an apoptosis network and a WNT5A network. Preliminary experiment results show the feasibility and effectiveness of our approach. The approach based on probabilistic model checking for optimal control avoids explicit computation of large-size state transition relations associated with PBNs. It enables a natural depiction of the dynamics of gene regulatory networks, and provides a canonical form to formulate optimal control problems using temporal properties that can be automated solved by leveraging the analysis power of underlying model checking engines. This work will be helpful for further utilization of the advances in formal verification techniques in system biology.

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

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Janardhanan, S.; Datta, B.

2011-12-01

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

Inverse Optimization: A New Perspective on the Black-Litterman Model

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Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch.

2014-01-01

The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct “BL”-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new “BL”-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views. PMID:25382873

Inverse Optimization: A New Perspective on the Black-Litterman Model.

Science.gov (United States)

Bertsimas, Dimitris; Gupta, Vishal; Paschalidis, Ioannis Ch

2012-12-11

The Black-Litterman (BL) model is a widely used asset allocation model in the financial industry. In this paper, we provide a new perspective. The key insight is to replace the statistical framework in the original approach with ideas from inverse optimization. This insight allows us to significantly expand the scope and applicability of the BL model. We provide a richer formulation that, unlike the original model, is flexible enough to incorporate investor information on volatility and market dynamics. Equally importantly, our approach allows us to move beyond the traditional mean-variance paradigm of the original model and construct "BL"-type estimators for more general notions of risk such as coherent risk measures. Computationally, we introduce and study two new "BL"-type estimators and their corresponding portfolios: a Mean Variance Inverse Optimization (MV-IO) portfolio and a Robust Mean Variance Inverse Optimization (RMV-IO) portfolio. These two approaches are motivated by ideas from arbitrage pricing theory and volatility uncertainty. Using numerical simulation and historical backtesting, we show that both methods often demonstrate a better risk-reward tradeoff than their BL counterparts and are more robust to incorrect investor views.

Sustainable logistics and transportation optimization models and algorithms

CERN Document Server

Gakis, Konstantinos; Pardalos, Panos

2017-01-01

Focused on the logistics and transportation operations within a supply chain, this book brings together the latest models, algorithms, and optimization possibilities. Logistics and transportation problems are examined within a sustainability perspective to offer a comprehensive assessment of environmental, social, ethical, and economic performance measures. Featured models, techniques, and algorithms may be used to construct policies on alternative transportation modes and technologies, green logistics, and incentives by the incorporation of environmental, economic, and social measures. Researchers, professionals, and graduate students in urban regional planning, logistics, transport systems, optimization, supply chain management, business administration, information science, mathematics, and industrial and systems engineering will find the real life and interdisciplinary issues presented in this book informative and useful.

Geometry optimization method versus predictive ability in QSPR modeling for ionic liquids

Science.gov (United States)

Rybinska, Anna; Sosnowska, Anita; Barycki, Maciej; Puzyn, Tomasz

2016-02-01

Computational techniques, such as Quantitative Structure-Property Relationship (QSPR) modeling, are very useful in predicting physicochemical properties of various chemicals. Building QSPR models requires calculating molecular descriptors and the proper choice of the geometry optimization method, which will be dedicated to specific structure of tested compounds. Herein, we examine the influence of the ionic liquids' (ILs) geometry optimization methods on the predictive ability of QSPR models by comparing three models. The models were developed based on the same experimental data on density collected for 66 ionic liquids, but with employing molecular descriptors calculated from molecular geometries optimized at three different levels of the theory, namely: (1) semi-empirical (PM7), (2) ab initio (HF/6-311+G*) and (3) density functional theory (B3LYP/6-311+G*). The model in which the descriptors were calculated by using ab initio HF/6-311+G* method indicated the best predictivity capabilities ({{Q}}_{{EXT}}2 = 0.87). However, PM7-based model has comparable values of quality parameters ({{Q}}_{{EXT}}2 = 0.84). Obtained results indicate that semi-empirical methods (faster and less expensive regarding CPU time) can be successfully employed to geometry optimization in QSPR studies for ionic liquids.

The analysis of optimal singular controls for SEIR model of tuberculosis

Science.gov (United States)

Marpaung, Faridawaty; Rangkuti, Yulita M.; Sinaga, Marlina S.

2014-12-01

The optimally of singular control for SEIR model of Tuberculosis is analyzed. There are controls that correspond to time of the vaccination and treatment schedule. The optimally of singular control is obtained by differentiate a switching function of the model. The result shows that vaccination and treatment control are singular.

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

Science.gov (United States)

Mehanna Ismail, Mohammed Ali

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

Efficient Output Solution for Nonlinear Stochastic Optimal Control Problem with Model-Reality Differences

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Sie Long Kek

2015-01-01

Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.

Power Grid Construction Project Portfolio Optimization Based on Bi-level programming model

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Zhao, Erdong; Li, Shangqi

2017-08-01

As the main body of power grid operation, county-level power supply enterprises undertake an important emission to guarantee the security of power grid operation and safeguard social power using order. The optimization of grid construction projects has been a key issue of power supply capacity and service level of grid enterprises. According to the actual situation of power grid construction project optimization of county-level power enterprises, on the basis of qualitative analysis of the projects, this paper builds a Bi-level programming model based on quantitative analysis. The upper layer of the model is the target restriction of the optimal portfolio; the lower layer of the model is enterprises’ financial restrictions on the size of the enterprise project portfolio. Finally, using a real example to illustrate operation proceeding and the optimization result of the model. Through qualitative analysis and quantitative analysis, the bi-level programming model improves the accuracy and normative standardization of power grid enterprises projects.

A review on condition-based maintenance optimization models for stochastically deteriorating system

International Nuclear Information System (INIS)

Alaswad, Suzan; Xiang, Yisha

2017-01-01

Condition-based maintenance (CBM) is a maintenance strategy that collects and assesses real-time information, and recommends maintenance decisions based on the current condition of the system. In recent decades, research on CBM has been rapidly growing due to the rapid development of computer-based monitoring technologies. Research studies have proven that CBM, if planned properly, can be effective in improving equipment reliability at reduced costs. This paper presents a review of CBM literature with emphasis on mathematical modeling and optimization approaches. We focus this review on important aspects of the CBM, such as optimization criteria, inspection frequency, maintenance degree, solution methodology, etc. Since the modeling choice for the stochastic deterioration process greatly influences CBM strategy decisions, this review classifies the literature on CBM models based on the underlying deterioration processes, namely discrete- and continuous-state deterioration, and proportional hazard model. CBM models for multi-unit systems are also reviewed in this paper. This paper provides useful references for CBM management professionals and researchers working on CBM modeling and optimization. - Highlights: • A review on Condition-based maintenance (CBM) optimization models is presented. • The CBM models are classified based on the underlying deterioration processes. • Existing CBM models for both single- and multi-unit systems are reviewed. • Future essential research directions on CBM are identified.

Modeling and optimization of potable water network

Energy Technology Data Exchange (ETDEWEB)

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

2000-07-01

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

Optimization of Operations Resources via Discrete Event Simulation Modeling

Science.gov (United States)

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

1996-01-01

The resource levels required for operation and support of reusable launch vehicles are typically defined through discrete event simulation modeling. Minimizing these resources constitutes an optimization problem involving discrete variables and simulation. Conventional approaches to solve such optimization problems involving integer valued decision variables are the pattern search and statistical methods. However, in a simulation environment that is characterized by search spaces of unknown topology and stochastic measures, these optimization approaches often prove inadequate. In this paper, we have explored the applicability of genetic algorithms to the simulation domain. Genetic algorithms provide a robust search strategy that does not require continuity and differentiability of the problem domain. The genetic algorithm successfully minimized the operation and support activities for a space vehicle, through a discrete event simulation model. The practical issues associated with simulation optimization, such as stochastic variables and constraints, were also taken into consideration.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

Science.gov (United States)

Said, Nadia; Engelhart, Michael; Kirches, Christian; Körkel, Stefan; Holt, Daniel V

2016-01-01

Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

An improved model for the oPtImal Measurement Probes Allocation tool

International Nuclear Information System (INIS)

Sterle, C.; Neto, A.C.; De Tommasi, G.

2015-01-01

Highlights: • The problem of optimally allocating the probes of a diagnostic system is tackled. • The problem is decomposed in two consecutive optimization problems. • Two original ILP models are proposed and sequentially solved to optimality. • The proposed ILP models improve and extend the previous work present in literature. • Real size instances have been optimally solved with very low computation time. - Abstract: The oPtImal Measurement Probes Allocation (PIMPA) tool has been recently proposed in [1] to maximize the reliability of a tokamak diagnostic system against the failure of one or more of the processing nodes. PIMPA is based on the solution of integer linear programming (ILP) problems, and it minimizes the effect of the failure of a data acquisition component. The first formulation of the PIMPA model did not support the concept of individual slots. This work presents an improved ILP model that addresses the above mentioned problem, by taking into account all the individual probes.

An improved model for the oPtImal Measurement Probes Allocation tool

Energy Technology Data Exchange (ETDEWEB)

Sterle, C., E-mail: claudio.sterle@unina.it [Consorzio CREATE/Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy); Neto, A.C. [Fusion for Energy, 08019 Barcelona (Spain); De Tommasi, G. [Consorzio CREATE/Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy)

2015-10-15

Highlights: • The problem of optimally allocating the probes of a diagnostic system is tackled. • The problem is decomposed in two consecutive optimization problems. • Two original ILP models are proposed and sequentially solved to optimality. • The proposed ILP models improve and extend the previous work present in literature. • Real size instances have been optimally solved with very low computation time. - Abstract: The oPtImal Measurement Probes Allocation (PIMPA) tool has been recently proposed in [1] to maximize the reliability of a tokamak diagnostic system against the failure of one or more of the processing nodes. PIMPA is based on the solution of integer linear programming (ILP) problems, and it minimizes the effect of the failure of a data acquisition component. The first formulation of the PIMPA model did not support the concept of individual slots. This work presents an improved ILP model that addresses the above mentioned problem, by taking into account all the individual probes.

Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.

Directory of Open Access Journals (Sweden)

Nadia Said

Full Text Available Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.

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

International Nuclear Information System (INIS)

Chen, Le; MacDonald, Erin

2014-01-01

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

Energy-saving management modelling and optimization for lead-acid battery formation process

Science.gov (United States)

Wang, T.; Chen, Z.; Xu, J. Y.; Wang, F. Y.; Liu, H. M.

2017-11-01

In this context, a typical lead-acid battery producing process is introduced. Based on the formation process, an efficiency management method is proposed. An optimization model with the objective to minimize the formation electricity cost in a single period is established. This optimization model considers several related constraints, together with two influencing factors including the transformation efficiency of IGBT charge-and-discharge machine and the time-of-use price. An example simulation is shown using PSO algorithm to solve this mathematic model, and the proposed optimization strategy is proved to be effective and learnable for energy-saving and efficiency optimization in battery producing industries.

Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

Science.gov (United States)

Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

2012-12-01

Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis

Optimizing refiner operation with statistical modelling

Energy Technology Data Exchange (ETDEWEB)

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

1997-02-01

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

Robust Optimization Model for Production Planning Problem under Uncertainty

Directory of Open Access Journals (Sweden)

Pembe GÜÇLÜ

2017-01-01

Full Text Available Conditions of businesses change very quickly. To take into account the uncertainty engendered by changes has become almost a rule while planning. Robust optimization techniques that are methods of handling uncertainty ensure to produce less sensitive results to changing conditions. Production planning, is to decide from which product, when and how much will be produced, with a most basic definition. Modeling and solution of the Production planning problems changes depending on structure of the production processes, parameters and variables. In this paper, it is aimed to generate and apply scenario based robust optimization model for capacitated two-stage multi-product production planning problem under parameter and demand uncertainty. With this purpose, production planning problem of a textile company that operate in İzmir has been modeled and solved, then deterministic scenarios’ and robust method’s results have been compared. Robust method has provided a production plan that has higher cost but, will result close to feasible and optimal for most of the different scenarios in the future.

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

Science.gov (United States)

Lu, M.; Lall, U.

2013-12-01

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

Oyster Creek cycle 10 nodal model parameter optimization study using PSMS

International Nuclear Information System (INIS)

Dougher, J.D.

1987-01-01

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

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.

Solving bi-level optimization problems in engineering design using kriging models

Science.gov (United States)

Xia, Yi; Liu, Xiaojie; Du, Gang

2018-05-01

Stackelberg game-theoretic approaches are applied extensively in engineering design to handle distributed collaboration decisions. Bi-level genetic algorithms (BLGAs) and response surfaces have been used to solve the corresponding bi-level programming models. However, the computational costs for BLGAs often increase rapidly with the complexity of lower-level programs, and optimal solution functions sometimes cannot be approximated by response surfaces. This article proposes a new method, namely the optimal solution function approximation by kriging model (OSFAKM), in which kriging models are used to approximate the optimal solution functions. A detailed example demonstrates that OSFAKM can obtain better solutions than BLGAs and response surface-based methods, and at the same time reduce the workload of computation remarkably. Five benchmark problems and a case study of the optimal design of a thin-walled pressure vessel are also presented to illustrate the feasibility and potential of the proposed method for bi-level optimization in engineering design.

[Temporal and spatial heterogeneity analysis of optimal value of sensitive parameters in ecological process model: The BIOME-BGC model as an example.

Science.gov (United States)

Li, Yi Zhe; Zhang, Ting Long; Liu, Qiu Yu; Li, Ying

2018-01-01

The ecological process models are powerful tools for studying terrestrial ecosystem water and carbon cycle at present. However, there are many parameters for these models, and weather the reasonable values of these parameters were taken, have important impact on the models simulation results. In the past, the sensitivity and the optimization of model parameters were analyzed and discussed in many researches. But the temporal and spatial heterogeneity of the optimal parameters is less concerned. In this paper, the BIOME-BGC model was used as an example. In the evergreen broad-leaved forest, deciduous broad-leaved forest and C3 grassland, the sensitive parameters of the model were selected by constructing the sensitivity judgment index with two experimental sites selected under each vegetation type. The objective function was constructed by using the simulated annealing algorithm combined with the flux data to obtain the monthly optimal values of the sensitive parameters at each site. Then we constructed the temporal heterogeneity judgment index, the spatial heterogeneity judgment index and the temporal and spatial heterogeneity judgment index to quantitatively analyze the temporal and spatial heterogeneity of the optimal values of the model sensitive parameters. The results showed that the sensitivity of BIOME-BGC model parameters was different under different vegetation types, but the selected sensitive parameters were mostly consistent. The optimal values of the sensitive parameters of BIOME-BGC model mostly presented time-space heterogeneity to different degrees which varied with vegetation types. The sensitive parameters related to vegetation physiology and ecology had relatively little temporal and spatial heterogeneity while those related to environment and phenology had generally larger temporal and spatial heterogeneity. In addition, the temporal heterogeneity of the optimal values of the model sensitive parameters showed a significant linear correlation

Combined optimization model for sustainable energization strategy

Science.gov (United States)

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.

An optimization model for transportation of hazardous materials

International Nuclear Information System (INIS)

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

2005-01-01

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

Preprocessing for Optimization of Probabilistic-Logic Models for Sequence Analysis

DEFF Research Database (Denmark)

Christiansen, Henning; Lassen, Ole Torp

2009-01-01

and approximation are needed. The first steps are taken towards a methodology for optimizing such models by approximations using auxiliary models for preprocessing or splitting them into submodels. Evaluation of such approximating models is challenging as authoritative test data may be sparse. On the other hand...

Real-time economic optimization for a fermentation process using Model Predictive Control

DEFF Research Database (Denmark)

Petersen, Lars Norbert; Jørgensen, John Bagterp

2014-01-01

Fermentation is a widely used process in production of many foods, beverages, and pharmaceuticals. The main goal of the control system is to maximize profit of the fermentation process, and thus this is also the main goal of this paper. We present a simple dynamic model for a fermentation process...... and demonstrate its usefulness in economic optimization. The model is formulated as an index-1 differential algebraic equation (DAE), which guarantees conservation of mass and energy in discrete form. The optimization is based on recent advances within Economic Nonlinear Model Predictive Control (E......-NMPC), and also utilizes the index-1 DAE model. The E-NMPC uses the single-shooting method and the adjoint method for computation of the optimization gradients. The process constraints are relaxed to soft-constraints on the outputs. Finally we derive the analytical solution to the economic optimization problem...

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

Science.gov (United States)

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

2017-09-01

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

Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor.

Science.gov (United States)

Kamesh, Reddi; Rani, K Yamuna

2016-09-01

A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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

2011-12-01

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

Optimal evolution models for quantum tomography

International Nuclear Information System (INIS)

Czerwiński, Artur

2016-01-01

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. (paper)

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

Science.gov (United States)

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

2018-03-01

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

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

Science.gov (United States)

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

2018-03-05

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

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.

Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power

DEFF Research Database (Denmark)

Chen, Peiyuan; Siano, Pierluigi; Bak-Jensen, Birgitte

2010-01-01

This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements....... The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the basis of a limited autoregressive integrated moving average (LARIMA) model by introducing a crosscorrelation...... structure to the LARIMA model. The proposed stochastic optimization is carried out on a 69-bus distribution system. Simulation results confirm that, under various combinations of WPG and load demand, the system power losses are considerably reduced with the optimal setting of WT power factor as compared...

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

Science.gov (United States)

Zhang, Xin-Li; Zhang, Ke-Cun

2009-09-01

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

The optimization model of the logging machinery usage in forestry practice

Directory of Open Access Journals (Sweden)

Jitka Janová

2009-01-01

Full Text Available The decision support systems commonly used in industry and economy managerial practice for optimizing the processes are based on algoritmization of the typical decision problems. In Czech forestry business, there is a lack of developed decision support systems, which could be easily used in daily practice. This stems from the fact, that the application of optimization methods is less successful in forestry decision making than in industry or economy due to inherent complexity of the forestry decision problems. There is worldwide ongoing research on optimization models applicable in forestry decision making, but the results are not globally applicable and moreover the cost of possibly arising software tools are indispensable. Especially small and medium forestry companies in Czech Republic can not afford such additional costs, although the results of optimization could positively in­fluen­ce not only the business itself but also the impact of forestry business on the environment. Hence there is a need for user friendly optimization models for forestry decision making in the area of Czech Republic, which could be easily solved in commonly available software, and whose results would be both, realistic and easily applicable in the daily decision making.The aim of this paper is to develop the optimization model for the machinery use planning in Czech logging firm in such a way, that the results can be obtained using MS EXCEL. The goal is to identify the integer number of particular machines which should be outsourced for the next period, when the total cost minimization is required. The linear programming model is designed covering the typical restrictions on available machinery and total volume of trees to be cut and transported. The model offers additional result in the form of optimal employment of particular machines. The solution procedure is described in detail and the results obtained are discussed with respect to its applicability in

Optimization and mathematical modeling in computer architecture

CERN Document Server

Sankaralingam, Karu; Nowatzki, Tony

2013-01-01

In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms t

Multi-Body Ski Jumper Model with Nonlinear Dynamic Inversion Muscle Control for Trajectory Optimization

Directory of Open Access Journals (Sweden)

Patrick Piprek

2018-02-01

Full Text Available This paper presents an approach to model a ski jumper as a multi-body system for an optimal control application. The modeling is based on the constrained Newton-Euler-Equations. Within this paper the complete multi-body modeling methodology as well as the musculoskeletal modeling is considered. For the musculoskeletal modeling and its incorporation in the optimization model, we choose a nonlinear dynamic inversion control approach. This approach uses the muscle models as nonlinear reference models and links them to the ski jumper movement by a control law. This strategy yields a linearized input-output behavior, which makes the optimal control problem easier to solve. The resulting model of the ski jumper can then be used for trajectory optimization whose results are compared to literature jumps. Ultimately, this enables the jumper to get a very detailed feedback of the flight. To achieve the maximal jump length, exact positioning of his body with respect to the air can be displayed.

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

Science.gov (United States)

Juusola, Jessie L; Brandeau, Margaret L

2016-04-01

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

Sparse optimization for inverse problems in atmospheric modelling

Czech Academy of Sciences Publication Activity Database

Adam, Lukáš; Branda, Martin

2016-01-01

Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf

Portfolio optimization for index tracking modelling in Malaysia stock market

Science.gov (United States)

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

2016-06-01

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

Optimization algorithms intended for self-tuning feedwater heater model

International Nuclear Information System (INIS)

Czop, P; Barszcz, T; Bednarz, J

2013-01-01

This work presents a self-tuning feedwater heater model. This work continues the work on first-principle gray-box methodology applied to diagnostics and condition assessment of power plant components. The objective of this work is to review and benchmark the optimization algorithms regarding the time required to achieve the best model fit to operational power plant data. The paper recommends the most effective algorithm to be used in the model adjustment process.

Developments in model-based optimization and control distributed control and industrial applications

CERN Document Server

Grancharova, Alexandra; Pereira, Fernando

2015-01-01

This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...

Clean coal technology optimization model

International Nuclear Information System (INIS)

Laseke, B.A.; Hance, S.B.

1992-01-01

Title IV of the Clean Air Act Amendments (CAAA) of 1990 contains provisions for the mitigation of acid rain precipitation through reductions in the annual emission of the acid rain precursors of sulfur dioxide (SO 2 ) and nitrogen oxide (NO x ). These provisions will affect primarily existing coal-fired power-generating plants by requiring nominal reductions of 5 millon and 10 million tons of SO 2 by the years 1995 and 2000, respectively, and 2 million tons of NO x by the year 2000 relative to the 1980 and 1985-87 reference period. The 1990 CAAA Title IV provisions are extremely complex in that they establish phased regulatory milestones, unit-level emission allowances and caps, a mechanism for inter-utility trading of emission allowances, and a system of emission allowance credits based on selection of control option and timing of its implementation. The net result of Title IV of the 1990 CAAA is that approximately 147 gigawatts (GW) of generating capacity is eligible to retrofit SO 2 controls by the year 2000. A number of options are available to bring affected boilers into compliance with Title IV. Market sharewill be influenced by technology performance and costs. These characteristics can be modeled through a bottom-up technology cost and performance optimization exercise to show their impact on the technology's potential market share. Such a model exists in the form of an integrated data base-model software system. This microcomputer (PC)-based software system consists of a unit (boiler)-level data base (ACIDBASE), a cost and performance engineering model (IAPCS), and a market forecast model (ICEMAN)

Modeling and optimization of planar microcoils

International Nuclear Information System (INIS)

Beyzavi, Ali; Nguyen, Nam-Trung

2008-01-01

Magnetic actuation has emerged as a useful tool for manipulating particles, droplets and biological samples in microfluidics. A planar coil is one of the suitable candidates for magnetic actuation and has the potential to be integrated in digital microfluidic devices. A simple model of microcoils is needed to optimize their use in actuation applications. This paper first develops an analytical model for calculating the magnetic field of a planar microcoil. The model was validated by experimental data from microcoils fabricated on printed circuit boards (PCB). The model was used for calculating the field strength and the force acting on a magnetic object. Finally, the effect of different coil parameters such as the magnitude of the electric current, the gap between the wires and the number of wire segments is discussed. Both analytical and experimental results show that a smaller gap size between wire segments, more wire segments and a higher electric current can increase both the magnitude and the gradient of the magnetic field, and consequently cause a higher actuating force. The planar coil analyzed in the paper is suitable for applications in magnetic droplet-based microfluidics

A PSO–GA optimal model to estimate primary energy demand of China

International Nuclear Information System (INIS)

Yu Shiwei; Wei Yiming; Wang Ke

2012-01-01

To improve estimation efficiency for future projections, the present study has proposed a hybrid algorithm, Particle Swarm Optimization and Genetic Algorithm optimal Energy Demand Estimating (PSO–GA EDE) model, for China. The coefficients of the three forms of the model (linear, exponential, and quadratic) are optimized by PSO–GA using factors, such as GDP, population, economic structure, urbanization rate, and energy consumption structure, that affect demand. Based on 20-year historical data between 1990 and 2009, the simulation results of the proposed model have greater accuracy and reliability than other single optimization methods. Moreover, it can be used with optimal coefficients for the energy demand projections of China. The departure coefficient method is applied to get the weights of the three forms of the model to obtain a combinational prediction. The energy demand of China is going to be 4.79, 4.04, and 4.48 billion tce in 2015, and 6.91, 5.03, and 6.11 billion tce (“standard” tons coal equivalent) in 2020 under three different scenarios. Further, the projection results are compared with other estimating methods. - Highlights: ► A hybrid algorithm PSO–GA optimal energy demands estimating model for China. ► Energy demand of China is estimated by 2020 in three different scenarios. ► The projection results are compared with other estimating methods.

Fuzzy linear model for production optimization of mining systems with multiple entities

Science.gov (United States)

Vujic, Slobodan; Benovic, Tomo; Miljanovic, Igor; Hudej, Marjan; Milutinovic, Aleksandar; Pavlovic, Petar

2011-12-01

Planning and production optimization within multiple mines or several work sites (entities) mining systems by using fuzzy linear programming (LP) was studied. LP is the most commonly used operations research methods in mining engineering. After the introductory review of properties and limitations of applying LP, short reviews of the general settings of deterministic and fuzzy LP models are presented. With the purpose of comparative analysis, the application of both LP models is presented using the example of the Bauxite Basin Niksic with five mines. After the assessment, LP is an efficient mathematical modeling tool in production planning and solving many other single-criteria optimization problems of mining engineering. After the comparison of advantages and deficiencies of both deterministic and fuzzy LP models, the conclusion presents benefits of the fuzzy LP model but is also stating that seeking the optimal plan of production means to accomplish the overall analysis that will encompass the LP model approaches.

3rd International Workshop on Advances in Simulation-Driven Optimization and Modeling

CERN Document Server

Leifsson, Leifur; Yang, Xin-She

2016-01-01

This edited volume is devoted to the now-ubiquitous use of computational models across most disciplines of engineering and science, led by a trio of world-renowned researchers in the field. Focused on recent advances of modeling and optimization techniques aimed at handling computationally-expensive engineering problems involving simulation models, this book will be an invaluable resource for specialists (engineers, researchers, graduate students) working in areas as diverse as electrical engineering, mechanical and structural engineering, civil engineering, industrial engineering, hydrodynamics, aerospace engineering, microwave and antenna engineering, ocean science and climate modeling, and the automotive industry, where design processes are heavily based on CPU-heavy computer simulations. Various techniques, such as knowledge-based optimization, adjoint sensitivity techniques, and fast replacement models (to name just a few) are explored in-depth along with an array of the latest techniques to optimize the...

Model Based Optimal Control, Estimation, and Validation of Lithium-Ion Batteries

Science.gov (United States)

Perez, Hector Eduardo

notion of interval observers to PDE models using a sensitivity-based approach. Practically, this chapter quantifies the sensitivity of battery state estimates to parameter variations, enabling robust battery management schemes. The effectiveness of the proposed sensitivity-based interval observers is verified via a numerical study for the range of uncertain parameters. Chapter 4: This chapter seeks to derive insight on battery charging control using electrochemistry models. Directly using full order complex multi-partial differential equation (PDE) electrochemical battery models is difficult and sometimes impossible to implement. This chapter develops an approach for obtaining optimal charge control schemes, while ensuring safety through constraint satisfaction. An optimal charge control problem is mathematically formulated via a coupled reduced order electrochemical-thermal model which conserves key electrochemical and thermal state information. The Legendre-Gauss-Radau (LGR) pseudo-spectral method with adaptive multi-mesh-interval collocation is employed to solve the resulting nonlinear multi-state optimal control problem. Minimum time charge protocols are analyzed in detail subject to solid and electrolyte phase concentration constraints, as well as temperature constraints. The optimization scheme is examined using different input current bounds, and an insight on battery design for fast charging is provided. Experimental results are provided to compare the tradeoffs between an electrochemical-thermal model based optimal charge protocol and a traditional charge protocol. Chapter 5: Fast and safe charging protocols are crucial for enhancing the practicality of batteries, especially for mobile applications such as smartphones and electric vehicles. This chapter proposes an innovative approach to devising optimally health-conscious fast-safe charge protocols. A multi-objective optimal control problem is mathematically formulated via a coupled electro

Influence of model errors in optimal sensor placement

Science.gov (United States)

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.

Optimal Retail Price Model for Partial Consignment to Multiple Retailers

Directory of Open Access Journals (Sweden)

Po-Yu Chen

2017-01-01

Full Text Available This paper investigates the product pricing decision-making problem under a consignment stock policy in a two-level supply chain composed of one supplier and multiple retailers. The effects of the supplier’s wholesale prices and its partial inventory cost absorption of the retail prices of retailers with different market shares are investigated. In the partial product consignment model this paper proposes, the seller and the retailers each absorb part of the inventory costs. This model also provides general solutions for the complete product consignment and the traditional policy that adopts no product consignment. In other words, both the complete consignment and nonconsignment models are extensions of the proposed model (i.e., special cases. Research results indicated that the optimal retail price must be between 1/2 (50% and 2/3 (66.67% times the upper limit of the gross profit. This study also explored the results and influence of parameter variations on optimal retail price in the model.

Optimization of atmospheric transport models on HPC platforms

Science.gov (United States)

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.

Bioprocess iterative batch-to-batch optimization based on hybrid parametric/nonparametric models.

Science.gov (United States)

Teixeira, Ana P; Clemente, João J; Cunha, António E; Carrondo, Manuel J T; Oliveira, Rui

2006-01-01

This paper presents a novel method for iterative batch-to-batch dynamic optimization of bioprocesses. The relationship between process performance and control inputs is established by means of hybrid grey-box models combining parametric and nonparametric structures. The bioreactor dynamics are defined by material balance equations, whereas the cell population subsystem is represented by an adjustable mixture of nonparametric and parametric models. Thus optimizations are possible without detailed mechanistic knowledge concerning the biological system. A clustering technique is used to supervise the reliability of the nonparametric subsystem during the optimization. Whenever the nonparametric outputs are unreliable, the objective function is penalized. The technique was evaluated with three simulation case studies. The overall results suggest that the convergence to the optimal process performance may be achieved after a small number of batches. The model unreliability risk constraint along with sampling scheduling are crucial to minimize the experimental effort required to attain a given process performance. In general terms, it may be concluded that the proposed method broadens the application of the hybrid parametric/nonparametric modeling technique to "newer" processes with higher potential for optimization.

Optimization models using fuzzy sets and possibility theory

CERN Document Server

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...

OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models

KAUST Repository

Magana-Mora, Arturo

2017-06-14

Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.

OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models

KAUST Repository

Magana-Mora, Arturo; Bajic, Vladimir B.

2017-01-01

Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F score error in the range from 100% to 2.25%, compared to the best performing model. This demonstrates that OmniGA produces robust models with improved performance. OmniGA code and datasets are available at www.cbrc.kaust.edu.sa/omniga/.

Optimal policies of non-cross-resistant chemotherapy on Goldie and Coldman's cancer model.

Science.gov (United States)

Chen, Jeng-Huei; Kuo, Ya-Hui; Luh, Hsing Paul

2013-10-01

Mathematical models can be used to study the chemotherapy on tumor cells. Especially, in 1979, Goldie and Coldman proposed the first mathematical model to relate the drug sensitivity of tumors to their mutation rates. Many scientists have since referred to this pioneering work because of its simplicity and elegance. Its original idea has also been extended and further investigated in massive follow-up studies of cancer modeling and optimal treatment. Goldie and Coldman, together with Guaduskas, later used their model to explain why an alternating non-cross-resistant chemotherapy is optimal with a simulation approach. Subsequently in 1983, Goldie and Coldman proposed an extended stochastic based model and provided a rigorous mathematical proof to their earlier simulation work when the extended model is approximated by its quasi-approximation. However, Goldie and Coldman's analytic study of optimal treatments majorly focused on a process with symmetrical parameter settings, and presented few theoretical results for asymmetrical settings. In this paper, we recast and restate Goldie, Coldman, and Guaduskas' model as a multi-stage optimization problem. Under an asymmetrical assumption, the conditions under which a treatment policy can be optimal are derived. The proposed framework enables us to consider some optimal policies on the model analytically. In addition, Goldie, Coldman and Guaduskas' work with symmetrical settings can be treated as a special case of our framework. Based on the derived conditions, this study provides an alternative proof to Goldie and Coldman's work. In addition to the theoretical derivation, numerical results are included to justify the correctness of our work. Copyright © 2013 Elsevier Inc. All rights reserved.

Advanced Nuclear Fuel Cycle Transitions: Optimization, Modeling Choices, and Disruptions

Science.gov (United States)

Carlsen, Robert W.

Many nuclear fuel cycle simulators have evolved over time to help understan the nuclear industry/ecosystem at a macroscopic level. Cyclus is one of th first fuel cycle simulators to accommodate larger-scale analysis with it liberal open-source licensing and first-class Linux support. Cyclus also ha features that uniquely enable investigating the effects of modeling choices o fuel cycle simulators and scenarios. This work is divided into thre experiments focusing on optimization, effects of modeling choices, and fue cycle uncertainty. Effective optimization techniques are developed for automatically determinin desirable facility deployment schedules with Cyclus. A novel method fo mapping optimization variables to deployment schedules is developed. Thi allows relationships between reactor types and scenario constraints to b represented implicitly in the variable definitions enabling the usage o optimizers lacking constraint support. It also prevents wasting computationa resources evaluating infeasible deployment schedules. Deployed power capacit over time and deployment of non-reactor facilities are also included a optimization variables There are many fuel cycle simulators built with different combinations o modeling choices. Comparing results between them is often difficult. Cyclus flexibility allows comparing effects of many such modeling choices. Reacto refueling cycle synchronization and inter-facility competition among othe effects are compared in four cases each using combinations of fleet of individually modeled reactors with 1-month or 3-month time steps. There are noticeable differences in results for the different cases. The larges differences occur during periods of constrained reactor fuel availability This and similar work can help improve the quality of fuel cycle analysi generally There is significant uncertainty associated deploying new nuclear technologie such as time-frames for technology availability and the cost of buildin advanced reactors

Integrated modeling of ozonation for optimization of drinking water treatment

NARCIS (Netherlands)

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

Optimal Designs for the Generalized Partial Credit Model

OpenAIRE

Bürkner, Paul-Christian; Schwabe, Rainer; Holling, Heinz

2018-01-01

Analyzing ordinal data becomes increasingly important in psychology, especially in the context of item response theory. The generalized partial credit model (GPCM) is probably the most widely used ordinal model and finds application in many large scale educational assessment studies such as PISA. In the present paper, optimal test designs are investigated for estimating persons' abilities with the GPCM for calibrated tests when item parameters are known from previous studies. We will derive t...

Modeling Reservoir-River Networks in Support of Optimizing Seasonal-Scale Reservoir Operations

Science.gov (United States)

Villa, D. L.; Lowry, T. S.; Bier, A.; Barco, J.; Sun, A.

2011-12-01

HydroSCOPE (Hydropower Seasonal Concurrent Optimization of Power and the Environment) is a seasonal time-scale tool for scenario analysis and optimization of reservoir-river networks. Developed in MATLAB, HydroSCOPE is an object-oriented model that simulates basin-scale dynamics with an objective of optimizing reservoir operations to maximize revenue from power generation, reliability in the water supply, environmental performance, and flood control. HydroSCOPE is part of a larger toolset that is being developed through a Department of Energy multi-laboratory project. This project's goal is to provide conventional hydropower decision makers with better information to execute their day-ahead and seasonal operations and planning activities by integrating water balance and operational dynamics across a wide range of spatial and temporal scales. This presentation details the modeling approach and functionality of HydroSCOPE. HydroSCOPE consists of a river-reservoir network model and an optimization routine. The river-reservoir network model simulates the heat and water balance of river-reservoir networks for time-scales up to one year. The optimization routine software, DAKOTA (Design Analysis Kit for Optimization and Terascale Applications - dakota.sandia.gov), is seamlessly linked to the network model and is used to optimize daily volumetric releases from the reservoirs to best meet a set of user-defined constraints, such as maximizing revenue while minimizing environmental violations. The network model uses 1-D approximations for both the reservoirs and river reaches and is able to account for surface and sediment heat exchange as well as ice dynamics for both models. The reservoir model also accounts for inflow, density, and withdrawal zone mixing, and diffusive heat exchange. Routing for the river reaches is accomplished using a modified Muskingum-Cunge approach that automatically calculates the internal timestep and sub-reach lengths to match the conditions of

Optimal Pricing and Advertising Policies for New Product Oligopoly Models

OpenAIRE

Gerald L. Thompson; Jinn-Tsair Teng

1984-01-01

In this paper our previous work on monopoly and oligopoly new product models is extended by the addition of pricing as well as advertising control variables. These models contain Bass's demand growth model, and the Vidale-Wolfe and Ozga advertising models, as well as the production learning curve model and an exponential demand function. The problem of characterizing an optimal pricing and advertising policy over time is an important question in the field of marketing as well as in the areas ...

MODELLING, SIMULATING AND OPTIMIZING BOILERS

DEFF Research Database (Denmark)

Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels

2004-01-01

In the present work a framework for optimizing the design of boilers for dynamic operation has been developed. A cost function to be minimized during the optimization has been formulated and for the present design variables related to the Boiler Volume and the Boiler load Gradient (i.e. ring rate...... on the boiler) have been dened. Furthermore a number of constraints related to: minimum and maximum boiler load gradient, minimum boiler size, Shrinking and Swelling and Steam Space Load have been dened. For dening the constraints related to the required boiler volume a dynamic model for simulating the boiler...... performance has been developed. Outputs from the simulations are shrinking and swelling of water level in the drum during for example a start-up of the boiler, these gures combined with the requirements with respect to allowable water level uctuations in the drum denes the requirements with respect to drum...

Parametric modeling and stagger angle optimization of an axial flow fan

International Nuclear Information System (INIS)

Li, M X; Zhang, C H; Liu, Y; Zheng, S Y

2013-01-01

Axial flow fans are widely used in every field of social production. Improving their efficiency is a sustained and urgent demand of domestic industry. The optimization of stagger angle is an important method to improve fan performance. Parametric modeling and calculation process automation are realized in this paper to improve optimization efficiency. Geometric modeling and mesh division are parameterized based on GAMBIT. Parameter setting and flow field calculation are completed in the batch mode of FLUENT. A control program is developed in Visual C++ to dominate the data exchange of mentioned software. It also extracts calculation results for optimization algorithm module (provided by Matlab) to generate directive optimization control parameters, which as feedback are transferred upwards to modeling module. The center line of the blade airfoil, based on CLARK y profile, is constructed by non-constant circulation and triangle discharge method. Stagger angles of six airfoil sections are optimized, to reduce the influence of inlet shock loss as well as gas leak in blade tip clearance and hub resistance at blade root. Finally an optimal solution is obtained, which meets the total pressure requirement under given conditions and improves total pressure efficiency by about 6%

Optimal Patent Life in a Variety-Expansion Growth Model

OpenAIRE

Lin, Hwan C.

2013-01-01

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

Derivation of Continuum Models from An Agent-based Cancer Model: Optimization and Sensitivity Analysis.

Science.gov (United States)

Voulgarelis, Dimitrios; Velayudhan, Ajoy; Smith, Frank

2017-01-01

Agent-based models provide a formidable tool for exploring complex and emergent behaviour of biological systems as well as accurate results but with the drawback of needing a lot of computational power and time for subsequent analysis. On the other hand, equation-based models can more easily be used for complex analysis in a much shorter timescale. This paper formulates an ordinary differential equations and stochastic differential equations model to capture the behaviour of an existing agent-based model of tumour cell reprogramming and applies it to optimization of possible treatment as well as dosage sensitivity analysis. For certain values of the parameter space a close match between the equation-based and agent-based models is achieved. The need for division of labour between the two approaches is explored. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A Gas Scheduling Optimization Model for Steel Enterprises

Directory of Open Access Journals (Sweden)

Niu Honghai

2017-01-01

Full Text Available Regarding the scheduling problems of steel enterprises, this research designs the gas scheduling optimization model according to the rules and priorities. Considering different features and the process changes of the gas unit in the process of actual production, the calculation model of process state and gas consumption soft measurement together with the rules of scheduling optimization is proposed to provide the dispatchers with real-time gas using status of each process, then help them to timely schedule and reduce the gas volume fluctuations. In the meantime, operation forewarning and alarm functions are provided to avoid the abnormal situation in the scheduling, which has brought about very good application effect in the actual scheduling and ensures the safety of the gas pipe network system and the production stability.

Optimization and Control of Agent-Based Models in Biology: A Perspective.

Science.gov (United States)

An, G; Fitzpatrick, B G; Christley, S; Federico, P; Kanarek, A; Neilan, R Miller; Oremland, M; Salinas, R; Laubenbacher, R; Lenhart, S

2017-01-01

Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models, which leaves simulation as the primary approach. As models become large, simulation becomes challenging. This paper proposes a novel approach to two mathematical aspects of ABMs, optimization and control, and it presents a few first steps outlining how one might carry out this approach. Rather than viewing the ABM as a model, it is to be viewed as a surrogate for the actual system. For a given optimization or control problem (which may change over time), the surrogate system is modeled instead, using data from the ABM and a modeling framework for which ready-made mathematical tools exist, such as differential equations, or for which control strategies can explored more easily. Once the optimization problem is solved for the model of the surrogate, it is then lifted to the surrogate and tested. The final step is to lift the optimization solution from the surrogate system to the actual system. This program is illustrated with published work, using two relatively simple ABMs as a demonstration, Sugarscape and a consumer-resource ABM. Specific techniques discussed include dimension reduction and approximation of an ABM by difference equations as well systems of PDEs, related to certain specific control objectives. This demonstration illustrates the very challenging mathematical problems that need to be solved before this approach can be realistically applied to complex and large ABMs, current and future. The paper outlines a research program to address them.

Genetic Algorithms for Models Optimization for Recognition of Translation Initiation Sites

KAUST Repository

Mora, Arturo Magana

2011-06-01

This work uses genetic algorithms (GA) to reduce the complexity of the artificial neural networks (ANNs) and decision trees (DTs) for the accurate recognition of translation initiation sites (TISs) in Arabidopsis Thaliana. The Arabidopsis data was extracted directly from genomic DNA sequences. Methods derived in this work resulted in both reduced complexity of the predictors, as well as in improvement in prediction accuracy (generalization). Optimization through use of GA is generally a computationally intensive task. One of the approaches to overcome this problem is to use parallelization of code that implements GA, thus allowing computation on multiprocessing infrastructure. However, further improvement in performance GA implementation could be achieved through modification done to GA basic operations such as selection, crossover and mutation. In this work we explored two such improvements, namely evolutive mutation and GA-Simplex crossover operation. In this thesis we studied the benefit of these modifications on the problem of TISs recognition. Compared to the non-modified GA approach, we reduced the number of weights in the resulting model\\'s neural network component by 51% and the number of nodes in the model\\'s DTs component by 97% whilst improving the model\\'s accuracy at the same time. Separately, we developed another methodology for reducing the complexity of prediction models by optimizing the composition of training data subsets in bootstrap aggregation (bagging) methodology. This optimization is achieved by applying a new GA-based bagging methodology in order to optimize the composition of each of the training data subsets. This approach has shown in our test cases to considerably enhance the accuracy of the TIS prediction model compared to the original bagging methodology. Although these methods are applied to the problem of accurate prediction of TISs we believe that these methodologies have a potential for wider scope of application.

Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

Energy Technology Data Exchange (ETDEWEB)

Vrugt, Jasper A [Los Alamos National Laboratory; Wohling, Thomas [NON LANL

2008-01-01

Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

Model assisted multiobjective optimization with {lambda}-control; Multikriterielle Optimierung mit {lambda}-geregelten modellgestuetzten Evolutionsstrategien

Energy Technology Data Exchange (ETDEWEB)

Braun, Jan; Hoffmann, Frank; Krettek, Johannes; Bertram, Torsten [Technische Univ. Dortmund (Germany). Lehrstuhl RST

2009-07-01

Evolutionary algorithms require a large number of fitness evaluations in order to find an optimal solution. This property limits their application to hardware in the loop optimization or optimization of time-consuming simulations and calculations. This contribution proposes a preselection with data based models in order to reduce the number of true fitness evaluations. It extends previous approaches for model assisted scalar optimization to multiobjective problems by a proper redefinition of model quality and ?-control. The application to multiobjective benchmark optimization problems underlies the improved convergence of the model assisted evolution strategy compared to a multiobjective evolution strategy as well as the advantages of a {lambda}-controlled variant compared to a static preselection. (orig.)

NLP model and stochastic multi-start optimization approach for heat exchanger networks

International Nuclear Information System (INIS)

Núñez-Serna, Rosa I.; Zamora, Juan M.

2016-01-01

Highlights: • An NLP model for the optimal design of heat exchanger networks is proposed. • The NLP model is developed from a stage-wise grid diagram representation. • A two-phase stochastic multi-start optimization methodology is utilized. • Improved network designs are obtained with different heat load distributions. • Structural changes and reductions in the number of heat exchangers are produced. - Abstract: Heat exchanger network synthesis methodologies frequently identify good network structures, which nevertheless, might be accompanied by suboptimal values of design variables. The objective of this work is to develop a nonlinear programming (NLP) model and an optimization approach that aim at identifying the best values for intermediate temperatures, sub-stream flow rate fractions, heat loads and areas for a given heat exchanger network topology. The NLP model that minimizes the total annual cost of the network is constructed based on a stage-wise grid diagram representation. To improve the possibilities of obtaining global optimal designs, a two-phase stochastic multi-start optimization algorithm is utilized for the solution of the developed model. The effectiveness of the proposed optimization approach is illustrated with the optimization of two network designs proposed in the literature for two well-known benchmark problems. Results show that from the addressed base network topologies it is possible to achieve improved network designs, with redistributions in exchanger heat loads that lead to reductions in total annual costs. The results also show that the optimization of a given network design sometimes leads to structural simplifications and reductions in the total number of heat exchangers of the network, thereby exposing alternative viable network topologies initially not anticipated.

Optimal management of ecosystem services with pollution traps : The lake model revisited

NARCIS (Netherlands)

de Zeeuw, Aart; Grass, Dieter; Xepapadeas, Anastasios

2017-01-01

In this paper, optimal management of the lake model and common-property outcomes are reconsidered when the lake model is extended with the slowly changing variable. New optimal trajectories are found that were hidden in the simplified analysis. Furthermore, it is shown that two Nash equilibria may

Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization.

Science.gov (United States)

Kim, Seongho; Li, Lang

2014-02-01

The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Optimal control in a model of malaria with differential susceptibility

Science.gov (United States)

Hincapié, Doracelly; Ospina, Juan

2014-06-01

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

Shape interior modeling and mass property optimization using ray-reps

DEFF Research Database (Denmark)

Wu, Jun; Kramer, Lou; Westermann, Rüdiger

2016-01-01

, and prove this parametrization covers the optimal interior regarding static and rotational stability criteria. This compact formulation thoroughly reduces the number of design variables compared to the general volumetric element-wise formulation. We demonstrate the effectiveness of our reduced formulation......We present a novel method for the modeling and optimization of the material distribution inside 3D shapes, such that their 3D printed replicas satisfy prescribed constraints regarding mass properties. In particular, we introduce an extension of ray-representation to shape interior modeling...

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

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

Directory of Open Access Journals (Sweden)

Mysara Ahmed Mohamed

2017-07-01

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

Optimization of a centrifugal compressor impeller using CFD: the choice of simulation model parameters

Science.gov (United States)

Neverov, V. V.; Kozhukhov, Y. V.; Yablokov, A. M.; Lebedev, A. A.

2017-08-01

Nowadays the optimization using computational fluid dynamics (CFD) plays an important role in the design process of turbomachines. However, for the successful and productive optimization it is necessary to define a simulation model correctly and rationally. The article deals with the choice of a grid and computational domain parameters for optimization of centrifugal compressor impellers using computational fluid dynamics. Searching and applying optimal parameters of the grid model, the computational domain and solver settings allows engineers to carry out a high-accuracy modelling and to use computational capability effectively. The presented research was conducted using Numeca Fine/Turbo package with Spalart-Allmaras and Shear Stress Transport turbulence models. Two radial impellers was investigated: the high-pressure at ψT=0.71 and the low-pressure at ψT=0.43. The following parameters of the computational model were considered: the location of inlet and outlet boundaries, type of mesh topology, size of mesh and mesh parameter y+. Results of the investigation demonstrate that the choice of optimal parameters leads to the significant reduction of the computational time. Optimal parameters in comparison with non-optimal but visually similar parameters can reduce the calculation time up to 4 times. Besides, it is established that some parameters have a major impact on the result of modelling.

Worst-Case Investment and Reinsurance Optimization for an Insurer under Model Uncertainty

Directory of Open Access Journals (Sweden)

Xiangbo Meng

2016-01-01

Full Text Available In this paper, we study optimal investment-reinsurance strategies for an insurer who faces model uncertainty. The insurer is allowed to acquire new business and invest into a financial market which consists of one risk-free asset and one risky asset whose price process is modeled by a Geometric Brownian motion. Minimizing the expected quadratic distance of the terminal wealth to a given benchmark under the “worst-case” scenario, we obtain the closed-form expressions of optimal strategies and the corresponding value function by solving the Hamilton-Jacobi-Bellman (HJB equation. Numerical examples are presented to show the impact of model parameters on the optimal strategies.

Optimal Resource Management in a Stochastic Schaefer Model

OpenAIRE

Richard Hartman

2008-01-01

This paper incorporates uncertainty into the growth function of the Schaefer model for the optimal management of a biological resource. There is a critical value for the biological stock, and it is optimal to do no harvesting if the biological stock is below that critical value and to exert whatever harvesting effort is necessary to prevent the stock from rising above that critical value. The introduction of uncertainty increases the critical value of the stock.

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

Energy Technology Data Exchange (ETDEWEB)

Rengers, Francis; Lunacek, Monte; Tucker, Gregory

2016-06-01

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

Variability aware compact model characterization for statistical circuit design optimization

Science.gov (United States)

Qiao, Ying; Qian, Kun; Spanos, Costas J.

2012-03-01

Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose an efficient variabilityaware compact model characterization methodology based on the linear propagation of variance. Hierarchical spatial variability patterns of selected compact model parameters are directly calculated from transistor array test structures. This methodology has been implemented and tested using transistor I-V measurements and the EKV-EPFL compact model. Calculation results compare well to full-wafer direct model parameter extractions. Further studies are done on the proper selection of both compact model parameters and electrical measurement metrics used in the method.

Hyperopt: a Python library for model selection and hyperparameter optimization

Science.gov (United States)

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.

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.

Model-based thermal system design optimization for the James Webb Space Telescope

Science.gov (United States)

Cataldo, Giuseppe; Niedner, Malcolm B.; Fixsen, Dale J.; Moseley, Samuel H.

2017-10-01

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

Linear Model for Optimal Distributed Generation Size Predication

Directory of Open Access Journals (Sweden)

Ahmed Al Ameri

2017-01-01

Full Text Available This article presents a linear model predicting optimal size of Distributed Generation (DG that addresses the minimum power loss. This method is based fundamentally on strong coupling between active power and voltage angle as well as between reactive power and voltage magnitudes. This paper proposes simplified method to calculate the total power losses in electrical grid for different distributed generation sizes and locations. The method has been implemented and tested on several IEEE bus test systems. The results show that the proposed method is capable of predicting approximate optimal size of DG when compared with precision calculations. The method that linearizes a complex model showed a good result, which can actually reduce processing time required. The acceptable accuracy with less time and memory required can help the grid operator to assess power system integrated within large-scale distribution generation.

Dynamic optimization and robust explicit model predictive control of hydrogen storage tank

KAUST Repository

Panos, C.

2010-09-01

We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.

Dynamic optimization and robust explicit model predictive control of hydrogen storage tank

KAUST Repository

Panos, C.; Kouramas, K.I.; Georgiadis, M.C.; Pistikopoulos, E.N.

2010-01-01

We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.

Coastal aquifer management based on surrogate models and multi-objective optimization

Science.gov (United States)

Mantoglou, A.; Kourakos, G.

2011-12-01

The demand for fresh water in coastal areas and islands can be very high, especially in summer months, due to increased local needs and tourism. In order to satisfy demand, a combined management plan is proposed which involves: i) desalinization (if needed) of pumped water to a potable level using reverse osmosis and ii) injection of biologically treated waste water into the aquifer. The management plan is formulated into a multiobjective optimization framework, where simultaneous minimization of economic and environmental costs is desired; subject to a constraint to satisfy demand. The method requires modeling tools, which are able to predict the salinity levels of the aquifer in response to different alternative management scenarios. Variable density models can simulate the interaction between fresh and saltwater; however, they are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNN)]. The surrogate models are trained adaptively during optimization based on a Genetic Algorithm. In the crossover step of the genetic algorithm, each pair of parents generates a pool of offspring. All offspring are evaluated based on the fast surrogate model. Then only the most promising offspring are evaluated based on the exact numerical model. This eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. Three new criteria for selecting the most promising offspring were proposed, which improve the Pareto set and maintain the diversity of the optimum solutions. The method has important advancements compared to previous methods, e.g. alleviation of propagation of errors due to surrogate model approximations. The method is applied to a real coastal aquifer in the island of Santorini which is a very touristy island with high water demands. The results show that the algorithm

Optimization of morphing flaps based on fluid structure interaction modeling

DEFF Research Database (Denmark)

Barlas, Athanasios; Akay, Busra

2018-01-01

This article describes the design optimization of morphing trailing edge flaps for wind turbines with ‘smart blades’. A high fidelity Fluid Structure Interaction (FSI) simulation framework is utilized, comprised of 2D Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) models....... A coupled aero-structural simulation of a 10% chordwise length morphing trailing edge flap for a 4 MW wind turbine rotor is carried out and response surfaces are produced with respect to the flap internal geometry design parameters for the design conditions. Surrogate model based optimization is applied...

Modeling and optimization of a utility system containing multiple extractions steam turbines

International Nuclear Information System (INIS)

Luo, Xianglong; Zhang, Bingjian; Chen, Ying; Mo, Songping

2011-01-01

Complex turbines with multiple controlled and/or uncontrolled extractions are popularly used in the processing industry and cogeneration plants to provide steam of different levels, electric power, and driving power. To characterize thermodynamic behavior under varying conditions, nonlinear mathematical models are developed based on energy balance, thermodynamic principles, and semi-empirical equations. First, the complex turbine is decomposed into several simple turbines from the controlled extraction stages and modeled in series. THM (The turbine hardware model) developing concept is applied to predict the isentropic efficiency of the decomposed simple turbines. Stodola's formulation is also used to simulate the uncontrolled extraction steam parameters. The thermodynamic properties of steam and water are regressed through linearization or piece-wise linearization. Second, comparison between the simulated results using the proposed model and the data in the working condition diagram provided by the manufacturer is conducted over a wide range of operations. The simulation results yield small deviation from the data in the working condition diagram where the maximum modeling error is 0.87% among the compared seven operation conditions. Last, the optimization model of a utility system containing multiple extraction turbines is established and a detailed case is analyzed. Compared with the conventional operation strategy, a maximum of 5.47% of the total operation cost is saved using the proposed optimization model. -- Highlights: → We develop a complete simulation model for steam turbine with multiple extractions. → We test the simulation model using the performance data of commercial turbines. → The simulation error of electric power generation is no more than 0.87%. → We establish a utility system operational optimization model. → The optimal industrial operation scheme featured with 5.47% of cost saving.

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

Science.gov (United States)

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

2014-01-01

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

3D Model Optimization of Four-Facet Drill for 3D Drilling Simulation

Directory of Open Access Journals (Sweden)

Buranský Ivan

2016-09-01

Full Text Available The article is focused on optimization of four-facet drill for 3D drilling numerical modelling. For optimization, the process of reverse engineering by PowerShape software was used. The design of four-facet drill was created in NumrotoPlus software. The modified 3D model of the drill was used in the numerical analysis of cutting forces. Verification of the accuracy of 3D models for reverse engineering was implemented using the colour deviation maps. The CAD model was in the STEP format. For simulation software, 3D model in the STEP format is ideal. STEP is a solid model. Simulation software automatically splits the 3D model into finite elements. The STEP model was therefore more suitable than the STL model.

An internet graph model based on trade-off optimization

Science.gov (United States)

Alvarez-Hamelin, J. I.; Schabanel, N.

2004-03-01

This paper presents a new model for the Internet graph (AS graph) based on the concept of heuristic trade-off optimization, introduced by Fabrikant, Koutsoupias and Papadimitriou in[CITE] to grow a random tree with a heavily tailed degree distribution. We propose here a generalization of this approach to generate a general graph, as a candidate for modeling the Internet. We present the results of our simulations and an analysis of the standard parameters measured in our model, compared with measurements from the physical Internet graph.

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...

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

Energy Technology Data Exchange (ETDEWEB)

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

2005-07-01

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

CT radiation dose and image quality optimization using a porcine model.

Science.gov (United States)

Zarb, Francis; McEntee, Mark F; Rainford, Louise

2013-01-01

To evaluate potential radiation dose savings and resultant image quality effects with regard to optimization of commonly performed computed tomography (CT) studies derived from imaging a porcine (pig) model. Imaging protocols for 4 clinical CT suites were developed based on the lowest milliamperage and kilovoltage, the highest pitch that could be set from current imaging protocol parameters, or both. This occurred before significant changes in noise, contrast, and spatial resolution were measured objectively on images produced from a quality assurance CT phantom. The current and derived phantom protocols were then applied to scan a porcine model for head, abdomen, and chest CT studies. Further optimized protocols were developed based on the same methodology as in the phantom study. The optimization achieved with respect to radiation dose and image quality was evaluated following data collection of radiation dose recordings and image quality review. Relative visual grading analysis of image quality criteria adapted from the European guidelines on radiology quality criteria for CT were used for studies completed with both the phantom-based or porcine-derived imaging protocols. In 5 out of 16 experimental combinations, the current clinical protocol was maintained. In 2 instances, the phantom protocol reduced radiation dose by 19% to 38%. In the remaining 9 instances, the optimization based on the porcine model further reduced radiation dose by 17% to 38%. The porcine model closely reflects anatomical structures in humans, allowing the grading of anatomical criteria as part of image quality review without radiation risks to human subjects. This study demonstrates that using a porcine model to evaluate CT optimization resulted in more radiation dose reduction than when imaging protocols were tested solely on quality assurance phantoms.

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.

Optimizing DNA assembly based on statistical language modelling.

Science.gov (United States)

Fang, Gang; Zhang, Shemin; Dong, Yafei

2017-12-15

By successively assembling genetic parts such as BioBrick according to grammatical models, complex genetic constructs composed of dozens of functional blocks can be built. However, usually every category of genetic parts includes a few or many parts. With increasing quantity of genetic parts, the process of assembling more than a few sets of these parts can be expensive, time consuming and error prone. At the last step of assembling it is somewhat difficult to decide which part should be selected. Based on statistical language model, which is a probability distribution P(s) over strings S that attempts to reflect how frequently a string S occurs as a sentence, the most commonly used parts will be selected. Then, a dynamic programming algorithm was designed to figure out the solution of maximum probability. The algorithm optimizes the results of a genetic design based on a grammatical model and finds an optimal solution. In this way, redundant operations can be reduced and the time and cost required for conducting biological experiments can be minimized. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Comparative evaluation of fuzzy logic and genetic algorithms models for portfolio optimization

Directory of Open Access Journals (Sweden)

Heidar Masoumi Soureh

2017-03-01

Full Text Available Selection of optimum methods which have appropriate speed and precision for planning and de-cision-making has always been a challenge for investors and managers. One the most important concerns for them is investment planning and optimization for acquisition of desirable wealth under controlled risk with the best return. This paper proposes a model based on Markowitz the-orem by considering the aforementioned limitations in order to help effective decisions-making for portfolio selection. Then, the model is investigated by fuzzy logic and genetic algorithms, for the optimization of the portfolio in selected active companies listed in Tehran Stock Exchange over the period 2012-2016 and the results of the above models are discussed. The results show that the two studied models had functional differences in portfolio optimization, its tools and the possibility of supplementing each other and their selection.

OPTIMIZATION OF ATM AND BRANCH CASH OPERATIONS USING AN INTEGRATED CASH REQUIREMENT FORECASTING AND CASH OPTIMIZATION MODEL

Directory of Open Access Journals (Sweden)

Canser BİLİR

2018-04-01

Full Text Available In this study, an integrated cash requirement forecasting and cash inventory optimization model is implemented in both the branch and automated teller machine (ATM networks of a mid-sized bank in Turkey to optimize the bank’s cash supply chain. The implemented model’s objective is to minimize the idle cash levels at both branches and ATMs without decreasing the customer service level (CSL by providing the correct amount of cash at the correct location and time. To the best of our knowledge, the model is the first integrated model in the literature to be applied to both ATMs and branches simultaneously. The results demonstrated that the integrated model dramatically decreased the idle cash levels at both branches and ATMs without degrading the availability of cash and hence customer satisfaction. An in-depth analysis of the results also indicated that the results were more remarkable for branches. The results also demonstrated that the utilization of various seasonal indices plays a very critical role in the forecasting of cash requirements for a bank. Another unique feature of the study is that the model is the first to include the recycling feature of ATMs. The results demonstrated that as a result of the inclusion of the deliberate seasonal indices in the forecasting model, the integrated cash optimization models can be used to estimate the cash requirements of recycling ATMs.

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.

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.

Optimization and Simulation Modeling of Disaster Relief Supply Chain: A Literature Review

OpenAIRE

Feng, Keli; Bizimana, Emmanuel; Agu, Deedee D.; Issac, Tana T.

2012-01-01

Recent natural and man-made disasters underscore the need of a resilient and agile disaster relief supply chain to mitigate the damages and save people’s lives. Optimization and simulation modeling have become powerful and useful tools to help decision makers tackle problems related to disaster relief supply chain. This paper reviews optimization and simulation models used in the field of disaster relief supply chain. We review the literature of the facility location optimization problems of ...

OPTIMAL STOCK MODELING FOR NON-FOODS RETAILER SELLING ON-CREDIT

Directory of Open Access Journals (Sweden)

Vladimir V. Manakhov

2016-01-01

Full Text Available The article is dedicated to the problem of retail stock optimization within mo-nopolistic competition market while selling non-food goods to customers on-credit. Optimization model has been developed and appropriate technique of stock volume planning has been introduced

Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model

Directory of Open Access Journals (Sweden)

Yi Liu

2016-01-01

Full Text Available Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithm MODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II on a daily streamflow forecasting model based on support vector machine (SVM. The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.

Entropy-Based Experimental Design for Optimal Model Discrimination in the Geosciences

Directory of Open Access Journals (Sweden)

Wolfgang Nowak

2016-11-01

Full Text Available Choosing between competing models lies at the heart of scientific work, and is a frequent motivation for experimentation. Optimal experimental design (OD methods maximize the benefit of experiments towards a specified goal. We advance and demonstrate an OD approach to maximize the information gained towards model selection. We make use of so-called model choice indicators, which are random variables with an expected value equal to Bayesian model weights. Their uncertainty can be measured with Shannon entropy. Since the experimental data are still random variables in the planning phase of an experiment, we use mutual information (the expected reduction in Shannon entropy to quantify the information gained from a proposed experimental design. For implementation, we use the Preposterior Data Impact Assessor framework (PreDIA, because it is free of the lower-order approximations of mutual information often found in the geosciences. In comparison to other studies in statistics, our framework is not restricted to sequential design or to discrete-valued data, and it can handle measurement errors. As an application example, we optimize an experiment about the transport of contaminants in clay, featuring the problem of choosing between competing isotherms to describe sorption. We compare the results of optimizing towards maximum model discrimination with an alternative OD approach that minimizes the overall predictive uncertainty under model choice uncertainty.

Modeling and optimization of batteryless hybrid PV (photovoltaic)/Diesel systems for off-grid applications

International Nuclear Information System (INIS)

Tsuanyo, David; Azoumah, Yao; Aussel, Didier; Neveu, Pierre

2015-01-01

This paper presents a new model and optimization procedure for off-grid hybrid PV (photovoltaic)/Diesel systems operating without battery storage. The proposed technico-economic model takes into account the variability of both the solar irradiation and the electrical loads. It allows optimizing the design and the operation of the hybrid systems by searching their lowest LCOE (Levelized Cost of Electricity). Two cases have been investigated: identical Diesel generators and Diesel generators with different sizes, and both are compared to conventional standalone Diesel generator systems. For the same load profile, the optimization results show that the LCOE of the optimized batteryless hybrid solar PV/Diesel (0.289 €/kWh for the hybrid system with identical Diesel generators and 0.284 €/kWh for the hybrid system with different sizes of Diesel generators) is lower than the LCOE obtained with standalone Diesel generators (0.32 €/kWh for the both cases). The obtained results are then confirmed by HOMER (Hybrid Optimization Model for Electric Renewables) software. - Highlights: • A technico-economic model for optimal design and operation management of batteryless hybrid systems is developed. • The model allows optimizing design and operation of hybrid systems by ensuring their lowest LCOE. • The model was validated by HOMER. • Batteryless hybrid system are suitable for off-grid applications

Evaluation of load flow and grid expansion in a unit-commitment and expansion optimization model SciGRID International Conference on Power Grid Modelling

Science.gov (United States)

Senkpiel, Charlotte; Biener, Wolfgang; Shammugam, Shivenes; Längle, Sven

2018-02-01

Energy system models serve as a basis for long term system planning. Joint optimization of electricity generating technologies, storage systems and the electricity grid leads to lower total system cost compared to an approach in which the grid expansion follows a given technology portfolio and their distribution. Modelers often face the problem of finding a good tradeoff between computational time and the level of detail that can be modeled. This paper analyses the differences between a transport model and a DC load flow model to evaluate the validity of using a simple but faster transport model within the system optimization model in terms of system reliability. The main findings in this paper are that a higher regional resolution of a system leads to better results compared to an approach in which regions are clustered as more overloads can be detected. An aggregation of lines between two model regions compared to a line sharp representation has little influence on grid expansion within a system optimizer. In a DC load flow model overloads can be detected in a line sharp case, which is therefore preferred. Overall the regions that need to reinforce the grid are identified within the system optimizer. Finally the paper recommends the usage of a load-flow model to test the validity of the model results.

Adaptive parametric model order reduction technique for optimization of vibro-acoustic models: Application to hearing aid design

Science.gov (United States)

Creixell-Mediante, Ester; Jensen, Jakob S.; Naets, Frank; Brunskog, Jonas; Larsen, Martin

2018-06-01

Finite Element (FE) models of complex structural-acoustic coupled systems can require a large number of degrees of freedom in order to capture their physical behaviour. This is the case in the hearing aid field, where acoustic-mechanical feedback paths are a key factor in the overall system performance and modelling them accurately requires a precise description of the strong interaction between the light-weight parts and the internal and surrounding air over a wide frequency range. Parametric optimization of the FE model can be used to reduce the vibroacoustic feedback in a device during the design phase; however, it requires solving the model iteratively for multiple frequencies at different parameter values, which becomes highly time consuming when the system is large. Parametric Model Order Reduction (pMOR) techniques aim at reducing the computational cost associated with each analysis by projecting the full system into a reduced space. A drawback of most of the existing techniques is that the vector basis of the reduced space is built at an offline phase where the full system must be solved for a large sample of parameter values, which can also become highly time consuming. In this work, we present an adaptive pMOR technique where the construction of the projection basis is embedded in the optimization process and requires fewer full system analyses, while the accuracy of the reduced system is monitored by a cheap error indicator. The performance of the proposed method is evaluated for a 4-parameter optimization of a frequency response for a hearing aid model, evaluated at 300 frequencies, where the objective function evaluations become more than one order of magnitude faster than for the full system.

An Optimal Electric Dipole Antenna Model and Its Field Propagation

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Yidong Xu

2016-01-01

Full Text Available An optimal electric dipole antennas model is presented and analyzed, based on the hemispherical grounding equivalent model and the superposition principle. The paper also presents a full-wave electromagnetic simulation for the electromagnetic field propagation in layered conducting medium, which is excited by the horizontal electric dipole antennas. Optimum frequency for field transmission in different depth is carried out and verified by the experimental results in comparison with previously reported simulation over a digital wireless Through-The-Earth communication system. The experimental results demonstrate that the dipole antenna grounding impedance and the output power can be efficiently reduced by using the optimal electric dipole antenna model and operating at the optimum frequency in a vertical transmission depth up to 300 m beneath the surface of the earth.

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...

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...

Model-based plant-wide optimization of large-scale lignocellulosic bioethanol plants

DEFF Research Database (Denmark)

Prunescu, Remus Mihail; Blanke, Mogens; Jakobsen, Jon Geest

2017-01-01

Second generation biorefineries transform lignocellulosic biomass into chemicals with higher added value following a conversion mechanism that consists of: pretreatment, enzymatic hydrolysis, fermentation and purification. The objective of this study is to identify the optimal operational point...... with respect to maximum economic profit of a large scale biorefinery plant using a systematic model-based plantwide optimization methodology. The following key process parameters are identified as decision variables: pretreatment temperature, enzyme dosage in enzymatic hydrolysis, and yeast loading per batch...... in fermentation. The plant is treated in an integrated manner taking into account the interactions and trade-offs between the conversion steps. A sensitivity and uncertainty analysis follows at the optimal solution considering both model and feed parameters. It is found that the optimal point is more sensitive...

Analytic model for ultrasound energy receivers and their optimal electric loads

Science.gov (United States)

Gorostiaga, M.; Wapler, M. C.; Wallrabe, U.

2017-08-01

In this paper, we present an analytic model for thickness resonating plate ultrasound energy receivers, which we have derived from the piezoelectric and the wave equations and, in which we have included dielectric, viscosity and acoustic attenuation losses. Afterwards, we explore the optimal electric load predictions by the zero reflection and power maximization approaches present in the literature with different acoustic boundary conditions, and discuss their limitations. To validate our model, we compared our expressions with the KLM model solved numerically with very good agreement. Finally, we discuss the differences between the zero reflection and power maximization optimal electric loads, which start to differ as losses in the receiver increase.

Layout optimization of DRAM cells using rigorous simulation model for NTD

Science.gov (United States)

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

2014-03-01

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

Mean-Variance-CvaR Model of Multiportfolio Optimization via Linear Weighted Sum Method

Directory of Open Access Journals (Sweden)

Younes Elahi

2014-01-01

Full Text Available We propose a new approach to optimizing portfolios to mean-variance-CVaR (MVC model. Although of several researches have studied the optimal MVC model of portfolio, the linear weighted sum method (LWSM was not implemented in the area. The aim of this paper is to investigate the optimal portfolio model based on MVC via LWSM. With this method, the solution of the MVC model of portfolio as the multiobjective problem is presented. In data analysis section, this approach in investing on two assets is investigated. An MVC model of the multiportfolio was implemented in MATLAB and tested on the presented problem. It is shown that, by using three objective functions, it helps the investors to manage their portfolio better and thereby minimize the risk and maximize the return of the portfolio. The main goal of this study is to modify the current models and simplify it by using LWSM to obtain better results.

Analytical development and optimization of a graphene–solution interface capacitance model

Directory of Open Access Journals (Sweden)

Hediyeh Karimi

2014-05-01

Full Text Available Graphene, which as a new carbon material shows great potential for a range of applications because of its exceptional electronic and mechanical properties, becomes a matter of attention in these years. The use of graphene in nanoscale devices plays an important role in achieving more accurate and faster devices. Although there are lots of experimental studies in this area, there is a lack of analytical models. Quantum capacitance as one of the important properties of field effect transistors (FETs is in our focus. The quantum capacitance of electrolyte-gated transistors (EGFETs along with a relevant equivalent circuit is suggested in terms of Fermi velocity, carrier density, and fundamental physical quantities. The analytical model is compared with the experimental data and the mean absolute percentage error (MAPE is calculated to be 11.82. In order to decrease the error, a new function of E composed of α and β parameters is suggested. In another attempt, the ant colony optimization (ACO algorithm is implemented for optimization and development of an analytical model to obtain a more accurate capacitance model. To further confirm this viewpoint, based on the given results, the accuracy of the optimized model is more than 97% which is in an acceptable range of accuracy.

Regional integrated solid waste management: an optimization model for northern Lebanon

International Nuclear Information System (INIS)

Abou Najm, M.; El Fadel, M.; El-Taha, M.; Ayoub, G.; Al-Awar

2000-01-01

Full text.Increased environmental concerns and the emphasis on material and energy recovery are gradually changing the orientation of municipal solid waste (MSW) management and planning. In this context, the application of optimization techniques have been introduced to design the least cost solid waste management systems, considering the variety of management processes (recycling, composting, anaerobic digestion, incineration and land filling) and the existence of uncertainties associated with the number of system components and their interrelations. This study presents a model that was developed and applied to serve as a solid socio-economic and environmental considerations. The model accounts for solid waste generation rates, composition, collection, treatment, disposal as well as potential environmental impacts of various MSW management techniques. The model follows a linear programming formulation with the framework of dynamic optimization. The model can serve as a tool to evaluate various MSW management alternatives and obtain the optimal combination of technologies for the handling, treatment and disposal of MSW in an economic and environmentally sustainable way. The sensitivity of various waste management policies is also addressed. Finally, the region of Northern Lebanon was considered as a case study with data collected for the year 2000, to demonstrate the applicability of the model

An optimal control strategies using vaccination and fogging in dengue fever transmission model

Science.gov (United States)

Fitria, Irma; Winarni, Pancahayani, Sigit; Subchan

2017-08-01

This paper discussed regarding a model and an optimal control problem of dengue fever transmission. We classified the model as human and vector (mosquito) population classes. For the human population, there are three subclasses, such as susceptible, infected, and resistant classes. Then, for the vector population, we divided it into wiggler, susceptible, and infected vector classes. Thus, the model consists of six dynamic equations. To minimize the number of dengue fever cases, we designed two optimal control variables in the model, the giving of fogging and vaccination. The objective function of this optimal control problem is to minimize the number of infected human population, the number of vector, and the cost of the controlling efforts. By giving the fogging optimally, the number of vector can be minimized. In this case, we considered the giving of vaccination as a control variable because it is one of the efforts that are being developed to reduce the spreading of dengue fever. We used Pontryagin Minimum Principle to solve the optimal control problem. Furthermore, the numerical simulation results are given to show the effect of the optimal control strategies in order to minimize the epidemic of dengue fever.

Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

Science.gov (United States)

Ariyarit, Atthaphon; Sugiura, Masahiko; Tanabe, Yasutada; Kanazaki, Masahiro

2018-06-01

A multi-fidelity optimization technique by an efficient global optimization process using a hybrid surrogate model is investigated for solving real-world design problems. The model constructs the local deviation using the kriging method and the global model using a radial basis function. The expected improvement is computed to decide additional samples that can improve the model. The approach was first investigated by solving mathematical test problems. The results were compared with optimization results from an ordinary kriging method and a co-kriging method, and the proposed method produced the best solution. The proposed method was also applied to aerodynamic design optimization of helicopter blades to obtain the maximum blade efficiency. The optimal shape obtained by the proposed method achieved performance almost equivalent to that obtained using the high-fidelity, evaluation-based single-fidelity optimization. Comparing all three methods, the proposed method required the lowest total number of high-fidelity evaluation runs to obtain a converged solution.

Modeling and analysis of a decentralized electricity market: An integrated simulation/optimization approach

International Nuclear Information System (INIS)

Sarıca, Kemal; Kumbaroğlu, Gürkan; Or, Ilhan

2012-01-01

In this study, a model is developed to investigate the implications of an hourly day-ahead competitive power market on generator profits, electricity prices, availability and supply security. An integrated simulation/optimization approach is employed integrating a multi-agent simulation model with two alternative optimization models. The simulation model represents interactions between power generator, system operator, power user and power transmitter agents while the network flow optimization model oversees and optimizes the electricity flows, dispatches generators based on two alternative approaches used in the modeling of the underlying transmission network: a linear minimum cost network flow model and a non-linear alternating current optimal power flow model. Supply, demand, transmission, capacity and other technological constraints are thereby enforced. The transmission network, on which the scenario analyses are carried out, includes 30 bus, 41 lines, 9 generators, and 21 power users. The scenarios examined in the analysis cover various settings of transmission line capacities/fees, and hourly learning algorithms. Results provide insight into key behavioral and structural aspects of a decentralized electricity market under network constraints and reveal the importance of using an AC network instead of a simplified linear network flow approach. -- Highlights: ► An agent-based simulation model with an AC transmission environment with a day-ahead market. ► Physical network parameters have dramatic effects over price levels and stability. ► Due to AC nature of transmission network, adaptive agents have more local market power than minimal cost network flow. ► Behavior of the generators has significant effect over market price formation, as pointed out by bidding strategies. ► Transmission line capacity and fee policies are found to be very effective in price formation in the market.

Study and optimization of the partial discharges in capacitor model ...

African Journals Online (AJOL)

Page 1 ... experiments methodology for the study of such processes, in view of their modeling and optimization. The obtained result is a mathematical model capable to identify the parameters and the interactions between .... 5mn; the next landing is situated in 200 V over the voltage of partial discharges appearance and.

Optimization model of a system of crude oil distillation units whit heat integration and meta modeling

International Nuclear Information System (INIS)

Lopez, Diana C; Mahecha, Cesar A; Hoyos, Luis J; Acevedo, Leonardo; Villamizar Jaime F

2009-01-01

The process of crude distillation impacts the economy of any refinery in a considerable manner. Therefore, it is necessary to improve it taking good advantage of the available infrastructure, generating products that conform to the specifications without violating the equipment operating constraints or plant restrictions at industrial units. The objective of this paper is to present the development of an optimization model for a Crude Distillation Unit (CDU) system at a ECOPETROL S.A. refinery in Barrancabermeja, involving the typical restrictions (flow according to pipeline capacity, pumps, distillation columns, etc) and a restriction that has not been included in bibliographic reports for this type of models: the heat integration of streams from Atmospheric Distillation Towers (ADTs) and Vacuum Distillation Towers (VDT) with the heat exchanger networks for crude pre-heating. On the other hand, ADTs were modeled with Meta models in function of column temperatures and pressures, pumparounds flows and return temperatures, stripping steam flows, Jet EBP ASTM D-86 and Diesel EBP ASTM D-86. Pre-heating trains were modeled with mass and energy balances, and design equation of each heat exchanger. The optimization model is NLP, maximizing the system profit. This model was implemented in GAMSide 22,2 using the CONOPT solver and it found new operating points with better economic results than those obtained with the normal operation in the real plants. It predicted optimum operation conditions of 3 ADTs for constant composition crude and calculated the yields and properties of atmospheric products, additional to temperatures and duties of 27 Crude Oil exchangers.

From leaf longevity to canopy seasonality: a carbon optimality phenology model for tropical evergreen forests

Science.gov (United States)

Xu, X.; Medvigy, D.; Wu, J.; Wright, S. J.; Kitajima, K.; Pacala, S. W.

2016-12-01

Tropical evergreen forests play a key role in the global carbon, water and energy cycles. Despite apparent evergreenness, this biome shows strong seasonality in leaf litter and photosynthesis. Recent studies have suggested that this seasonality is not directly related to environmental variability but is dominated by seasonal changes of leaf development and senescence. Meanwhile, current terrestrial biosphere models (TBMs) can not capture this pattern because leaf life cycle is highly underrepresented. One challenge to model this leaf life cycle is the remarkable diversity in leaf longevity, ranging from several weeks to multiple years. Ecologists have proposed models where leaf longevity is regarded as a strategy to optimize carbon gain. However previous optimality models can not be readily integrated into TBMs because (i) there are still large biases in predicted leaf longevity and (ii) it is never tested whether the carbon optimality model can capture the observed seasonality in leaf demography and canopy photosynthesis. In this study, we develop a new carbon optimality model for leaf demography. The novelty of our approach is two-fold. First, we incorporate a mechanistic photosynthesis model that can better estimate leaf carbon gain. Second, we consider the interspecific variations in leaf senescence rate, which strongly influence the modelled optimal carbon gain. We test our model with a leaf trait database for Panamanian evergreen forests. Then, we apply the model at seasonal scale and compare simulated seasonality of leaf litter and canopy photosynthesis with in-situ observations from several Amazonian forest sites. We find that (i) compared with original optimality model, the regression slope between observed and predicted leaf longevity increases from 0.15 to 1.04 in our new model and (ii) that our new model can capture the observed seasonal variations of leaf demography and canopy photosynthesis. Our results suggest that the phenology in tropical evergreen

Optimization of Excitation in FDTD Method and Corresponding Source Modeling

Directory of Open Access Journals (Sweden)

B. Dimitrijevic

2015-04-01

Full Text Available Source and excitation modeling in FDTD formulation has a significant impact on the method performance and the required simulation time. Since the abrupt source introduction yields intensive numerical variations in whole computational domain, a generally accepted solution is to slowly introduce the source, using appropriate shaping functions in time. The main goal of the optimization presented in this paper is to find balance between two opposite demands: minimal required computation time and acceptable degradation of simulation performance. Reducing the time necessary for source activation and deactivation is an important issue, especially in design of microwave structures, when the simulation is intensively repeated in the process of device parameter optimization. Here proposed optimized source models are realized and tested within an own developed FDTD simulation environment.

Optimal electricity price calculation model for retailers in a deregulated market

Energy Technology Data Exchange (ETDEWEB)

Yusta, J.M.; Dominguez-Navarro, J.A. [Zaragoza Univ., Dept. of Electrical Engineering, Zaragoza (Spain); Ramirez-Rosado, I.J. [La Rioja Univ., Dept. of Electrical Engineering, Logrono (Spain); Perez-Vidal, J.M. [McKinnon and Clarke, Energy Services Div., Zaragoza (Spain)

2005-07-01

The electricity retailing, a new business in deregulated electric power systems, needs the development of efficient tools to optimize its operation. This paper defines a technical-economic model of an electric energy service provider in the environment of the deregulated electricity market in Spain. This model results in an optimization problem, for calculating the optimal electric power and energy selling prices that maximize the economic profits obtained by the provider. This problem is applied to different cases, where the impact on the profits of several factors, such as the price strategy, the discount on tariffs and the elasticity of customer demand functions, is studied. (Author)

Optimal electricity price calculation model for retailers in a deregulated market

International Nuclear Information System (INIS)