Optimization modeling with spreadsheets
Baker, Kenneth R
2015-01-01
An accessible introduction to optimization analysis using spreadsheets Updated and revised, Optimization Modeling with Spreadsheets, Third Edition emphasizes model building skills in optimization analysis. By emphasizing both spreadsheet modeling and optimization tools in the freely available Microsoft® Office Excel® Solver, the book illustrates how to find solutions to real-world optimization problems without needing additional specialized software. The Third Edition includes many practical applications of optimization models as well as a systematic framework that il
Optimization Modeling with Spreadsheets
Baker, Kenneth R
2011-01-01
This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming and heuristic programming; as well as an emphasis on model building using Excel and Solver. The emphasis on model building (rather than algorithms) is one of the features that makes this book distinctive. Most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the sp
Model Risk in Portfolio Optimization
Directory of Open Access Journals (Sweden)
David Stefanovits
2014-08-01
Full Text Available We consider a one-period portfolio optimization problem under model uncertainty. For this purpose, we introduce a measure of model risk. We derive analytical results for this measure of model risk in the mean-variance problem assuming we have observations drawn from a normal variance mixture model. This model allows for heavy tails, tail dependence and leptokurtosis of marginals. The results show that mean-variance optimization is seriously compromised by model uncertainty, in particular, for non-Gaussian data and small sample sizes. To mitigate these shortcomings, we propose a method to adjust the sample covariance matrix in order to reduce model risk.
Risk modelling in portfolio optimization
Lam, W. H.; Jaaman, Saiful Hafizah Hj.; Isa, Zaidi
2013-09-01
Risk management is very important in portfolio optimization. The mean-variance model has been used in portfolio optimization to minimize the investment risk. The objective of the mean-variance model is to minimize the portfolio risk and achieve the target rate of return. Variance is used as risk measure in the mean-variance model. The purpose of this study is to compare the portfolio composition as well as performance between the optimal portfolio of mean-variance model and equally weighted portfolio. Equally weighted portfolio means the proportions that are invested in each asset are equal. The results show that the portfolio composition of the mean-variance optimal portfolio and equally weighted portfolio are different. Besides that, the mean-variance optimal portfolio gives better performance because it gives higher performance ratio than the equally weighted portfolio.
Pyomo optimization modeling in Python
Hart, William E; Watson, Jean-Paul; Woodruff, David L; Hackebeil, Gabriel A; Nicholson, Bethany L; Siirola, John D
2017-01-01
This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. This second edition provides an expanded presentation of Pyomo’s modeling capabilities, providing a broader description of the software that will enable the user to develop and optimize models. Introductory chapters have been revised to extend tutorials; chapters that discuss advanced features now include the new functionalities added to Pyomo since the first edition including generalized disjunctive programming, mathematical programming with equilibrium constraints, and bilevel programming. Pyomo is an open source software package fo...
Optimal Strategy and Business Models
DEFF Research Database (Denmark)
Johnson, Peter; Foss, Nicolai Juul
2016-01-01
, it is possible to formalize useful notions of a business model, resources, and competitive advantage. The business model that underpins strategy may be seen as a set of constraints on resources that can be interpreted as controls in optimal control theory. Strategy then might be considered to be the control...... 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....
The Brookhaven Process Optimization Models
Energy Technology Data Exchange (ETDEWEB)
Pilati, D. A.; Sparrow, F. T.
1979-01-01
The Brookhaven National Laboratory Industry Model Program (IMP) has undertaken the development of a set of industry-specific process-optimization models. These models are to be used for energy-use projections, energy-policy analyses, and process technology assessments. Applications of the models currently under development show that system-wide energy impacts may be very different from engineering estimates, selected investment tax credits for cogeneration (or other conservation strategies) may have the perverse effect of increasing industrial energy use, and that a proper combination of energy taxes and investment tax credits is more socially desirable than either policy alone. A section is included describing possible extensions of these models to answer questions or address other systems (e.g., a single plant instead of an entire industry).
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
This paper describes the modelling, simulating and optimizing including experimental verification as being carried out as part of a Ph.D. project being written resp. supervised by the authors. The work covers dynamic performance of both water-tube boilers and fire tube boilers. A detailed dynamic...... model of the boiler has been developed and simulations carried out by means of the Matlab integration routines. The model is prepared as a dynamic model consisting of both ordinary differential equations and algebraic equations, together formulated as a Differential-Algebraic-Equation system. Being able...... to operate a boiler plant dynamically means that the boiler designs must be able to absorb any fluctuations in water level and temperature gradients resulting from the pressure change in the boiler. On the one hand a large water-/steam space may be required, i.e. to build the boiler as big as possible. Due...
MODELLING, SIMULATING AND OPTIMIZING BOILERS
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
This paper describes the modelling, simulating and optimizing including experimental verification as being carried out as part of a Ph.D. project being written resp. supervised by the authors. The work covers dynamic performance of both water-tube boilers and fire tube boilers. A detailed dynamic...... to the internal pressure the consequence of the increased volume (i.e. water-/steam space) is an increased wall thickness in the pressure part of the boiler. The stresses introduced in the boiler pressure part as a result of the temperature gradients are proportional to the square of the wall thickness...... model of the boiler has been developed and simulations carried out by means of the Matlab integration routines. The model is prepared as a dynamic model consisting of both ordinary differential equations and algebraic equations, together formulated as a Differential-Algebraic-Equation system. Being able...
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...
Bioprocess optimization under uncertainty using ensemble modeling
Liu, Yang; Gunawan, Rudiyanto
2017-01-01
The performance of model-based bioprocess optimizations depends on the accuracy of the mathematical model. However, models of bioprocesses often have large uncertainty due to the lack of model identifiability. In the presence of such uncertainty, process optimizations that rely on the predictions of a single “best fit” model, e.g. the model resulting from a maximum likelihood parameter estimation using the available process data, may perform poorly in real life. In this study, we employed ens...
Optimal Appearance Model for Visual Tracking.
Directory of Open Access Journals (Sweden)
Yuru Wang
Full Text Available Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models.
Simulation–optimization model for groundwater contamination ...
Indian Academy of Sciences (India)
the current optimum particles (Kennedy & Eberhart 1995). Parsopoulas & Vrahatis (2002) also concluded that PSO is a very useful and simple technique for solving complex problems. If simulation model is embedded in an optimization model using advanced optimization tools like particle swarm optimization (PSO) or ...
Model-Based Optimizing Control and Estimation Using Modelica Model
Directory of Open Access Journals (Sweden)
L. Imsland
2010-07-01
Full Text Available This paper reports on experiences from case studies in using Modelica/Dymola models interfaced to control and optimization software, as process models in real time process control applications. Possible applications of the integrated models are in state- and parameter estimation and nonlinear model predictive control. It was found that this approach is clearly possible, providing many advantages over modeling in low-level programming languages. However, some effort is required in making the Modelica models accessible to NMPC software.
Rethinking exchange market models as optimization algorithms
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
Optimal design for nonlinear response models
Fedorov, Valerii V
2013-01-01
Optimal Design for Nonlinear Response Models discusses the theory and applications of model-based experimental design with a strong emphasis on biopharmaceutical studies. The book draws on the authors' many years of experience in academia and the pharmaceutical industry. While the focus is on nonlinear models, the book begins with an explanation of the key ideas, using linear models as examples. Applying the linearization in the parameter space, it then covers nonlinear models and locally optimal designs as well as minimax, optimal on average, and Bayesian designs. The authors also discuss ada
Optimal Decision Making in Neural Inhibition Models
van Ravenzwaaij, Don; van der Maas, Han L. J.; Wagenmakers, Eric-Jan
2012-01-01
In their influential "Psychological Review" article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the feedforward inhibition model (FFI), can mimic the…
Optimal Hedging with the Vector Autoregressive Model
L. Gatarek (Lukasz); S.G. Johansen (Soren)
2014-01-01
markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be
Thermoelectric heat sink modeling and optimization
International Nuclear Information System (INIS)
Buist, R.J.; Nagy, M.J.
1994-01-01
Proper design and optimization of a thermoelectric heat sinks has been neglected somewhat in the design of the thermoelectric cooling systems. TE Technology, Inc. has developed a model over a period of 30 hears. The use and application of this model through optimizing heat sink performance is presented
The linear utility model for optimal selection
Mellenbergh, Gideon J.; van der Linden, Willem J.
A linear utility model is introduced for optimal selection when several subpopulations of applicants are to be distinguished. Using this model, procedures are described for obtaining optimal cutting scores in subpopulations in quota-free as well as quota-restricted selection situations. The cutting
Optimization in engineering models and algorithms
Sioshansi, Ramteen
2017-01-01
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...
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
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 decision making in neural inhibition models
van Ravenzwaaij, D.; van der Maas, H.L.J.; Wagenmakers, E.-J.
2012-01-01
In their influential Psychological Review article, Bogacz, Brown, Moehlis, Holmes, and Cohen (2006) discussed optimal decision making as accomplished by the drift diffusion model (DDM). The authors showed that neural inhibition models, such as the leaky competing accumulator model (LCA) and the
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)
Portfolio optimization with mean-variance model
Hoe, Lam Weng; Siew, Lam Weng
2016-06-01
Investors wish to achieve the target rate of return at the minimum level of risk in their investment. Portfolio optimization is an investment strategy that can be used to minimize the portfolio risk and can achieve the target rate of return. The mean-variance model has been proposed in portfolio optimization. The mean-variance model is an optimization model that aims to minimize the portfolio risk which is the portfolio variance. The objective of this study is to construct the optimal portfolio using the mean-variance model. The data of this study consists of weekly returns of 20 component stocks of FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI). The results of this study show that the portfolio composition of the stocks is different. Moreover, investors can get the return at minimum level of risk with the constructed optimal mean-variance portfolio.
Handbook on modelling for discrete optimization
Pitsoulis, Leonidas; Williams, H
2006-01-01
The primary objective underlying the Handbook on Modelling for Discrete Optimization is to demonstrate and detail the pervasive nature of Discrete Optimization. While its applications cut across an incredibly wide range of activities, many of the applications are only known to specialists. It is the aim of this handbook to correct this. It has long been recognized that "modelling" is a critically important mathematical activity in designing algorithms for solving these discrete optimization problems. Nevertheless solving the resultant models is also often far from straightforward. In recent years it has become possible to solve many large-scale discrete optimization problems. However, some problems remain a challenge, even though advances in mathematical methods, hardware, and software technology have pushed the frontiers forward. This handbook couples the difficult, critical-thinking aspects of mathematical modeling with the hot area of discrete optimization. It will be done in an academic handbook treatment...
Surrogate Modeling for Geometry Optimization
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas; Holzwarth, Natalie
2009-01-01
A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used.......A new approach for optimizing the nuclear geometry of an atomic system is described. Instead of the original expensive objective function (energy functional), a small number of simpler surrogates is used....
Bioprocess optimization under uncertainty using ensemble modeling.
Liu, Yang; Gunawan, Rudiyanto
2017-02-20
The performance of model-based bioprocess optimizations depends on the accuracy of the mathematical model. However, models of bioprocesses often have large uncertainty due to the lack of model identifiability. In the presence of such uncertainty, process optimizations that rely on the predictions of a single "best fit" model, e.g. the model resulting from a maximum likelihood parameter estimation using the available process data, may perform poorly in real life. In this study, we employed ensemble modeling to account for model uncertainty in bioprocess optimization. More specifically, we adopted a Bayesian approach to define the posterior distribution of the model parameters, based on which we generated an ensemble of model parameters using a uniformly distributed sampling of the parameter confidence region. The ensemble-based process optimization involved maximizing the lower confidence bound of the desired bioprocess objective (e.g. yield or product titer), using a mean-standard deviation utility function. We demonstrated the performance and robustness of the proposed strategy in an application to a monoclonal antibody batch production by mammalian hybridoma cell culture. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.
A Model of Optimal Development
Prabir C. Bhattacharya
2015-01-01
The paper addresses the question of optimal development of a developing economy. The framework presented, it is believed, can be of help in thinking about policies relating, inter alia, to population growth, inter-sectoral migration, agriculture-industry relationship, wages in different sectors, and income distribution in an inter-connected way in the context of optimal development of an economy with an informal sector.
Mathematical modeling and optimization of complex structures
Repin, Sergey; Tuovinen, Tero
2016-01-01
This volume contains selected papers in three closely related areas: mathematical modeling in mechanics, numerical analysis, and optimization methods. The papers are based upon talks presented on the International Conference for Mathematical Modeling and Optimization in Mechanics, held in Jyväskylä, Finland, March 6-7, 2014 dedicated to Prof. N. Banichuk on the occasion of his 70th birthday. The articles are written by well-known scientists working in computational mechanics and in optimization of complicated technical models. Also, the volume contains papers discussing the historical development, the state of the art, new ideas, and open problems arising in modern continuum mechanics and applied optimization problems. Several papers are concerned with mathematical problems in numerical analysis, which are also closely related to important mechanical models. The main topics treated include: * Computer simulation methods in mechanics, physics, and biology; * Variational problems and methods; minimiz...
Benchmarking model-free and model-based optimal control
Koryakovskiy, I.; Kudruss, M.; Babuska, R.; Caarls, W.; Kirches, Christian; Mombaur, Katja; Schlöder, Johannes P.; Vallery, H.
2017-01-01
Model-free reinforcement learning and nonlinear model predictive control are two different approaches for controlling a dynamic system in an optimal way according to a prescribed cost function. Reinforcement learning acquires a control policy through exploratory interaction with the system, while
Parameter optimization for surface flux transport models
Whitbread, T.; Yeates, A. R.; Muñoz-Jaramillo, A.; Petrie, G. J. D.
2017-11-01
Accurate prediction of solar activity calls for precise calibration of solar cycle models. Consequently we aim to find optimal parameters for models which describe the physical processes on the solar surface, which in turn act as proxies for what occurs in the interior and provide source terms for coronal models. We use a genetic algorithm to optimize surface flux transport models using National Solar Observatory (NSO) magnetogram data for Solar Cycle 23. This is applied to both a 1D model that inserts new magnetic flux in the form of idealized bipolar magnetic regions, and also to a 2D model that assimilates specific shapes of real active regions. The genetic algorithm searches for parameter sets (meridional flow speed and profile, supergranular diffusivity, initial magnetic field, and radial decay time) that produce the best fit between observed and simulated butterfly diagrams, weighted by a latitude-dependent error structure which reflects uncertainty in observations. Due to the easily adaptable nature of the 2D model, the optimization process is repeated for Cycles 21, 22, and 24 in order to analyse cycle-to-cycle variation of the optimal solution. We find that the ranges and optimal solutions for the various regimes are in reasonable agreement with results from the literature, both theoretical and observational. The optimal meridional flow profiles for each regime are almost entirely within observational bounds determined by magnetic feature tracking, with the 2D model being able to accommodate the mean observed profile more successfully. Differences between models appear to be important in deciding values for the diffusive and decay terms. In like fashion, differences in the behaviours of different solar cycles lead to contrasts in parameters defining the meridional flow and initial field strength.
Regularizing and Optimizing LSTM Language Models
Merity, Stephen; Keskar, Nitish Shirish; Socher, Richard
2017-01-01
Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs), serve as a fundamental building block for many sequence learning tasks, including machine translation, language modeling, and question answering. In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models. We propose the weight-dropped LSTM which uses DropConnect on hidden-to-hidden weights as a form of recurrent r...
Modeling and optimization of LCD optical performance
Yakovlev, Dmitry A; Kwok, Hoi-Sing
2015-01-01
The aim of this book is to present the theoretical foundations of modeling the optical characteristics of liquid crystal displays, critically reviewing modern modeling methods and examining areas of applicability. The modern matrix formalisms of optics of anisotropic stratified media, most convenient for solving problems of numerical modeling and optimization of LCD, will be considered in detail. The benefits of combined use of the matrix methods will be shown, which generally provides the best compromise between physical adequacy and accuracy with computational efficiency and optimization fac
Enhanced index tracking modelling in portfolio optimization
Lam, W. S.; Hj. Jaaman, Saiful Hafizah; Ismail, Hamizun bin
2013-09-01
Enhanced index tracking is a popular form of passive fund management in stock market. It is a dual-objective optimization problem, a trade-off between maximizing the mean return and minimizing the risk. Enhanced index tracking aims to generate excess return over the return achieved by the index without purchasing all of the stocks that make up the index by establishing an optimal portfolio. The objective of this study is to determine the optimal portfolio composition and performance by using weighted model in enhanced index tracking. Weighted model focuses on the trade-off between the excess return and the risk. The results of this study show that the optimal portfolio for the weighted model is able to outperform the Malaysia market index which is Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.
Dynamic optimization deterministic and stochastic models
Hinderer, Karl; Stieglitz, Michael
2016-01-01
This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
Optimization Models for Petroleum Field Exploitation
Energy Technology Data Exchange (ETDEWEB)
Jonsbraaten, Tore Wiig
1998-12-31
This thesis presents and discusses various models for optimal development of a petroleum field. The objective of these optimization models is to maximize, under many uncertain parameters, the project`s expected net present value. First, an overview of petroleum field optimization is given from the point of view of operations research. Reservoir equations for a simple reservoir system are derived and discretized and included in optimization models. Linear programming models for optimizing production decisions are discussed and extended to mixed integer programming models where decisions concerning platform, wells and production strategy are optimized. Then, optimal development decisions under uncertain oil prices are discussed. The uncertain oil price is estimated by a finite set of price scenarios with associated probabilities. The problem is one of stochastic mixed integer programming, and the solution approach is to use a scenario and policy aggregation technique developed by Rockafellar and Wets although this technique was developed for continuous variables. Stochastic optimization problems with focus on problems with decision dependent information discoveries are also discussed. A class of ``manageable`` problems is identified and an implicit enumeration algorithm for finding optimal decision policy is proposed. Problems involving uncertain reservoir properties but with a known initial probability distribution over possible reservoir realizations are discussed. Finally, a section on Nash-equilibrium and bargaining in an oil reservoir management game discusses the pool problem arising when two lease owners have access to the same underlying oil reservoir. Because the oil tends to migrate, both lease owners have incentive to drain oil from the competitors part of the reservoir. The discussion is based on a numerical example. 107 refs., 31 figs., 14 tabs.
Modeling, simulation and optimization of bipedal walking
Berns, Karsten
2013-01-01
The model-based investigation of motions of anthropomorphic systems is an important interdisciplinary research topic involving specialists from many fields such as Robotics, Biomechanics, Physiology, Orthopedics, Psychology, Neurosciences, Sports, Computer Graphics and Applied Mathematics. This book presents a study of basic locomotion forms such as walking and running is of particular interest due to the high demand on dynamic coordination, actuator efficiency and balance control. Mathematical models and numerical simulation and optimization techniques are explained, in combination with experimental data, which can help to better understand the basic underlying mechanisms of these motions and to improve them. Example topics treated in this book are Modeling techniques for anthropomorphic bipedal walking systems Optimized walking motions for different objective functions Identification of objective functions from measurements Simulation and optimization approaches for humanoid robots Biologically inspired con...
MODELING AND OPTIMIZATION OF THE AEROCONCRETE TECHNOLOGY
Directory of Open Access Journals (Sweden)
Zhukov Aleksey Dmitrievich
2012-07-01
Selection of the appropriate composition and optimal technological parameters is performed with the help of G-BAT-2011 software programme developed at MSUCE. The software is based on the methodology that is based on complete factorial experiments, experiments based on fractional replicates and testing of all essential statistical hypotheses. Linear, incomplete quadratic and quadratic equations generated as a result of experiments make it possible to design a model that represents natural processes in the adequate manner. The model is analytically optimized and interpreted thereafter.
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
Mathematical model of highways network optimization
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.
Applied probability models with optimization applications
Ross, Sheldon M
1992-01-01
Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.
Optimal information diffusion in stochastic block models.
Curato, Gianbiagio; Lillo, Fabrizio
2016-09-01
We use the linear threshold model to study the diffusion of information on a network generated by the stochastic block model. We focus our analysis on a two-community structure where the initial set of informed nodes lies only in one of the two communities and we look for optimal network structures, i.e., those maximizing the asymptotic extent of the diffusion. We find that, constraining the mean degree and the fraction of initially informed nodes, the optimal structure can be assortative (modular), core-periphery, or even disassortative. We then look for minimal cost structures, i.e., those for which a minimal fraction of initially informed nodes is needed to trigger a global cascade. We find that the optimal networks are assortative but with a structure very close to a core-periphery graph, i.e., a very dense community linked to a much more sparsely connected periphery.
Optimal inventory management and order book modeling
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.
Fuzzy Stochastic Optimization Theory, Models and Applications
Wang, Shuming
2012-01-01
Covering in detail both theoretical and practical perspectives, this book is a self-contained and systematic depiction of current fuzzy stochastic optimization that deploys the fuzzy random variable as a core mathematical tool to model the integrated fuzzy random uncertainty. It proceeds in an orderly fashion from the requisite theoretical aspects of the fuzzy random variable to fuzzy stochastic optimization models and their real-life case studies. The volume reflects the fact that randomness and fuzziness (or vagueness) are two major sources of uncertainty in the real world, with significant implications in a number of settings. In industrial engineering, management and economics, the chances are high that decision makers will be confronted with information that is simultaneously probabilistically uncertain and fuzzily imprecise, and optimization in the form of a decision must be made in an environment that is doubly uncertain, characterized by a co-occurrence of randomness and fuzziness. This book begins...
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
MARKOV CHAIN PORTFOLIO LIQUIDITY OPTIMIZATION MODEL
Directory of Open Access Journals (Sweden)
Eder Oliveira Abensur
2014-05-01
Full Text Available The international financial crisis of September 2008 and May 2010 showed the importance of liquidity as an attribute to be considered in portfolio decisions. This study proposes an optimization model based on available public data, using Markov chain and Genetic Algorithms concepts as it considers the classic duality of risk versus return and incorporating liquidity costs. The work intends to propose a multi-criterion non-linear optimization model using liquidity based on a Markov chain. The non-linear model was tested using Genetic Algorithms with twenty five Brazilian stocks from 2007 to 2009. The results suggest that this is an innovative development methodology and useful for developing an efficient and realistic financial portfolio, as it considers many attributes such as risk, return and liquidity.
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.
Efficient Iris Localization via Optimization Model
Directory of Open Access Journals (Sweden)
Qi Wang
2017-01-01
Full Text Available Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.
Space Mapping Optimization of Microwave Circuits Exploiting Surrogate Models
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
A powerful new space-mapping (SM) optimization algorithm is presented in this paper. It draws upon recent developments in both surrogate model-based optimization and modeling of microwave devices, SM optimization is formulated as a general optimization problem of a surrogate model. This model...
Modeling and Optimization : Theory and Applications Conference
Terlaky, Tamás
2015-01-01
This volume contains a selection of contributions that were presented at the Modeling and Optimization: Theory and Applications Conference (MOPTA) held at Lehigh University in Bethlehem, Pennsylvania, USA on August 13-15, 2014. The conference brought together a diverse group of researchers and practitioners, working on both theoretical and practical aspects of continuous or discrete optimization. Topics presented included algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization problems, and addressed the application of deterministic and stochastic optimization techniques in energy, finance, logistics, analytics, healthcare, and other important fields. The contributions contained in this volume represent a sample of these topics and applications and illustrate the broad diversity of ideas discussed at the meeting.
Modeling and Optimization : Theory and Applications Conference
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.
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.
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
Optimization and mathematical modeling in computer architecture
Sankaralingam, Karu; Nowatzki, Tony
2013-01-01
In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms t
Modeling and optimization of a chiller plant
International Nuclear Information System (INIS)
Wei, Xiupeng; Xu, Guanglin; Kusiak, Andrew
2014-01-01
A data-driven approach is utilized to model a chiller plant that has four chillers, four cooling towers, and two chilled water storage tanks. The chillers have varying energy efficiency. Since the chiller plant model derived from data-driven approach is nonlinear and non-convex, it is not practical to solve it by using the traditional gradient-based optimization algorithm. A two-level intelligent algorithm is developed to solve the model aiming at minimizing the total cost of the chilled water plant. The proposed algorithm can effectively search the optimum under the non-convex and nonlinear situation. A simulation case is conducted and the corresponding results are discussed. - Highlights: • Development of a data-driven based model of a complete chiller plant. • A two-level intelligent algorithm is proposed to optimize the chiller plant which is non-convex and nonlinear problem. • A simulation is conducted to verify the performance of the model and algorithm. • 14 percent of energy saving can be achieved with proposed method
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... systems. To have better understanding of water leakage, to control pressure and leakage effectively and for optimal design of water supply system, suitable modeling is an important prerequisite. Therefore a model with the main objective of pressure control and consequently leakage reduction is presented...
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.
Combinatorial Optimization in Forest Ecosystem Management Modeling
BAŞKENT, Emin Zeki
2001-01-01
Modeling forest management activities has been tackled by scientists over the last two decades. Both simulation and optimization techniques have been used in solving forest management planning problems. With the introduction of ecosystems management that focuses on the sustainable production and maintenance of ecological, social and economical values, neither approach provided a credible solution technique to help design the complex structure of forest management activities. Alternative to th...
Optimization of hybrid model on hajj travel
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.
Combined optimization model for sustainable energization strategy
Abtew, Mohammed Seid
Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.
Space Mapping Optimization of Microwave Circuits Exploiting Surrogate Models
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
A powerful new space-mapping (SM) optimization algorithm is presented in this paper. It draws upon recent developments in both surrogate model-based optimization and modeling of microwave devices, SM optimization is formulated as a general optimization problem of a surrogate model. This model...... is a convex combination of a mapped coarse model and a linearized fine model. It exploits, in a novel way, a linear frequency-sensitive mapping. During the optimization iterates, the coarse and fine models are simulated at different sets of frequencies. This approach is shown to be especially powerful...
Aerodynamic Modelling and Optimization of Axial Fans
DEFF Research Database (Denmark)
Sørensen, Dan Nørtoft
A numerically efficient mathematical model for the aerodynamics oflow speed axial fans of the arbitrary vortex flow type has been developed.The model is based on a blade-element principle, whereby therotor is divided into a number of annular streamtubes.For each of these streamtubes relations...... and integrated propertiesshow that the computed results agree well with the measurements.Integrating a rotor-only version of the aerodynamic modelwith an algorithm for numerical designoptimization, enables the finding of an optimum fan rotor.The angular velocity of the rotor, the hub radius and the spanwise...... of fan efficiency in a design interval of flow rates,thus designinga fan which operates well over a range of different flow conditions.The optimization scheme was used to investigate the dependence ofmaximum efficiency on1: the number of blades,2: the width of the design interval and3: the hub radius...
GPCR Homology Model Generation for Lead Optimization.
Tautermann, Christofer S
2018-01-01
The vast increase of recently solved GPCR X-ray structures forms the basis for GPCR homology modeling to atomistic accuracy. Nowadays, homology models can be employed for GPCR-ligand optimization and have been reported as invaluable tools for drug design in the last few years. Elucidation of the complex GPCR pharmacology and the associated GPCR conformations made clear that different homology models have to be constructed for different activation states of the GPCRs. Therefore, templates have to be chosen accordingly to their sequence homology as well as to their activation state. The subsequent ligand placement is nontrivial, as some recent X-ray structures show very unusual ligand binding sites and solvent involvement, expanding the space of the putative ligand binding site from the generic retinal binding pocket to the whole receptor. In the present study, a workflow is presented starting from the selection of the target sequence, guiding through the GPCR modeling process, and finishing with ligand placement and pose validation.
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
Application of Scheffe's Model in Optimization of Compressive cube ...
African Journals Online (AJOL)
experimental method using Scheffe's simplex model in optimizing the compressive strength of river stone aggregate concrete. The model was used to optimize the compressive strength of concrete made from river stone aggregate, river sand, cement, ...
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)
Incorporating model uncertainty into optimal insurance contract design
Pflug, G.; Timonina-Farkas, A.; Hochrainer-Stigler, S.
2017-01-01
In stochastic optimization models, the optimal solution heavily depends on the selected probability model for the scenarios. However, the scenario models are typically chosen on the basis of statistical estimates and are therefore subject to model error. We demonstrate here how the model uncertainty can be incorporated into the decision making process. We use a nonparametric approach for quantifying the model uncertainty and a minimax setup to find model-robust solutions. The method is illust...
Modeling and optimization of wet sizing process
International Nuclear Information System (INIS)
Thai Ba Cau; Vu Thanh Quang and Nguyen Ba Tien
2004-01-01
Mathematical simulation on basis of Stock law has been done for wet sizing process on cylinder equipment of laboratory and semi-industrial scale. The model consists of mathematical equations describing relations between variables, such as: - Resident time distribution function of emulsion particles in the separating zone of the equipment depending on flow-rate, height, diameter and structure of the equipment. - Size-distribution function in the fine and coarse parts depending on resident time distribution function of emulsion particles, characteristics of the material being processed, such as specific density, shapes, and characteristics of the environment of classification, such as specific density, viscosity. - Experimental model was developed on data collected from an experimental cylindrical equipment with diameter x height of sedimentation chamber equal to 50 x 40 cm for an emulsion of zirconium silicate in water. - Using this experimental model allows to determine optimal flow-rate in order to obtain product with desired grain size in term of average size or size distribution function. (author)
Kanban simulation model for production process optimization
Directory of Open Access Journals (Sweden)
Golchev Riste
2015-01-01
Full Text Available A long time has passed since the KANBAN system has been established as an efficient method for coping with the excessive inventory. Still, the possibilities for its improvement through its integration with other different approaches should be investigated further. The basic research challenge of this paper is to present benefits of KANBAN implementation supported with Discrete Event Simulation (DES. In that direction, at the beginning, the basics of KANBAN system are presented with emphasis on the information and material flow, together with a methodology for implementation of KANBAN system. Certain analysis on combining the simulation with this methodology is presented. The paper is concluded with a practical example which shows that through understanding the philosophy of the implementation methodology of KANBAN system and the simulation methodology, a simulation model can be created which can serve as a basis for a variety of experiments that can be conducted within a short period of time, resulting with production process optimization.
Multiobjective Optimization Model for Wind Power Allocation
Directory of Open Access Journals (Sweden)
Juan Alemany
2017-01-01
Full Text Available There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented ε-constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process.
An optimization model for improving highway safety
Directory of Open Access Journals (Sweden)
Promothes Saha
2016-12-01
Full Text Available This paper developed a traffic safety management system (TSMS for improving safety on county paved roads in Wyoming. TSMS is a strategic and systematic process to improve safety of roadway network. When funding is limited, it is important to identify the best combination of safety improvement projects to provide the most benefits to society in terms of crash reduction. The factors included in the proposed optimization model are annual safety budget, roadway inventory, roadway functional classification, historical crashes, safety improvement countermeasures, cost and crash reduction factors (CRFs associated with safety improvement countermeasures, and average daily traffics (ADTs. This paper demonstrated how the proposed model can identify the best combination of safety improvement projects to maximize the safety benefits in terms of reducing overall crash frequency. Although the proposed methodology was implemented on the county paved road network of Wyoming, it could be easily modified for potential implementation on the Wyoming state highway system. Other states can also benefit by implementing a similar program within their jurisdictions.
Determining Reduced Order Models for Optimal Stochastic Reduced Order Models
Energy Technology Data Exchange (ETDEWEB)
Bonney, Matthew S. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Brake, Matthew R.W. [Sandia National Lab. (SNL-CA), Livermore, CA (United States)
2015-08-01
The use of parameterized reduced order models(PROMs) within the stochastic reduced order model (SROM) framework is a logical progression for both methods. In this report, five different parameterized reduced order models are selected and critiqued against the other models along with truth model for the example of the Brake-Reuss beam. The models are: a Taylor series using finite difference, a proper orthogonal decomposition of the the output, a Craig-Bampton representation of the model, a method that uses Hyper-Dual numbers to determine the sensitivities, and a Meta-Model method that uses the Hyper-Dual results and constructs a polynomial curve to better represent the output data. The methods are compared against a parameter sweep and a distribution propagation where the first four statistical moments are used as a comparison. Each method produces very accurate results with the Craig-Bampton reduction having the least accurate results. The models are also compared based on time requirements for the evaluation of each model where the Meta- Model requires the least amount of time for computation by a significant amount. Each of the five models provided accurate results in a reasonable time frame. The determination of which model to use is dependent on the availability of the high-fidelity model and how many evaluations can be performed. Analysis of the output distribution is examined by using a large Monte-Carlo simulation along with a reduced simulation using Latin Hypercube and the stochastic reduced order model sampling technique. Both techniques produced accurate results. The stochastic reduced order modeling technique produced less error when compared to an exhaustive sampling for the majority of methods.
Markowitz portfolio optimization model employing fuzzy measure
Ramli, Suhailywati; Jaaman, Saiful Hafizah
2017-04-01
Markowitz in 1952 introduced the mean-variance methodology for the portfolio selection problems. His pioneering research has shaped the portfolio risk-return model and become one of the most important research fields in modern finance. This paper extends the classical Markowitz's mean-variance portfolio selection model applying the fuzzy measure to determine the risk and return. In this paper, we apply the original mean-variance model as a benchmark, fuzzy mean-variance model with fuzzy return and the model with return are modeled by specific types of fuzzy number for comparison. The model with fuzzy approach gives better performance as compared to the mean-variance approach. The numerical examples are included to illustrate these models by employing Malaysian share market data.
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.
Ion source mathematical modeling and optimization
International Nuclear Information System (INIS)
Egorov, N.V.; Vinogradova, E.M.
2004-01-01
Full text: The system of beam formation and control in the ion gun is under investigation. The calculation of the ion gun must take into account the field ion cathode influence on the beam focusing and transport conditions and the other electrodes influence both on the field cathode emission ability and on the characteristics of the formation and control systems. It's considered a mathematical model of the gun as a axially symmetrical ion-optical system which consists of a cathode, i.e. axially symmetrical thin tip on a flat substrate and a system of round apertures as the focusing electrodes. The tip shape may be various. The number of the apertures may be various too. The potential of the tip is equal to the substrate potential and is assumed to be zero without the loss of general character of the problem. A method is proposed for the determination the potential distribution. lt is calculated the distribution of potentials for whole region of the ion-optical system. All geometrical dimensions of the system and the electrodes' potentials are the parameters of this method. The problem of the optimal geometrical parameters and electrodes potentials is solved to have the required emission current. (author)
Response Surface Model Building and Multidisciplinary Optimization Using D-Optimal Designs
Unal, Resit; Lepsch, Roger A.; McMillin, Mark L.
1998-01-01
This paper discusses response surface methods for approximation model building and multidisciplinary design optimization. The response surface methods discussed are central composite designs, Bayesian methods and D-optimal designs. An over-determined D-optimal design is applied to a configuration design and optimization study of a wing-body, launch vehicle. Results suggest that over determined D-optimal designs may provide an efficient approach for approximation model building and for multidisciplinary design optimization.
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...... and the backingplate by solving an inverse modelling problem in which experimental data and a numerical model are used for determining the contact heat transfer coefficient. Different parametrizations of the spatial distribution of the heat transfer coefficient are studied and discussed, and the optimization problem...
Visual prosthesis wireless energy transfer system optimal modeling.
Li, Xueping; Yang, Yuan; Gao, Yong
2014-01-16
Wireless energy transfer system is an effective way to solve the visual prosthesis energy supply problems, theoretical modeling of the system is the prerequisite to do optimal energy transfer system design. On the basis of the ideal model of the wireless energy transfer system, according to visual prosthesis application condition, the system modeling is optimized. During the optimal modeling, taking planar spiral coils as the coupling devices between energy transmitter and receiver, the effect of the parasitic capacitance of the transfer coil is considered, and especially the concept of biological capacitance is proposed to consider the influence of biological tissue on the energy transfer efficiency, resulting in the optimal modeling's more accuracy for the actual application. The simulation data of the optimal model in this paper is compared with that of the previous ideal model, the results show that under high frequency condition, the parasitic capacitance of inductance and biological capacitance considered in the optimal model could have great impact on the wireless energy transfer system. The further comparison with the experimental data verifies the validity and accuracy of the optimal model proposed in this paper. The optimal model proposed in this paper has a higher theoretical guiding significance for the wireless energy transfer system's further research, and provide a more precise model reference for solving the power supply problem in visual prosthesis clinical application.
Aerial Search Optimization Model (ASOM) for UAVs in Special Operations
National Research Council Canada - National Science Library
Kress, Moshe; Royset, Johannes O
2007-01-01
.... The goal is to detect the largest possible number of targets with the given resources. The model prescribes optimal deployment locations for the ground units and optimal time-phased search areas for the UAVs...
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...
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.
Optimal experimental designs for inverse quadratic regression models
Dette, Holger; Kiss, Christine
2007-01-01
In this paper optimal experimental designs for inverse quadratic regression models are determined. We consider two different parameterizations of the model and investigate local optimal designs with respect to the $c$-, $D$- and $E$-criteria, which reflect various aspects of the precision of the maximum likelihood estimator for the parameters in inverse quadratic regression models. In particular it is demonstrated that for a sufficiently large design space geometric allocation rules are optim...
Integrated modeling of ozonation for optimization of drinking water treatment
van der Helm, A.W.C.
2007-01-01
Drinking water treatment plants automation becomes more sophisticated, more on-line monitoring systems become available and integration of modeling environments with control systems becomes easier. This gives possibilities for model-based optimization. In operation of drinking water treatment
RF building block modeling: optimization and synthesis
Cheng, W.
2012-01-01
For circuit designers it is desirable to have relatively simple RF circuit models that do give decent estimation accuracy and provide sufficient understanding of circuits. Chapter 2 in this thesis shows a general weak nonlinearity model that meets these demands. Using a method that is related to
Modelling in Optimal Inspection and Repair
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Rackwitz, R.; Faber, Michael Havbro
1991-01-01
of this model with a preposterior analysis from statistical decision theory is discussed. It is deseribed how information obtained by an inspection can be used in the repair decision process and to update the inspection plan. Stochastic models for inspection, measurement and repair actions are presented...
Optimal model distributions in supervisory adaptive control
Ghosh, D.; Baldi, S.
2017-01-01
Several classes of multi-model adaptive control schemes have been proposed in literature: instead of one single parameter-varying controller, in this adaptive methodology multiple fixed-parameter controllers for different operating regimes (i.e. different models) are utilised. Despite advances in
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...
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.
Optimization and modeling of cellulase protein from Trichoderma ...
African Journals Online (AJOL)
Research surface methodology was suggested for optimization of process conditions of cellulase biosynthesis. Logistic kinetic model was the best model for the mixed substrates. A conceptual Artificial Neural Network (ANN) model was well incorporated in the fermentative production of cellulase. By adopting these models ...
Optimal Scaling of Interaction Effects in Generalized Linear Models
van Rosmalen, Joost; Koning, Alex J.; Groenen, Patrick J. F.
2009-01-01
Multiplicative interaction models, such as Goodman's (1981) RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are suitable only for data sets with two or three predictor variables. Here, we discuss an optimal scaling model for analyzing the content of…
On Optimal Input Design and Model Selection for Communication Channels
Energy Technology Data Exchange (ETDEWEB)
Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Optimization models in a transition economy
Sergienko, Ivan V; Koshlai, Ludmilla
2014-01-01
This book opens new avenues in understanding mathematical models within the context of a transition economy. The exposition lays out the methods for combining different mathematical structures and tools to effectively build the next model that will accurately reflect real world economic processes. Mathematical modeling of weather phenomena allows us to forecast certain essential weather parameters without any possibility of changing them. By contrast, modeling of transition economies gives us the freedom to not only predict changes in important indexes of all types of economies, but also to influence them more effectively in the desired direction. Simply put: any economy, including a transitional one, can be controlled. This book is useful to anyone who wants to increase profits within their business, or improve the quality of their family life and the economic area they live in. It is beneficial for undergraduate and graduate students specializing in the fields of Economic Informatics, Economic Cybernetic...
Optimizing mouse models for precision cancer prevention.
Le Magnen, Clémentine; Dutta, Aditya; Abate-Shen, Cory
2016-03-01
As cancer has become increasingly prevalent, cancer prevention research has evolved towards placing a greater emphasis on reducing cancer deaths and minimizing the adverse consequences of having cancer. 'Precision cancer prevention' takes into account the collaboration of intrinsic and extrinsic factors in influencing cancer incidence and aggressiveness in the context of the individual, as well as recognizing that such knowledge can improve early detection and enable more accurate discrimination of cancerous lesions. However, mouse models, and particularly genetically engineered mouse (GEM) models, have yet to be fully integrated into prevention research. In this Opinion article, we discuss opportunities and challenges for precision mouse modelling, including the essential criteria of mouse models for prevention research, representative success stories and opportunities for more refined analyses in future studies.
Optimization of experimental human leukemia models (review
Directory of Open Access Journals (Sweden)
D. D. Pankov
2012-01-01
Full Text Available Actual problem of assessing immunotherapy prospects including antigenpecific cell therapy using animal models was covered in this review.Describe the various groups of currently existing animal models and methods of their creating – from different immunodeficient mice to severalvariants of tumor cells engraftment in them. The review addresses the possibility of tumor stem cells studying using mouse models for the leukemia treatment with adoptive cell therapy including WT1. Also issues of human leukemia cells migration and proliferation in a mice withdifferent immunodeficiency degree are discussed. To assess the potential immunotherapy efficacy comparison of immunodeficient mouse model with clinical situation in oncology patients after chemotherapy is proposed.
Optimization using surrogate models - by the space mapping technique
DEFF Research Database (Denmark)
Søndergaard, Jacob
2003-01-01
Surrogate modelling and optimization techniques are intended for engineering design in the case where an expensive physical model is involved. This thesis provides a literature overview of the field of surrogate modelling and optimization. The space mapping technique is one such method for constr......Surrogate modelling and optimization techniques are intended for engineering design in the case where an expensive physical model is involved. This thesis provides a literature overview of the field of surrogate modelling and optimization. The space mapping technique is one such method...... conditions are satisfied. So hybrid methods, combining the space mapping technique with classical optimization methods, should be used if convergence to high accuracy is wanted. Approximation abilities of the space mapping surrogate are compared with those of a Taylor model of the expensive model. The space...... mapping surrogate has a lower approximation error for long steps. For short steps, however, the Taylor model of the expensive model is best, due to exact interpolation at the model origin. Five algorithms for space mapping optimization are presented and the numerical performance is evaluated. Three...
Structural robust optimization design based on convex model
Directory of Open Access Journals (Sweden)
Xuyong Chen
Full Text Available There exist a great amount of uncertain factors in actual engineering. In order to involve these uncertain factors in analytical model, they have been expressed as the convex variables. In addition, the convex model was further classified into the hyper-ellipsoidal model and the interval model. After pointing out the intrinsic difference between these two kinds of models, the principle for applying which one of the models within the analysis has been indicated according to the available testing points. After standardizing the convex variables, the difference and relation between these two models for the optimization and solution process have been presented. With the analysis mentality available from the hyper-ellipsoidal model, the basic method about the robust optimization for the interval model was emphasized. After classification of the interval variables within the optimization process, the characteristics of the robust optimization were highlighted with different constraint conditions. Using the target-performance-based analytical scheme, the algorithm, the solution step and the convergence criteria for the robust optimization have been also presented with only one reliability index. Numerical examples and engineering problems were used to demonstrate the effectiveness and correctness of the proposed approach. Keywords: Robust optimization, Non-probabilistic reliability, Interval model, Hyper-ellipsoidal model, Probabilistic index
Asymptotic Optimality of Estimating Function Estimator for CHARN Model
Directory of Open Access Journals (Sweden)
Tomoyuki Amano
2012-01-01
Full Text Available CHARN model is a famous and important model in the finance, which includes many financial time series models and can be assumed as the return processes of assets. One of the most fundamental estimators for financial time series models is the conditional least squares (CL estimator. However, recently, it was shown that the optimal estimating function estimator (G estimator is better than CL estimator for some time series models in the sense of efficiency. In this paper, we examine efficiencies of CL and G estimators for CHARN model and derive the condition that G estimator is asymptotically optimal.
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.
Algorithms for Optimal Model Distributions in Adaptive Switching Control Schemes
Directory of Open Access Journals (Sweden)
Debarghya Ghosh
2016-03-01
Full Text Available Several multiple model adaptive control architectures have been proposed in the literature. Despite many advances in theory, the crucial question of how to synthesize the pairs model/controller in a structurally optimal way is to a large extent not addressed. In particular, it is not clear how to place the pairs model/controller is such a way that the properties of the switching algorithm (e.g., number of switches, learning transient, final performance are optimal with respect to some criteria. In this work, we focus on the so-called multi-model unfalsified adaptive supervisory switching control (MUASSC scheme; we define a suitable structural optimality criterion and develop algorithms for synthesizing the pairs model/controller in such a way that they are optimal with respect to the structural optimality criterion we defined. The peculiarity of the proposed optimality criterion and algorithms is that the optimization is carried out so as to optimize the entire behavior of the adaptive algorithm, i.e., both the learning transient and the steady-state response. A comparison is made with respect to the model distribution of the robust multiple model adaptive control (RMMAC, where the optimization considers only the steady-state ideal response and neglects any learning transient.
Surrogate Modeling for Geometry Optimization in Material Design
DEFF Research Database (Denmark)
Rojas Larrazabal, Marielba de la Caridad; Abraham, Yonas B.; Holzwarth, Natalie A.W.
2007-01-01
We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)......We propose a new approach based on surrogate modeling for geometry optimization in material design. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)...
Development of Optimized Strength Model of Lateritic Hollow Block ...
African Journals Online (AJOL)
The work is an investigation to develop and optimize a model of the compressive strength of lateritic hollow sandcrete block with mound soil inclusion. The study applies the Scheffe's optimization approach to obtain a mathematical model of the form f(xi1,xi2,xi3,,xi4), where xi are proportions of the concrete components, viz: ...
An Optimization Model Development for Laterized-Concrete Mix ...
African Journals Online (AJOL)
An Optimization Model Development for Laterized-Concrete Mix Proportioning in Building Constructions. ... Nigerian Journal of Technology ... In this study, a mathematical model was developed and was used to optimize the mix proportion that will produce the maximum strength of laterized concrete using Scheffe's simplex ...
In Search of Optimal Cognitive Diagnostic Model(s) for ESL Grammar Test Data
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…
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
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.
Optimal control of a delayed SLBS computer virus model
Chen, Lijuan; Hattaf, Khalid; Sun, Jitao
2015-06-01
In this paper, a delayed SLBS computer virus model is firstly proposed. To the best of our knowledge, this is the first time to discuss the optimal control of the SLBS model. By using the optimal control strategy, we present an optimal strategy to minimize the total number of the breakingout computers and the cost associated with toxication or detoxication. We show that an optimal control solution exists for the control problem. Some examples are presented to show the efficiency of this optimal control.
Airfoil Shape Optimization based on Surrogate Model
Mukesh, R.; Lingadurai, K.; Selvakumar, U.
2018-02-01
Engineering design problems always require enormous amount of real-time experiments and computational simulations in order to assess and ensure the design objectives of the problems subject to various constraints. In most of the cases, the computational resources and time required per simulation are large. In certain cases like sensitivity analysis, design optimisation etc where thousands and millions of simulations have to be carried out, it leads to have a life time of difficulty for designers. Nowadays approximation models, otherwise called as surrogate models (SM), are more widely employed in order to reduce the requirement of computational resources and time in analysing various engineering systems. Various approaches such as Kriging, neural networks, polynomials, Gaussian processes etc are used to construct the approximation models. The primary intention of this work is to employ the k-fold cross validation approach to study and evaluate the influence of various theoretical variogram models on the accuracy of the surrogate model construction. Ordinary Kriging and design of experiments (DOE) approaches are used to construct the SMs by approximating panel and viscous solution algorithms which are primarily used to solve the flow around airfoils and aircraft wings. The method of coupling the SMs with a suitable optimisation scheme to carryout an aerodynamic design optimisation process for airfoil shapes is also discussed.
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
A niche width model of optimal specialization
Bruggeman, J.P.; Ó Nualláin, Breanndán
2000-01-01
Niche width theory, a part of organizational ecology, predicts whether “specialist” or “generalist” forms of organizations have higher “fitness,” in a continually changing environment. To this end, niche width theory uses a mathematical model borrowed from biology. In this paper, we first loosen the
Optimization Model for Reducing Emissions of Greenhouse ...
The EPA Vehicle Greenhouse Gas (VGHG) model is used to apply various technologies to a defined set of vehicles in order to meet a specified GHG emission target, and to then calculate the costs and benefits of doing so. To facilitate its analysis of the costs and benefits of the control of GHG emissions from cars and trucks.
A model for optimal constrained adaptive testing
van der Linden, Willem J.; Reese, Lynda M.
1997-01-01
A model for constrained computerized adaptive testing is proposed in which the information in the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum
Optimal Allocation in Stratified Randomized Response Model
Directory of Open Access Journals (Sweden)
Javid Shabbir
2005-07-01
Full Text Available A Warner (1965 randomized response model based on stratification is used to determine the allocation of samples. Both linear and log-linear cost functions are discussed under uni and double stratification. It observed that by using a log-linear cost function, one can get better allocations.
A model for optimal constrained adaptive testing
van der Linden, Willem J.; Reese, Lynda M.
2001-01-01
A model for constrained computerized adaptive testing is proposed in which the information on the test at the ability estimate is maximized subject to a large variety of possible constraints on the contents of the test. At each item-selection step, a full test is first assembled to have maximum
Optimal Experimental Design for Model Discrimination
Myung, Jay I.; Pitt, Mark A.
2009-01-01
Models of a psychological process can be difficult to discriminate experimentally because it is not easy to determine the values of the critical design variables (e.g., presentation schedule, stimulus structure) that will be most informative in differentiating them. Recent developments in sampling-based search methods in statistics make it…
COBRA-SFS modifications and cask model optimization
International Nuclear Information System (INIS)
Rector, D.R.; Michener, T.E.
1989-01-01
Spent-fuel storage systems are complex systems and developing a computational model for one can be a difficult task. The COBRA-SFS computer code provides many capabilities for modeling the details of these systems, but these capabilities can also allow users to specify a more complex model than necessary. This report provides important guidance to users that dramatically reduces the size of the model while maintaining the accuracy of the calculation. A series of model optimization studies was performed, based on the TN-24P spent-fuel storage cask, to determine the optimal model geometry. Expanded modeling capabilities of the code are also described. These include adding fluid shear stress terms and a detailed plenum model. The mathematical models for each code modification are described, along with the associated verification results. 22 refs., 107 figs., 7 tabs
Constrained optimization with a continuous Hopfield-Lagrange model
J.H. van den Berg (Jan); J.C. Bioch (Cor)
1993-01-01
textabstractIn this paper, a generalized Hopfield model with continuous neurons using Lagrange multipliers, originally introduced Wacholder, Han &Mann [1989], is thoroughly analysed. We have termed the model the Hopfield-Lagrange model. It can be used to resolve constrained optimization problems. In
Pavement maintenance optimization model using Markov Decision Processes
Mandiartha, P.; Duffield, C. F.; Razelan, I. S. b. M.; Ismail, A. b. H.
2017-09-01
This paper presents an optimization model for selection of pavement maintenance intervention using a theory of Markov Decision Processes (MDP). There are some particular characteristics of the MDP developed in this paper which distinguish it from other similar studies or optimization models intended for pavement maintenance policy development. These unique characteristics include a direct inclusion of constraints into the formulation of MDP, the use of an average cost method of MDP, and the policy development process based on the dual linear programming solution. The limited information or discussions that are available on these matters in terms of stochastic based optimization model in road network management motivates this study. This paper uses a data set acquired from road authorities of state of Victoria, Australia, to test the model and recommends steps in the computation of MDP based stochastic optimization model, leading to the development of optimum pavement maintenance policy.
A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models
Wong, Weng Kee; Chen, Ray-Bing; Huang, Chien-Chih; Wang, Weichung
2015-01-01
Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1]. PMID:26091237
Optimal treatment interruptions control of TB transmission model
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.
CREATION OF OPTIMIZATION MODEL OF STEAM BOILER RECUPERATIVE AIR HEATER
Directory of Open Access Journals (Sweden)
N. B. Carnickiy
2006-01-01
Full Text Available The paper proposes to use a mathematical modeling as one of the ways intended to improve quality of recuperative air heater design (RAH without significant additional costs, connected with the change of design materials or fuel type. The described conceptual mathematical AHP optimization model of RAH consists of optimized and constant parameters, technical limitations and optimality criteria.The paper considers a methodology for search of design and regime parameters of an air heater which is based on the methods of multi-criteria optimization. Conclusions for expediency of the given approach usage are made in the paper.
Constrained optimization via simulation models for new product innovation
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.
Empty tracks optimization based on Z-Map model
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.
Modeling and Optimization of Cement Raw Materials Blending Process
Directory of Open Access Journals (Sweden)
Xianhong Li
2012-01-01
Full Text Available This paper focuses on modelling and solving the ingredient ratio optimization problem in cement raw material blending process. A general nonlinear time-varying (G-NLTV model is established for cement raw material blending process via considering chemical composition, feed flow fluctuation, and various craft and production constraints. Different objective functions are presented to acquire optimal ingredient ratios under various production requirements. The ingredient ratio optimization problem is transformed into discrete-time single objective or multiple objectives rolling nonlinear constraint optimization problem. A framework of grid interior point method is presented to solve the rolling nonlinear constraint optimization problem. Based on MATLAB-GUI platform, the corresponding ingredient ratio software is devised to obtain optimal ingredient ratio. Finally, several numerical examples are presented to study and solve ingredient ratio optimization problems.
Model based optimization of MSWC process control
Kessel, L.B.M. van; Leskens, M.
2002-01-01
Optimization of municipal solid waste combustion (MSWC), processes is an im portant issue doe to the ever-lasting need for emission reduction. more optimal use of raw materials and overall cost reduction. The key of the approach of TNO (Netherlands Orgaru sation for Applied Scientific Research) to
The Optimal Economic Order: the simplest model
J. Tinbergen (Jan)
1992-01-01
textabstractIn the last five years humanity has become faced with the problem of the optimal socioeconomic order more clearly than ever. After the confrontation of capitalism and socialism, which was the core of the Marxist thesis, the fact transpired that capitalism was not the optimal order. It
Learning optimal quantum models is NP-hard
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.
Models and Methods for Free Material Optimization
DEFF Research Database (Denmark)
Weldeyesus, Alemseged Gebrehiwot
conditions for physical attainability, in the context that, it has to be symmetric and positive semidefinite. FMO problems have been studied for the last two decades in many articles that led to the development of a wide range of models, methods, and theories. As the design variables in FMO are the local...... programs. The method has successfully obtained solutions to large-scale classical FMO problems of simultaneous analysis and design, nested and dual formulations. The second goal is to extend the method and the FMO problem formulations to general laminated shell structures. The thesis additionally addresses...
Integration and Optimization of Projectile Design Models
National Research Council Canada - National Science Library
Farina, Anthony P; Chassapis, Constantin; Chen, Yin M
2006-01-01
... an existing projectile, will be optimized with respect to performance requirement(s). Additionally, the design process will be simplified by the integration between predictive codes in this environment...
Model optimizing production structure of coal enterprise and its application
Energy Technology Data Exchange (ETDEWEB)
Zhang, Q. [China University of Mining and Technology, Xuzhou (China). College of Business and Adminsitration
2000-05-01
The models which optimize the product structure and maximize the profit of a coal enterprise were established. These models are mainly the increment and total analysis model for determining the optimum coal preparation scheme, the model of management risk analysis, and the linear plan model for deciding the optimum product structure under the condition of a given technology. They have been applied to coal enterprise in practice and obtained obvious economic benefit. This shows that the models are scientifically reasonable and efficient, and are valuable in designing preparation plant and in planning production under the condition of market economy. 2 refs., 2 tabs.
A merchant ship size optimization model
Choi, Ki-Chul
1983-01-01
Approved for public release; distribution in unlimited. This paper analyzes how a shipowner or charterer may determine the specification of optimal ship size for a given route with respect to certain market requirements . The theory of optimal ship size, a methodology for estimating scale economics, and the various factors affecting ship size are examined using a typical conventional cargo ship and bulk cargo carriers based on shipowners ' cost data. http://archi...
Optimal vaccination and treatment of an epidemic network model
Energy Technology Data Exchange (ETDEWEB)
Chen, Lijuan [Department of Mathematics, Tongji University, Shanghai 200092 (China); College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350002 (China); Sun, Jitao, E-mail: sunjt@sh163.net [Department of Mathematics, Tongji University, Shanghai 200092 (China)
2014-08-22
In this Letter, we firstly propose an epidemic network model incorporating two controls which are vaccination and treatment. For the constant controls, by using Lyapunov function, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. For the non-constant controls, by using the optimal control strategy, we discuss an optimal strategy to minimize the total number of the infected and the cost associated with vaccination and treatment. Table 1 and Figs. 1–5 are presented to show the global stability and the efficiency of this optimal control. - Highlights: • Propose an optimally controlled SIRS epidemic model on heterogeneous networks. • Obtain criteria of global stability of the disease-free equilibrium and the endemic equilibrium. • Investigate existence of optimal control for the control problem. • The results be illustrated by some numerical simulations.
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.
Deterministic operations research models and methods in linear optimization
Rader, David J
2013-01-01
Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear
Shape optimization in biomimetics by homogenization modelling
International Nuclear Information System (INIS)
Hoppe, Ronald H.W.; Petrova, Svetozara I.
2003-08-01
Optimal shape design of microstructured materials has recently attracted a great deal of attention in material science. The shape and the topology of the microstructure have a significant impact on the macroscopic properties. The present work is devoted to the shape optimization of new biomorphic microcellular ceramics produced from natural wood by biotemplating. We are interested in finding the best material-and-shape combination in order to achieve the optimal prespecified performance of the composite material. The computation of the effective material properties is carried out using the homogenization method. Adaptive mesh-refinement technique based on the computation of recovered stresses is applied in the microstructure to find the homogenized elasticity coefficients. Numerical results show the reliability of the implemented a posteriori error estimator. (author)
Portfolio optimization for index tracking modelling in Malaysia stock market
Siew, Lam Weng; Jaaman, Saiful Hafizah; Ismail, Hamizun
2016-06-01
Index tracking is an investment strategy in portfolio management which aims to construct an optimal portfolio to generate similar mean return with the stock market index mean return without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using the optimization model which adopts regression approach in tracking the benchmark stock market index return. In this study, the data consists of weekly price of stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2013. The results of this study show that the optimal portfolio is able to track FBMKLCI Index at minimum tracking error of 1.0027% with 0.0290% excess mean return over the mean return of FBMKLCI Index. The significance of this study is to construct the optimal portfolio using optimization model which adopts regression approach in tracking the stock market index without purchasing all index components.
Following an Optimal Batch Bioreactor Operations Model
DEFF Research Database (Denmark)
Ibarra-Junquera, V.; Jørgensen, Sten Bay; Virgen-Ortíz, J.J.
2012-01-01
-batch reactor for biomass production is studied using a differential geometry approach. The maximization problem is solved by handling both the optimal filling policy and substrate concentration in the inlet stream. In order to follow the OBBOM, a master–slave synchronization is used. The OBBOM is considered...... 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...
Optimization of a new mathematical model for bacterial growth
The objective of this research is to optimize a new mathematical equation as a primary model to describe the growth of bacteria under constant temperature conditions. An optimization algorithm was used in combination with a numerical (Runge-Kutta) method to solve the differential form of the new gr...
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...... microstructure is optimized using topology optimization in order to achieve a better macroscopic performance quantified by vertical or torsional deflections. Constraints are introduced to ensure a certain deflection/extension ratio of the actuator....
Group Elevator Peak Scheduling Based on Robust Optimization Model
ZHANG, J.; ZONG, Q.
2013-01-01
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 re...
Optimization and emergence in marine ecosystem models
DEFF Research Database (Denmark)
Mariani, Patrizio; Visser, Andre
2010-01-01
Ingestion rates and mortality rates of zooplankton are dynamic parameters reflecting a behavioural trade-off between encounters with food and predators. An evolutionarily consistent behaviour is that which optimizes the trade-off in terms of the fitness conferred to an individual. We argue that i...
Optimal Combining Data for Improving Ocean Modeling
2014-09-30
optimal is searched separately. We have fused ADCP and CTD data collected in NAVOCEANO shipboard survey based upon the California Cooperative Fisheries ... reconstructed principal circulation patterns. A physically important result is that many of retrieved eddies turned out to live in rather shallow water, not...deeper than ~ 150 m. 3 Figure1. Reconstruction of circulation from NAVOCEANO data
Stochastic dynamic programming model for optimal resource ...
Indian Academy of Sciences (India)
M Bhuvaneswari
2018-04-11
Apr 11, 2018 ... containers, doctors, nurses, cash and stocks. Similarly, the uncertainty may have different characterizations in these applications. An approximate stochastic dynamic programming (SDP) [3] allows nodes with a number of possible actions with clear strategies for devising an effective decision on optimal ...
Optimizing Classroom Acoustics Using Computer Model Studies.
Reich, Rebecca; Bradley, John
1998-01-01
Investigates conditions relating to the maximum useful-to-detrimental sound ratios present in classrooms and determining the optimum conditions for speech intelligibility. Reveals that speech intelligibility is more strongly influenced by ambient noise levels and that the optimal location for sound absorbing material is on a classroom's upper…
Robust and fast nonlinear optimization of diffusion MRI microstructure models.
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
Mathematical Model for the Optimization of Compressive Strength of ...
African Journals Online (AJOL)
These mathematical models are adopted for optimization of strength of sandcrete block in compression. With the model, any desired strength of sandcrete block, given any mix proportions, is easily evaluated. Basic Language is used in the development of the computer program. The maximum compressive strength ...
Mathematical programming model for the optimization of nutritional ...
African Journals Online (AJOL)
The use of a mathematical programming model for determining optimal nutritional strategy for a dairy cow is described. Mixed Integer Programming (MIP) may be used to fit curvilinear functions, such as the changes in the nutrient requirements of the cow, into a standard mathematical programme. The model determines the.
Rare earth-doped integrated glass components: modeling and optimization
DEFF Research Database (Denmark)
Lumholt, Ole; Bjarklev, Anders Overgaard; Rasmussen, Thomas
1995-01-01
For the integrated optic erbium-doped phosphate silica-amplifier, a comprehensive model is presented which includes high-concentration dissipative ion-ion interactions. Based on actual waveguide parameters, the model is seen to reproduce measured gains closely. A rigorous design optimization is p...
Optimal Model-Based Control in HVAC Systems
DEFF Research Database (Denmark)
Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik
2008-01-01
This paper presents optimal model-based control of a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger (a rotary wheel heat recovery) and a water-to- air heat exchanger. First dynamic model of the HVAC system...
Optimizing incomplete sample designs for item response model parameters
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
Optimization of multi-model ensemble forecasting of typhoon waves
Directory of Open Access Journals (Sweden)
Shun-qi Pan
2016-01-01
Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.
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 and Optimization of Materials and Structures
Karama, Moussa; Borouchaki, Houman; Cherouat, Abel; El Hami, Abdelkhalak
2015-01-01
Preface : The current economic and ecological context requires different industries towards the optimization of structures. One of the most striking examples is the soaring price of oil, which launched the major aircraft manufacturers Airbus and Boeing in a race to relief structures, to reduce their consumption. Therefore, the need of competitive products, both in terms of reliability, performance or operating cost, is pervasive and growing, aviation and elsewhere. Thus, work on the material ...
A Framework for Cloudy Model Optimization and Database Storage
Calvén, Emilia; Helton, Andrew; Sankrit, Ravi
2018-01-01
We present a framework for producing Cloudy photoionization models of the nebular emission from novae ejecta and storing a subset of the results in SQL database format for later usage. The database can be searched for models best fitting observed spectral line ratios. Additionally, the framework includes an optimization feature that can be used in tandem with the database to search for and improve on models by creating new Cloudy models while, varying the parameters. The database search and optimization can be used to explore the structures of nebulae by deriving their properties from the best-fit models. The goal is to provide the community with a large database of Cloudy photoionization models, generated from parameters reflecting conditions within novae ejecta, that can be easily fitted to observed spectral lines; either by directly accessing the database using the framework code or by usage of a website specifically made for this purpose.
Detailed modelling and optimal design of membrane separation systems
Marriott, J. I.
2001-01-01
The search for alternatives to traditional energy intensive separation methods such as distillation has led to the introduction of processes based on membranes. In this research, the use of detailed mathematical models for the optimal design of membrane systems is investigated. Mathematical models of hollow-fibre and spiral-wound membrane modules are presented in this thesis. The models are developed from rigorous mass, momentum and energy balances and can be used to describ...
Multiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model
Tahriri, Farzad; Zawiah Md Dawal, Siti; Taha, Zahari
2014-01-01
It can be deduced from previous studies that there exists a research gap in assembly line sequencing optimization model for mixed-model production lines. In particular, there is a lack of studies which focus on the integration between job shop and assembly lines using fuzzy techniques. Hence, this paper is aimed at addressing the multiobjective mixed-model assembly line sequencing problem by integrating job shop and assembly production lines for factories with modular layouts. The primary go...
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...
Fuzzy Modelling of Knee Joint with Genetic Optimization
Directory of Open Access Journals (Sweden)
B. S. K. K. Ibrahim
2011-01-01
Full Text Available Modelling of joint properties of lower limbs in people with spinal cord injury is significantly challenging for researchers due to the complexity of the system. The objective of this study is to develop a knee joint model capable of relating electrical parameters to dynamic joint torque as well as knee angle for functional electrical stimulation application. The joint model consists of a segmental dynamic, time-invariant passive properties and uncertain time-variant active properties. The knee joint model structure comprising optimised equations of motion and fuzzy models to represent the passive viscoelasticity and active muscle properties is formulated. The model thus formulated is optimised using genetic optimization, and validated against experimental data. The developed model can be used for simulation of joint movements as well as for control development. The results show that the model developed gives an accurate dynamic characterisation of the knee joint.
Modeling to Optimize Hospital Evacuation Planning in EMS Systems.
Bish, Douglas R; Tarhini, Hussein; Amara, Roel; Zoraster, Richard; Bosson, Nichole; Gausche-Hill, Marianne
2017-01-01
To develop optimal hospital evacuation plans within a large urban EMS system using a novel evacuation planning model and a realistic hospital evacuation scenario, and to illustrate the ways in which a decision support model may be useful in evacuation planning. An optimization model was used to produce detailed evacuation plans given the number and type of patients in the evacuating hospital, resource levels (teams to move patients, vehicles, and beds at other hospitals), and evacuation rules. Optimal evacuation plans under various resource levels and rules were developed and high-level metrics were calculated, including evacuation duration and the utilization of resources. Using this model we were able to determine the limiting resources and demonstrate how strategically augmenting the resource levels can improve the performance of the evacuation plan. The model allowed the planner to test various evacuation conditions and resource levels to demonstrate the effect on performance of the evacuation plan. We present a hospital evacuation planning analysis for a hospital in a large urban EMS system using an optimization model. This model can be used by EMS administrators and medical directors to guide planning decisions and provide a better understanding of various resource allocation decisions and rules that govern a hospital evacuation.
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
The effect of using a robust optimality criterion in model based adaptive optimization.
Strömberg, Eric A; Hooker, Andrew C
2017-08-01
Optimizing designs using robust (global) optimality criteria has been shown to be a more flexible approach compared to using local optimality criteria. Additionally, model based adaptive optimal design (MBAOD) may be less sensitive to misspecification in the prior information available at the design stage. In this work, we investigate the influence of using a local (lnD) or a robust (ELD) optimality criterion for a MBAOD of a simulated dose optimization study, for rich and sparse sampling schedules. A stopping criterion for accurate effect prediction is constructed to determine the endpoint of the MBAOD by minimizing the expected uncertainty in the effect response of the typical individual. 50 iterations of the MBAODs were run using the MBAOD R-package, with the concentration from a one-compartment first-order absorption pharmacokinetic model driving the population effect response in a sigmoidal EMAX pharmacodynamics model. The initial cohort consisted of eight individuals in two groups and each additional cohort added two individuals receiving a dose optimized as a discrete covariate. The MBAOD designs using lnD and ELD optimality with misspecified initial model parameters were compared by evaluating the efficiency relative to an lnD-optimal design based on the true parameter values. For the explored example model, the MBAOD using ELD-optimal designs converged quicker to the theoretically optimal lnD-optimal design based on the true parameters for both sampling schedules. Thus, using a robust optimality criterion in MBAODs could reduce the number of adaptations required and improve the practicality of adaptive trials using optimal design.
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
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.
Optimal control of information epidemics modeled as Maki Thompson rumors
Kandhway, Kundan; Kuri, Joy
2014-12-01
We model the spread of information in a homogeneously mixed population using the Maki Thompson rumor model. We formulate an optimal control problem, from the perspective of single campaigner, to maximize the spread of information when the campaign budget is fixed. Control signals, such as advertising in the mass media, attempt to convert ignorants and stiflers into spreaders. We show the existence of a solution to the optimal control problem when the campaigning incurs non-linear costs under the isoperimetric budget constraint. The solution employs Pontryagin's Minimum Principle and a modified version of forward backward sweep technique for numerical computation to accommodate the isoperimetric budget constraint. The techniques developed in this paper are general and can be applied to similar optimal control problems in other areas. We have allowed the spreading rate of the information epidemic to vary over the campaign duration to model practical situations when the interest level of the population in the subject of the campaign changes with time. The shape of the optimal control signal is studied for different model parameters and spreading rate profiles. We have also studied the variation of the optimal campaigning costs with respect to various model parameters. Results indicate that, for some model parameters, significant improvements can be achieved by the optimal strategy compared to the static control strategy. The static strategy respects the same budget constraint as the optimal strategy and has a constant value throughout the campaign horizon. This work finds application in election and social awareness campaigns, product advertising, movie promotion and crowdfunding campaigns.
Optimizing Biorefinery Design and Operations via Linear Programming Models
Energy Technology Data Exchange (ETDEWEB)
Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric
2017-03-28
The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for
Optimizing the calculation grid for atmospheric dispersion modelling
International Nuclear Information System (INIS)
Van Thielen, S.; Turcanu, C.; Camps, J.; Keppens, R.
2015-01-01
This paper presents three approaches to find optimized grids for atmospheric dispersion measurements and calculations in emergency planning. This can be useful for deriving optimal positions for mobile monitoring stations, or help to reduce discretization errors and improve recommendations. Indeed, threshold-based recommendations or conclusions may differ strongly on the shape and size of the grid on which atmospheric dispersion measurements or calculations of pollutants are based. Therefore, relatively sparse grids that retain as much information as possible, are required. The grid optimization procedure proposed here is first demonstrated with a simple Gaussian plume model as adopted in atmospheric dispersion calculations, which provides fast calculations. The optimized grids are compared to the Noodplan grid, currently used for emergency planning in Belgium, and to the exact solution. We then demonstrate how it can be used in more realistic dispersion models. - Highlights: • Grid points for atmospheric dispersion calculations are optimized. • Using heuristics the optimization problem results into different grid shapes. • Comparison between optimized models and the Noodplan grid is performed
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.
Optimizing a gap conductance model applicable to VVER-1000 thermal–hydraulic model
International Nuclear Information System (INIS)
Rahgoshay, M.; Hashemi-Tilehnoee, M.
2012-01-01
Highlights: ► Two known conductance models for application in VVER-1000 thermal–hydraulic code are examined. ► An optimized gap conductance model is developed which can predict the gap conductance in good agreement with FSAR data. ► The licensed thermal–hydraulic code is coupled with the gap conductance model predictor externally. -- Abstract: The modeling of gap conductance for application in VVER-1000 thermal–hydraulic codes is addressed. Two known models, namely CALZA-BINI and RELAP5 gap conductance models, are examined. By externally linking of gap conductance models and COBRA-EN thermal hydraulic code, the acceptable range of each model is specified. The result of each gap conductance model versus linear heat rate has been compared with FSAR data. A linear heat rate of about 9 kW/m is the boundary for optimization process. Since each gap conductance model has its advantages and limitation, the optimized gap conductance model can predict the gap conductance better than each of the two other models individually.
Modeling, estimation and optimal filtration in signal processing
Najim, Mohamed
2010-01-01
The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the
Empirical modelling of ENSO dynamics: construction of optimal complexity models from data
Mukhina, A.; Kondrashov, D.; Mukhin, D.
2012-04-01
One of the main problems arising in modelling of data taken from natural system is finding of a phase space suitable for construction of the evolution operator model. The matter is we ususaly deal with strongly high-dimensional behavior and we are forced to construct a model working in some projection of system phase space corresponding to time scales of interest. Selection of optimal projecion is non-trivial problem since there are many ways to reconstruct phase variables from given time series, especially in the case when time series has a form of spatial field depending on time. Actually, it is sort of model selection problem, because, on the one hand, the transformation of data to some phase variables vector can be considered as a part of the model. On the other hand, such an optimization of a phase space makes sense only in relation to the parameterization of the model we use, i.e. representation of evolution operator, so we should find an optimal structure of the model togerther with phase variables vector. In this work we suggest Bayesian approach to this problem: a prior set of the models of different complexity is defined, then posterior probabilities of each model from this set given the data are calculated, and the model corresponding to largest probability is selected. The suggested approach is applied to optimization of EMR-model of ENSO phenomenon elaborated by Kondrashov et. al. This model operates with number of principal EOFs constructed from spatial field of SST in Equatorial Pacific, and has a form of stochastic differential equations (SDE) system with polynomial parameterization of the right-hand part. Optimal values for both the number of EOFs and the order of SDE system are estimated from the time series generated by Jin & Neelin intermediate ENSO model.
A dynamic optimization model for solid waste recycling.
Anghinolfi, Davide; Paolucci, Massimo; Robba, Michela; Taramasso, Angela Celeste
2013-02-01
Recycling is an important part of waste management (that includes different kinds of issues: environmental, technological, economic, legislative, social, etc.). Differently from many works in literature, this paper is focused on recycling management and on the dynamic optimization of materials collection. The developed dynamic decision model is characterized by state variables, corresponding to the quantity of waste in each bin per each day, and control variables determining the quantity of material that is collected in the area each day and the routes for collecting vehicles. The objective function minimizes the sum of costs minus benefits. The developed decision model is integrated in a GIS-based Decision Support System (DSS). A case study related to the Cogoleto municipality is presented to show the effectiveness of the proposed model. From optimal results, it has been found that the net benefits of the optimized collection are about 2.5 times greater than the estimated current policy. Copyright © 2012 Elsevier Ltd. All rights reserved.
Optimal control for micro-algae on a raceway model.
Hurst, Todd; Rehbock, Volker
2018-01-01
We apply numerical optimal control methods to an existing algae growth model with the aim to determine the best performance of the model under known conditions using a variety of decision variables. We transform the system of differential algebraic equations in the existing model to a system of ordinary differential equations which introduces dynamics for average light intensity and chlorophyll. In addition, we allow for variable nitrogen concentration of the inflow as well as variable initial nitrogen concentration of the raceway. Our main focus is on optimizing of the production of lipids. We calculate both open and closed loop optimal controllers and test their robustness. Finally, we also consider raceway depth as a decision variable. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:107-119, 2018. © 2017 American Institute of Chemical Engineers.
Characterization, Modeling, and Optimization of Light-Emitting Diode Systems
DEFF Research Database (Denmark)
Thorseth, Anders
This thesis explores, characterization, modeling, and optimization of light-emitting diodes (LED) for general illumination. An automated setup has been developed for spectral radiometric characterization of LED components with precise control of the settings of forward current and operating...... temperature. The automated setup has been used to characterize commercial LED components with respect to multiple settings. It is shown that the droop in quantum efficiency can be approximated by a simple parabolic function. The investigated models of the spectral power distributions (SPD) from LEDs...... comparing the chromaticity of the measured SPD with tted models, the deviation is found to be larger than the lower limit of human color perception. A method has been developed to optimize multicolored cluster LED systems with respect to light quality, using multi objective optimization. The results...
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
Space engineering modeling and optimization with case studies
Pintér, János
2016-01-01
This book presents a selection of advanced case studies that cover a substantial range of issues and real-world challenges and applications in space engineering. Vital mathematical modeling, optimization methodologies and numerical solution aspects of each application case study are presented in detail, with discussions of a range of advanced model development and solution techniques and tools. Space engineering challenges are discussed in the following contexts: •Advanced Space Vehicle Design •Computation of Optimal Low Thrust Transfers •Indirect Optimization of Spacecraft Trajectories •Resource-Constrained Scheduling, •Packing Problems in Space •Design of Complex Interplanetary Trajectories •Satellite Constellation Image Acquisition •Re-entry Test Vehicle Configuration Selection •Collision Risk Assessment on Perturbed Orbits •Optimal Robust Design of Hybrid Rocket Engines •Nonlinear Regression Analysis in Space Engineering< •Regression-Based Sensitivity Analysis and Robust Design ...
Model-Based Optimization for Flapping Foil Actuation
Izraelevitz, Jacob; Triantafyllou, Michael
2014-11-01
Flapping foil actuation in nature, such as wings and flippers, often consist of highly complex joint kinematics which present an impossibly large parameter space for designing bioinspired mechanisms. Designers therefore often build a simplified model to limit the parameter space so an optimum motion trajectory can be experimentally found, or attempt to replicate exactly the joint geometry and kinematics of a suitable organism whose behavior is assumed to be optimal. We present a compromise: using a simple local fluids model to guide the design of optimized trajectories through a succession of experimental trials, even when the parameter space is too large to effectively search. As an example, we illustrate an optimization routine capable of designing asymmetric flapping trajectories for a large aspect-ratio pitching and heaving foil, with the added degree of freedom of allowing the foil to move parallel to flow. We then present PIV flow visualizations of the optimized trajectories.
Optimizing the calculation grid for atmospheric dispersion modelling.
Van Thielen, S; Turcanu, C; Camps, J; Keppens, R
2015-04-01
This paper presents three approaches to find optimized grids for atmospheric dispersion measurements and calculations in emergency planning. This can be useful for deriving optimal positions for mobile monitoring stations, or help to reduce discretization errors and improve recommendations. Indeed, threshold-based recommendations or conclusions may differ strongly on the shape and size of the grid on which atmospheric dispersion measurements or calculations of pollutants are based. Therefore, relatively sparse grids that retain as much information as possible, are required. The grid optimization procedure proposed here is first demonstrated with a simple Gaussian plume model as adopted in atmospheric dispersion calculations, which provides fast calculations. The optimized grids are compared to the Noodplan grid, currently used for emergency planning in Belgium, and to the exact solution. We then demonstrate how it can be used in more realistic dispersion models. Copyright © 2015 Elsevier Ltd. All rights reserved.
Integer programming model for optimizing bus timetable using genetic algorithm
Wihartiko, F. D.; Buono, A.; Silalahi, B. P.
2017-01-01
Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.
Optimizing Computing Platforms for Climate-Driven Ecological Forecasting Models
Farley, S. S.; Williams, J. W.
2016-12-01
Species distribution models are widely used, climate-driven ecological forecasting tools that use machine-learning techniques to predict species range shifts and ecological responses to 21st century climate change. As high-resolution modern and fossil biodiversity data becomes increasingly available and statistical learning methods become more computationally intensive, choosing the correct computing configuration on which to run these models becomes more important. With a variety of low-cost cloud and desktop computing options available, users of forecasting models must balance performance gains achieved by provisioning more powerful hardware with the cost of using these resources. We present a framework for estimating the optimal computing solution for a given modeling activity. We argue that this framework is capable of identifying the optimal computing solution - the one that maximizes model accuracy while minimizing resource cost and computing time. Our framework is built on constituent models of algorithm execution time, predictive skill, and computing cost. We demonstrate the results of the framework using four leading species distribution models: multivariate adaptive regression splines, generalized additive models, support vector machines, and boosted regression trees. The constituent models themselves are shown to have high predictive accuracy, and can be used independently to estimate the effects of using larger input datasets, such as those that incorporate data from the fossil record. When used together, our framework shows highly significant predictive ability, and is designed to be used by researchers to inform future computing provisioning strategies.
TLM modeling and system identification of optimized antenna structures
Directory of Open Access Journals (Sweden)
N. Fichtner
2008-05-01
Full Text Available The transmission line matrix (TLM method in conjunction with the genetic algorithm (GA is presented for the bandwidth optimization of a low profile patch antenna. The optimization routine is supplemented by a system identification (SI procedure. By the SI the model parameters of the structure are estimated which is used for a reduction of the total TLM simulation time. The SI utilizes a new stability criterion of the physical poles for the parameter extraction.
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
A stochastic discrete optimization model for designing container terminal facilities
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.
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.
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...
Optimization methods and silicon solar cell numerical models
Girardini, K.; Jacobsen, S. E.
1986-01-01
An optimization algorithm for use with numerical silicon solar cell models was developed. By coupling an optimization algorithm with a solar cell model, it is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junction depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm was developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAP1D). SCAP1D uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the performance of a solar cell. A major obstacle is that the numerical methods used in SCAP1D require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the values associated with the maximum efficiency. This problem was alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution.
A Multiobjective Optimization Model in Automotive Supply Chain Networks
Directory of Open Access Journals (Sweden)
Abdolhossein Sadrnia
2013-01-01
Full Text Available In the new decade, green investment decisions are attracting more interest in design supply chains due to the hidden economic benefits and environmental legislative barriers. In this paper, a supply chain network design problem with both economic and environmental concerns is presented. Therefore, a multiobjective optimization model that captures the trade-off between the total logistics cost and CO2 emissions is proposed. With regard to the complexity of logistic networks, a new multiobjective swarm intelligence algorithm known as a multiobjective Gravitational search algorithm (MOGSA has been implemented for solving the proposed mathematical model. To evaluate the effectiveness of the model, a comprehensive set of numerical experiments is explained. The results obtained show that the proposed model can be applied as an effective tool in strategic planning for optimizing cost and CO2 emissions in an environmentally friendly automotive supply chain.
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
Time dependent optimal switching controls in online selling models
Energy Technology Data Exchange (ETDEWEB)
Bradonjic, Milan [Los Alamos National Laboratory; Cohen, Albert [MICHIGAN STATE UNIV
2010-01-01
We present a method to incorporate dishonesty in online selling via a stochastic optimal control problem. In our framework, the seller wishes to maximize her average wealth level W at a fixed time T of her choosing. The corresponding Hamilton-Jacobi-Bellmann (HJB) equation is analyzed for a basic case. For more general models, the admissible control set is restricted to a jump process that switches between extreme values. We propose a new approach, where the optimal control problem is reduced to a multivariable optimization problem.
Neutron density optimal control of A-1 reactor analoque model
International Nuclear Information System (INIS)
Grof, V.
1975-01-01
Two applications are described of the optimal control of a reactor analog model. Both cases consider the control of neutron density. Control loops containing the on-line controlled process, the reactor of the first Czechoslovak nuclear power plant A-1, are simulated on an analog computer. Two versions of the optimal control algorithm are derived using modern control theory (Pontryagin's maximum principle, the calculus of variations, and Kalman's estimation theory), the minimum time performance index, and the quadratic performance index. The results of the optimal control analysis are compared with the A-1 reactor conventional control. (author)
Optimal experiment design for model selection in biochemical networks.
Vanlier, Joep; Tiemann, Christian A; Hilbers, Peter A J; van Riel, Natal A W
2014-02-20
Mathematical modeling is often used to formalize hypotheses on how a biochemical network operates by discriminating between competing models. Bayesian model selection offers a way to determine the amount of evidence that data provides to support one model over the other while favoring simple models. In practice, the amount of experimental data is often insufficient to make a clear distinction between competing models. Often one would like to perform a new experiment which would discriminate between competing hypotheses. We developed a novel method to perform Optimal Experiment Design to predict which experiments would most effectively allow model selection. A Bayesian approach is applied to infer model parameter distributions. These distributions are sampled and used to simulate from multivariate predictive densities. The method is based on a k-Nearest Neighbor estimate of the Jensen Shannon divergence between the multivariate predictive densities of competing models. We show that the method successfully uses predictive differences to enable model selection by applying it to several test cases. Because the design criterion is based on predictive distributions, which can be computed for a wide range of model quantities, the approach is very flexible. The method reveals specific combinations of experiments which improve discriminability even in cases where data is scarce. The proposed approach can be used in conjunction with existing Bayesian methodologies where (approximate) posteriors have been determined, making use of relations that exist within the inferred posteriors.
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.
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.
Reduced order modeling in topology optimization of vibroacoustic problems
DEFF Research Database (Denmark)
Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Brunskog, Jonas
2017-01-01
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...... suffer from the curse of dimensionality. Moreover, the fact that the nature of the elements changes throughout the optimization (material to void or material to air) makes it more difficult to create a global basis that is accurate throughout the whole design space. In this work, these challenges...
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.
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
Pareto-Optimal Model Selection via SPRINT-Race.
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.
Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.
2015-01-01
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
Electrochemical model based charge optimization for lithium-ion batteries
Pramanik, Sourav; Anwar, Sohel
2016-05-01
In this paper, we propose the design of a novel optimal strategy for charging the lithium-ion battery based on electrochemical battery model that is aimed at improved performance. A performance index that aims at minimizing the charging effort along with a minimum deviation from the rated maximum thresholds for cell temperature and charging current has been defined. The method proposed in this paper aims at achieving a faster charging rate while maintaining safe limits for various battery parameters. Safe operation of the battery is achieved by including the battery bulk temperature as a control component in the performance index which is of critical importance for electric vehicles. Another important aspect of the performance objective proposed here is the efficiency of the algorithm that would allow higher charging rates without compromising the internal electrochemical kinetics of the battery which would prevent abusive conditions, thereby improving the long term durability. A more realistic model, based on battery electro-chemistry has been used for the design of the optimal algorithm as opposed to the conventional equivalent circuit models. To solve the optimization problem, Pontryagins principle has been used which is very effective for constrained optimization problems with both state and input constraints. Simulation results show that the proposed optimal charging algorithm is capable of shortening the charging time of a lithium ion cell while maintaining the temperature constraint when compared with the standard constant current charging. The designed method also maintains the internal states within limits that can avoid abusive operating conditions.
Stochastic Modelling and Optimization of Complex Infrastructure Systems
DEFF Research Database (Denmark)
Thoft-Christensen, Palle
In this paper it is shown that recent progress in stochastic modelling and optimization in combination with advanced computer systems has now made it possible to improve the design and the maintenance strategies for infrastructure systems. The paper concentrates on highway networks and single large...
Optimal Tax Reduction by Depreciation : A Stochastic Model
Berg, M.; De Waegenaere, A.M.B.; Wielhouwer, J.L.
1996-01-01
This paper focuses on the choice of a depreciation method, when trying to minimize the expected value of the present value of future tax payments.In a quite general model that allows for stochastic future cash- ows and a tax structure with tax brackets, we determine the optimal choice between the
A mechanistic model for rational design of optimal cellulase mixtures.
Levine, Seth E; Fox, Jerome M; Clark, Douglas S; Blanch, Harvey W
2011-11-01
A model-based framework is described that permits the optimal composition of cellulase enzyme mixtures to be found for lignocellulose hydrolysis. The rates of hydrolysis are shown to be dependent on the nature of the substrate. For bacterial microcrystalline cellulose (BMCC) hydrolyzed by a ternary cellulase mixture of EG2, CBHI, and CBHII, the optimal predicted mixture was 1:0:1 EG2:CBHI:CBHII at 24 h and 1:1:0 at 72 h, at loadings of 10 mg enzyme per g substrate. The model was validated with measurements of soluble cello-oligosaccharide production from BMCC during both single enzyme and mixed enzyme hydrolysis. Three-dimensional diagrams illustrating cellulose conversion were developed for mixtures of EG2, CBHI, CBHII acting on BMCC and predicted for other substrates with a range of substrate properties. Model predictions agreed well with experimental values of conversion after 24 h for a variety of enzyme mixtures. The predicted mixture performances for substrates with varying properties demonstrated the effects of initial degree of polymerization (DP) and surface area on the performance of cellulase mixtures. For substrates with a higher initial DP, endoglucanase enzymes accounted for a larger fraction of the optimal mixture. Substrates with low surface areas showed significantly reduced hydrolysis rates regardless of mixture composition. These insights, along with the quantitative predictions, demonstrate the utility of this model-based framework for optimizing cellulase mixtures. Copyright © 2011 Wiley Periodicals, Inc.
Modeling and Optimization in USEF-compliant Hierarchical Energy Markets
Nguyen, Dinh Bao; Scherpen, Jacquelien M.A.; ter Haar, B.; Bliek, Frits
2016-01-01
This paper presents a new model and optimization method for balancing in the Universal Smart Energy Framework. We address the problem of minimizing the error between the forecasted and the actual load in the power system that arise from the uncertainties of renewable energy production. The algorithm
The Optimal Portfolio Selection Model under g-Expectation
Directory of Open Access Journals (Sweden)
Li Li
2014-01-01
complicated and sophisticated, the optimal solution turns out to be surprisingly simple, the payoff of a portfolio of two binary claims. Also I give the economic meaning of my model and the comparison with that one in the work of Jin and Zhou, 2008.
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...
Discover for Yourself: An Optimal Control Model in Insect Colonies
Winkel, Brian
2013-01-01
We describe the enlightening path of self-discovery afforded to the teacher of undergraduate mathematics. This is demonstrated as we find and develop background material on an application of optimal control theory to model the evolutionary strategy of an insect colony to produce the maximum number of queen or reproducer insects in the colony at…
Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU.
Xia, Yong; Wang, Kuanquan; Zhang, Henggui
2015-01-01
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.
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.
Modeling of biological intelligence for SCM system optimization.
Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang
2012-01-01
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.
Modeling of Biological Intelligence for SCM System Optimization
Directory of Open Access Journals (Sweden)
Shengyong Chen
2012-01-01
Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.
Modeling of Biological Intelligence for SCM System Optimization
Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang
2012-01-01
This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724
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...... tools, so different solver backends can be used and changed in a transparent way. Optlang’s object-oriented API takes advantage of the symbolic math library SymPy (Team 2016) to allow objective functions and constraints to be easily formulated algebraically from symbolic expressions of variables....... Optlang targets scientists who can thus focus on formulating optimization problems based on mathematical equations derived from domain knowledge. Solver interfaces can be added by subclassing the four main classes of the optlang API (Variable, Constraint, Objective, and Model) and implementing...
Modelling, Optimization and Optimal Control of Small Scale Stirred Tank Bioreactors
Directory of Open Access Journals (Sweden)
Mitko Petrov
2004-10-01
Full Text Available Models of the mass-transfer in a stirred tank bioreactor depending on general indexes of the processes of aeration and mixing in concrete simplifications of the hydrodynamic structure of the flows are developed. The offered combined model after parameters identification is used for optimization of the parameters of the apparatus construction. The optimization problem is solved by using of the fuzzy sets theory and in this way the unspecified as a result of the model simplification are read. In conclusion an optimal control of a fed-batch fermentation process of E. coli is completed by using Neuro-Dynamic programming. The received results after optimization show a considerable improvement of the mass-transfer indexes and the quantity indexes at the end of the process.
Optimized volume models of earthquake-triggered landslides.
Xu, Chong; Xu, Xiwei; Shen, Lingling; Yao, Qi; Tan, Xibin; Kang, Wenjun; Ma, Siyuan; Wu, Xiyan; Cai, Juntao; Gao, Mingxing; Li, Kang
2016-07-12
In this study, we proposed three optimized models for calculating the total volume of landslides triggered by the 2008 Wenchuan, China Mw 7.9 earthquake. First, we calculated the volume of each deposit of 1,415 landslides triggered by the quake based on pre- and post-quake DEMs in 20 m resolution. The samples were used to fit the conventional landslide "volume-area" power law relationship and the 3 optimized models we proposed, respectively. Two data fitting methods, i.e. log-transformed-based linear and original data-based nonlinear least square, were employed to the 4 models. Results show that original data-based nonlinear least square combining with an optimized model considering length, width, height, lithology, slope, peak ground acceleration, and slope aspect shows the best performance. This model was subsequently applied to the database of landslides triggered by the quake except for two largest ones with known volumes. It indicates that the total volume of the 196,007 landslides is about 1.2 × 10(10) m(3) in deposit materials and 1 × 10(10) m(3) in source areas, respectively. The result from the relationship of quake magnitude and entire landslide volume related to individual earthquake is much less than that from this study, which reminds us the necessity to update the power-law relationship.
Optimal control in a model of malaria with differential susceptibility
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.
Health benefit modelling and optimization of vehicular pollution control strategies
Sonawane, Nayan V.; Patil, Rashmi S.; Sethi, Virendra
2012-12-01
This study asserts that the evaluation of pollution reduction strategies should be approached on the basis of health benefits. The framework presented could be used for decision making on the basis of cost effectiveness when the strategies are applied concurrently. Several vehicular pollution control strategies have been proposed in literature for effective management of urban air pollution. The effectiveness of these strategies has been mostly studied as a one at a time approach on the basis of change in pollution concentration. The adequacy and practicality of such an approach is studied in the present work. Also, the assessment of respective benefits of these strategies has been carried out when they are implemented simultaneously. An integrated model has been developed which can be used as a tool for optimal prioritization of various pollution management strategies. The model estimates health benefits associated with specific control strategies. ISC-AERMOD View has been used to provide the cause-effect relation between control options and change in ambient air quality. BenMAP, developed by U.S. EPA, has been applied for estimation of health and economic benefits associated with various management strategies. Valuation of health benefits has been done for impact indicators of premature mortality, hospital admissions and respiratory syndrome. An optimization model has been developed to maximize overall social benefits with determination of optimized percentage implementations for multiple strategies. The model has been applied for sub-urban region of Mumbai city for vehicular sector. Several control scenarios have been considered like revised emission standards, electric, CNG, LPG and hybrid vehicles. Reduction in concentration and resultant health benefits for the pollutants CO, NOx and particulate matter are estimated for different control scenarios. Finally, an optimization model has been applied to determine optimized percentage implementation of specific
A Convex Optimization Model and Algorithm for Retinex
Directory of Open Access Journals (Sweden)
Qing-Nan Zhao
2017-01-01
Full Text Available Retinex is a theory on simulating and explaining how human visual system perceives colors under different illumination conditions. The main contribution of this paper is to put forward a new convex optimization model for Retinex. Different from existing methods, the main idea is to rewrite a multiplicative form such that the illumination variable and the reflection variable are decoupled in spatial domain. The resulting objective function involves three terms including the Tikhonov regularization of the illumination component, the total variation regularization of the reciprocal of the reflection component, and the data-fitting term among the input image, the illumination component, and the reciprocal of the reflection component. We develop an alternating direction method of multipliers (ADMM to solve the convex optimization model. Numerical experiments demonstrate the advantages of the proposed model which can decompose an image into the illumination and the reflection components.
A model for HIV/AIDS pandemic with optimal control
Sule, Amiru; Abdullah, Farah Aini
2015-05-01
Human immunodeficiency virus and acquired immune deficiency syndrome (HIV/AIDS) is pandemic. It has affected nearly 60 million people since the detection of the disease in 1981 to date. In this paper basic deterministic HIV/AIDS model with mass action incidence function are developed. Stability analysis is carried out. And the disease free equilibrium of the basic model was found to be locally asymptotically stable whenever the threshold parameter (RO) value is less than one, and unstable otherwise. The model is extended by introducing two optimal control strategies namely, CD4 counts and treatment for the infective using optimal control theory. Numerical simulation was carried out in order to illustrate the analytic results.
Linear versus quadratic portfolio optimization model with transaction cost
Razak, Norhidayah Bt Ab; Kamil, Karmila Hanim; Elias, Siti Masitah
2014-06-01
Optimization model is introduced to become one of the decision making tools in investment. Hence, it is always a big challenge for investors to select the best model that could fulfill their goal in investment with respect to risk and return. In this paper we aims to discuss and compare the portfolio allocation and performance generated by quadratic and linear portfolio optimization models namely of Markowitz and Maximin model respectively. The application of these models has been proven to be significant and popular among others. However transaction cost has been debated as one of the important aspects that should be considered for portfolio reallocation as portfolio return could be significantly reduced when transaction cost is taken into consideration. Therefore, recognizing the importance to consider transaction cost value when calculating portfolio' return, we formulate this paper by using data from Shariah compliant securities listed in Bursa Malaysia. It is expected that, results from this paper will effectively justify the advantage of one model to another and shed some lights in quest to find the best decision making tools in investment for individual investors.
The PDB_REDO server for macromolecular structure model optimization
Directory of Open Access Journals (Sweden)
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.
A new adaptive hybrid electromagnetic damper: modelling, optimization, and experiment
International Nuclear Information System (INIS)
Asadi, Ehsan; Ribeiro, Roberto; Behrad Khamesee, Mir; Khajepour, Amir
2015-01-01
This paper presents the development of a new electromagnetic hybrid damper which provides regenerative adaptive damping force for various applications. Recently, the introduction of electromagnetic technologies to the damping systems has provided researchers with new opportunities for the realization of adaptive semi-active damping systems with the added benefit of energy recovery. In this research, a hybrid electromagnetic damper is proposed. The hybrid damper is configured to operate with viscous and electromagnetic subsystems. The viscous medium provides a bias and fail-safe damping force while the electromagnetic component adds adaptability and the capacity for regeneration to the hybrid design. The electromagnetic component is modeled and analyzed using analytical (lumped equivalent magnetic circuit) and electromagnetic finite element method (FEM) (COMSOL ® software package) approaches. By implementing both modeling approaches, an optimization for the geometric aspects of the electromagnetic subsystem is obtained. Based on the proposed electromagnetic hybrid damping concept and the preliminary optimization solution, a prototype is designed and fabricated. A good agreement is observed between the experimental and FEM results for the magnetic field distribution and electromagnetic damping forces. These results validate the accuracy of the modeling approach and the preliminary optimization solution. An analytical model is also presented for viscous damping force, and is compared with experimental results The results show that the damper is able to produce damping coefficients of 1300 and 0–238 N s m −1 through the viscous and electromagnetic components, respectively. (paper)
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.
Galerkin v. discrete-optimal projection in nonlinear model reduction
Energy Technology Data Exchange (ETDEWEB)
Carlberg, Kevin Thomas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Barone, Matthew Franklin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Antil, Harbir [George Mason Univ., Fairfax, VA (United States)
2015-04-01
Discrete-optimal model-reduction techniques such as the Gauss{Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible ow problems where standard Galerkin techniques have failed. However, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform projection at the time-continuous level, while discrete-optimal techniques do so at the time-discrete level. This work provides a detailed theoretical and experimental comparison of the two techniques for two common classes of time integrators: linear multistep schemes and Runge{Kutta schemes. We present a number of new ndings, including conditions under which the discrete-optimal ROM has a time-continuous representation, conditions under which the two techniques are equivalent, and time-discrete error bounds for the two approaches. Perhaps most surprisingly, we demonstrate both theoretically and experimentally that decreasing the time step does not necessarily decrease the error for the discrete-optimal ROM; instead, the time step should be `matched' to the spectral content of the reduced basis. In numerical experiments carried out on a turbulent compressible- ow problem with over one million unknowns, we show that increasing the time step to an intermediate value decreases both the error and the simulation time of the discrete-optimal reduced-order model by an order of magnitude.
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.
Routing and Scheduling Optimization Model of Sea Transportation
barus, Mika debora br; asyrafy, Habib; nababan, Esther; mawengkang, Herman
2018-01-01
This paper examines the routing and scheduling optimization model of sea transportation. One of the issues discussed is about the transportation of ships carrying crude oil (tankers) which is distributed to many islands. The consideration is the cost of transportation which consists of travel costs and the cost of layover at the port. Crude oil to be distributed consists of several types. This paper develops routing and scheduling model taking into consideration some objective functions and constraints. The formulation of the mathematical model analyzed is to minimize costs based on the total distance visited by the tanker and minimize the cost of the ports. In order for the model of the problem to be more realistic and the cost calculated to be more appropriate then added a parameter that states the multiplier factor of cost increases as the charge of crude oil is filled.
Optimization of arterial age prediction models based in pulse wave
International Nuclear Information System (INIS)
Scandurra, A G; Meschino, G J; Passoni, L I; Dai Pra, A L; Introzzi, A R; Clara, F M
2007-01-01
We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff
Optimization of arterial age prediction models based in pulse wave
Energy Technology Data Exchange (ETDEWEB)
Scandurra, A G [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Meschino, G J [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Passoni, L I [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Dai Pra, A L [Engineering Aplied Artificial Intelligence Group, Mathematics Department, Mar del Plata University (Argentina); Introzzi, A R [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina); Clara, F M [Bioengineering Laboratory, Electronic Department, Mar del Plata University (Argentina)
2007-11-15
We propose the detection of early arterial ageing through a prediction model of arterial age based in the coherence assumption between the pulse wave morphology and the patient's chronological age. Whereas we evaluate several methods, a Sugeno fuzzy inference system is selected. Models optimization is approached using hybrid methods: parameter adaptation with Artificial Neural Networks and Genetic Algorithms. Features selection was performed according with their projection on main factors of the Principal Components Analysis. The model performance was tested using the bootstrap error type .632E. The model presented an error smaller than 8.5%. This result encourages including this process as a diagnosis module into the device for pulse analysis that has been developed by the Bioengineering Laboratory staff.
Distributionally Robust Return-Risk Optimization Models and Their Applications
Directory of Open Access Journals (Sweden)
Li Yang
2014-01-01
Full Text Available Based on the risk control of conditional value-at-risk, distributionally robust return-risk optimization models with box constraints of random vector are proposed. They describe uncertainty in both the distribution form and moments (mean and covariance matrix of random vector. It is difficult to solve them directly. Using the conic duality theory and the minimax theorem, the models are reformulated as semidefinite programming problems, which can be solved by interior point algorithms in polynomial time. An important theoretical basis is therefore provided for applications of the models. Moreover, an application of the models to a practical example of portfolio selection is considered, and the example is evaluated using a historical data set of four stocks. Numerical results show that proposed methods are robust and the investment strategy is safe.
Multiobjective Fuzzy Mixed Assembly Line Sequencing Optimization Model
Directory of Open Access Journals (Sweden)
Farzad Tahriri
2014-01-01
Full Text Available It can be deduced from previous studies that there exists a research gap in assembly line sequencing optimization model for mixed-model production lines. In particular, there is a lack of studies which focus on the integration between job shop and assembly lines using fuzzy techniques. Hence, this paper is aimed at addressing the multiobjective mixed-model assembly line sequencing problem by integrating job shop and assembly production lines for factories with modular layouts. The primary goal is to minimize the make-span, setup time, and cost simultaneously in mixed-model assembly lines. Such conflicting goals arise when switching between different products. A genetic algorithm (GA approach is used to solve this problem, in which trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data.
Modeling Power-Constrained Optimal Backlight Dimming for Color Displays
DEFF Research Database (Denmark)
Burini, Nino; Nadernejad, Ehsan; Korhonen, Jari
2013-01-01
In this paper, we present a framework for modeling color liquid crystal displays (LCDs) having local light-emitting diode (LED) backlight with dimming capability. The proposed framework includes critical aspects like leakage, clipping, light diffusion and human perception of luminance and allows...... adjustable penalization of power consumption. Based on the framework, we have designed a set of optimization-based backlight dimming algorithms providing a perceptual optimal balance of clipping and leakage, if necessary. The novel algorithms are compared with several other schemes known from the literature...
Replica Analysis for Portfolio Optimization with Single-Factor Model
Shinzato, Takashi
2017-06-01
In this paper, we use replica analysis to investigate the influence of correlation among the return rates of assets on the solution of the portfolio optimization problem. We consider the behavior of an optimal solution for the case where the return rate is described with a single-factor model and compare the findings obtained from our proposed methods with correlated return rates with those obtained with independent return rates. We then analytically assess the increase in the investment risk when correlation is included. Furthermore, we also compare our approach with analytical procedures for minimizing the investment risk from operations research.
Modelling of Rabies Transmission Dynamics Using Optimal Control Analysis
Directory of Open Access Journals (Sweden)
Joshua Kiddy K. Asamoah
2017-01-01
Full Text Available We examine an optimal way of eradicating rabies transmission from dogs into the human population, using preexposure prophylaxis (vaccination and postexposure prophylaxis (treatment due to public education. We obtain the disease-free equilibrium, the endemic equilibrium, the stability, and the sensitivity analysis of the optimal control model. Using the Latin hypercube sampling (LHS, the forward-backward sweep scheme and the fourth-order Range-Kutta numerical method predict that the global alliance for rabies control’s aim of working to eliminate deaths from canine rabies by 2030 is attainable through mass vaccination of susceptible dogs and continuous use of pre- and postexposure prophylaxis in humans.
Process model development for optimization of forged disk manufacturing processes
Energy Technology Data Exchange (ETDEWEB)
Fischer, C.E.; Gunasekera, J.S. [Ohio Univ., Athens, OH (United States). Center for Advanced Materials Processing; Malas, J.C. [Wright Labs., Wright Patterson AFB, OH (United States). Materials Directorate
1997-12-31
This paper addresses the development of a system which will enable the optimization of an entire processing sequence for a forged part. Typically such a sequence may involve several stages and alternative routes of manufacturing a given part. It is important that such a system be optimized globally, (rather than locally, as is the current practice) in order to achieve improvements in affordability, producibility, and performance. This paper demonstrates the development of a simplified forging model, discussion techniques for searching and reducing a very large design space, and an objective function to evaluate the cost of a design sequence.
On the role of modeling parameters in IMRT plan optimization
International Nuclear Information System (INIS)
Krause, Michael; Scherrer, Alexander; Thieke, Christian
2008-01-01
The formulation of optimization problems in intensity-modulated radiotherapy (IMRT) planning comprises the choice of various values such as function-specific parameters or constraint bounds. In current inverse planning programs that yield a single treatment plan for each optimization, it is often unclear how strongly these modeling parameters affect the resulting plan. This work investigates the mathematical concepts of elasticity and sensitivity to deal with this problem. An artificial planning case with a horse-shoe formed target with different opening angles surrounding a circular risk structure is studied. As evaluation functions the generalized equivalent uniform dose (EUD) and the average underdosage below and average overdosage beyond certain dose thresholds are used. A single IMRT plan is calculated for an exemplary parameter configuration. The elasticity and sensitivity of each parameter are then calculated without re-optimization, and the results are numerically verified. The results show the following. (1) elasticity can quantify the influence of a modeling parameter on the optimization result in terms of how strongly the objective function value varies under modifications of the parameter value. It also can describe how strongly the geometry of the involved planning structures affects the optimization result. (2) Based on the current parameter settings and corresponding treatment plan, sensitivity analysis can predict the optimization result for modified parameter values without re-optimization, and it can estimate the value intervals in which such predictions are valid. In conclusion, elasticity and sensitivity can provide helpful tools in inverse IMRT planning to identify the most critical parameters of an individual planning problem and to modify their values in an appropriate way
Capital Cost Optimization for Prefabrication: A Factor Analysis Evaluation Model
Directory of Open Access Journals (Sweden)
Hong Xue
2018-01-01
Full Text Available High capital cost is a significant hindrance to the promotion of prefabrication. In order to optimize cost management and reduce capital cost, this study aims to explore the latent factors and factor analysis evaluation model. Semi-structured interviews were conducted to explore potential variables and then questionnaire survey was employed to collect professionals’ views on their effects. After data collection, exploratory factor analysis was adopted to explore the latent factors. Seven latent factors were identified, including “Management Index”, “Construction Dissipation Index”, “Productivity Index”, “Design Efficiency Index”, “Transport Dissipation Index”, “Material increment Index” and “Depreciation amortization Index”. With these latent factors, a factor analysis evaluation model (FAEM, divided into factor analysis model (FAM and comprehensive evaluation model (CEM, was established. The FAM was used to explore the effect of observed variables on the high capital cost of prefabrication, while the CEM was used to evaluate comprehensive cost management level on prefabrication projects. Case studies were conducted to verify the models. The results revealed that collaborative management had a positive effect on capital cost of prefabrication. Material increment costs and labor costs had significant impacts on production cost. This study demonstrated the potential of on-site management and standardization design to reduce capital cost. Hence, collaborative management is necessary for cost management of prefabrication. Innovation and detailed design were needed to improve cost performance. The new form of precast component factories can be explored to reduce transportation cost. Meanwhile, targeted strategies can be adopted for different prefabrication projects. The findings optimized the capital cost and improved the cost performance through providing an evaluation and optimization model, which helps managers to
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
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.
Roll levelling semi-analytical model for process optimization
Silvestre, E.; Garcia, D.; Galdos, L.; Saenz de Argandoña, E.; Mendiguren, J.
2016-08-01
Roll levelling is a primary manufacturing process used to remove residual stresses and imperfections of metal strips in order to make them suitable for subsequent forming operations. In the last years the importance of this process has been evidenced with the apparition of Ultra High Strength Steels with strength > 900 MPa. The optimal setting of the machine as well as a robust machine design has become critical for the correct processing of these materials. Finite Element Method (FEM) analysis is the widely used technique for both aspects. However, in this case, the FEM simulation times are above the admissible ones in both machine development and process optimization. In the present work, a semi-analytical model based on a discrete bending theory is presented. This model is able to calculate the critical levelling parameters i.e. force, plastification rate, residual stresses in a few seconds. First the semi-analytical model is presented. Next, some experimental industrial cases are analyzed by both the semi-analytical model and the conventional FEM model. Finally, results and computation times of both methods are compared.
Optimizing ZigBee Security using Stochastic Model Checking
DEFF Research Database (Denmark)
Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming
ZigBee is a fairly new but promising wireless sensor network standard that offers the advantages of simple and low resource communication. Nevertheless, security is of great concern to ZigBee, and enhancements are prescribed in the latest ZigBee specication: ZigBee-2007. In this technical report......, we identify an important gap in the specification on key updates, and present a methodology for determining optimal key update policies and security parameters. We exploit the stochastic model checking approach using the probabilistic model checker PRISM, and assess the security needs for realistic...
Optimizing Markovian modeling of chaotic systems with recurrent neural networks
International Nuclear Information System (INIS)
Cechin, Adelmo L.; Pechmann, Denise R.; Oliveira, Luiz P.L. de
2008-01-01
In this paper, we propose a methodology for optimizing the modeling of an one-dimensional chaotic time series with a Markov Chain. The model is extracted from a recurrent neural network trained for the attractor reconstructed from the data set. Each state of the obtained Markov Chain is a region of the reconstructed state space where the dynamics is approximated by a specific piecewise linear map, obtained from the network. The Markov Chain represents the dynamics of the time series in its statistical essence. An application to a time series resulted from Lorenz system is included
Error Modeling and Design Optimization of Parallel Manipulators
DEFF Research Database (Denmark)
Wu, Guanglei
challenges due to their highly nonlinear behaviors, thus, the parameter and performance analysis, especially the accuracy and stiness, are particularly important. Toward the requirements of robotic technology such as light weight, compactness, high accuracy and low energy consumption, utilizing optimization...... theory and virtual spring approach, a general kinetostatic model of the spherical parallel manipulators is developed and validated with Finite Element approach. This model is applied to the stiness analysis of a special spherical parallel manipulator with unlimited rolling motion and the obtained stiness...
Optimal hedging with the cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Gatarek, Lukasz; Johansen, Søren
We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated...... with the hedged asset and among themselves. We nd that the minimum variance hedge for assets driven by the CVAR, depends strongly on the portfolio holding period. The hedge is dened as a function of correlation and cointegration parameters. For short holding periods the correlation impact is predominant. For long...... horizons, the hedge ratio should overweight the cointegration parameters rather then short-run correlation information. In the innite horizon, the hedge ratios shall be equal to the cointegrating vector. The hedge ratios for any intermediate portfolio holding period should be based on the weighted average...
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.
Autonomous guided vehicles methods and models for optimal path planning
Fazlollahtabar, Hamed
2015-01-01
This book provides readers with extensive information on path planning optimization for both single and multiple Autonomous Guided Vehicles (AGVs), and discusses practical issues involved in advanced industrial applications of AGVs. After discussing previously published research in the field and highlighting the current gaps, it introduces new models developed by the authors with the goal of reducing costs and increasing productivity and effectiveness in the manufacturing industry. The new models address the increasing complexity of manufacturing networks, due for example to the adoption of flexible manufacturing systems that involve automated material handling systems, robots, numerically controlled machine tools, and automated inspection stations, while also considering the uncertainty and stochastic nature of automated equipment such as AGVs. The book discusses and provides solutions to important issues concerning the use of AGVs in the manufacturing industry, including material flow optimization with A...
Sustainable logistics and transportation optimization models and algorithms
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.
Genetic Algorithm Optimization of Artificial Neural Networks for Hydrological Modelling
Abrahart, R. J.
2004-05-01
This paper will consider the case for genetic algorithm optimization in the development of an artificial neural network model. It will provide a methodological evaluation of reported investigations with respect to hydrological forecasting and prediction. The intention in such operations is to develop a superior modelling solution that will be: \\begin{itemize} more accurate in terms of output precision and model estimation skill; more tractable in terms of personal requirements and end-user control; and/or more robust in terms of conceptual and mechanical power with respect to adverse conditions. The genetic algorithm optimization toolbox could be used to perform a number of specific roles or purposes and it is the harmonious and supportive relationship between neural networks and genetic algorithms that will be highlighted and assessed. There are several neural network mechanisms and procedures that could be enhanced and potential benefits are possible at different stages in the design and construction of an operational hydrological model e.g. division of inputs; identification of structure; initialization of connection weights; calibration of connection weights; breeding operations between successful models; and output fusion associated with the development of ensemble solutions. Each set of opportunities will be discussed and evaluated. Two strategic questions will also be considered: [i] should optimization be conducted as a set of small individual procedures or as one large holistic operation; [ii] what specific function or set of weighted vectors should be optimized in a complex software product e.g. timings, volumes, or quintessential hydrological attributes related to the 'problem situation' - that might require the development flood forecasting, drought estimation, or record infilling applications. The paper will conclude with a consideration of hydrological forecasting solutions developed on the combined methodologies of co-operative co-evolution and
Thin film bulk acoustic wave devices : performance optimization and modeling
Pensala, Tuomas
2011-01-01
Thin film bulk acoustic wave (BAW) resonators and filters operating in the GHz range are used in mobile phones for the most demanding filtering applications and complement the surface acoustic wave (SAW) based filters. Their main advantages are small size and high performance at frequencies above 2 GHz. This work concentrates on the characterization, performance optimization, and modeling techniques of thin film BAW devices. Laser interferometric vibration measurements together with plat...
Modeling marine surface microplastic transport to assess optimal removal locations
Sherman, P; Van Sebille, E
2016-01-01
Marine plastic pollution is an ever-increasing problem that demands immediate mitigation and reduction plans. Here, a model based on satellite-tracked buoy observations and scaled to a large data set of observations on microplastic from surface trawls was used to simulate the transport of plastics floating on the ocean surface from 2015 to 2025, with the goal to assess the optimal marine microplastic removal locations for two scenarios: removing the most surface microplastic and reducing the ...
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Feipeng Guo; Qibei Lu
2013-01-01
With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic met...
Modeling, simulation and optimization for science and technology
Kuznetsov, Yuri; Neittaanmäki, Pekka; Pironneau, Olivier
2014-01-01
This volume contains thirteen articles on advances in applied mathematics and computing methods for engineering problems. Six papers are on optimization methods and algorithms with emphasis on problems with multiple criteria; four articles are on numerical methods for applied problems modeled with nonlinear PDEs; two contributions are on abstract estimates for error analysis; finally one paper deals with rare events in the context of uncertainty quantification. Applications include aerospace, glaciology and nonlinear elasticity. Herein is a selection of contributions from speakers at two conferences on applied mathematics held in June 2012 at the University of Jyväskylä, Finland. The first conference, “Optimization and PDEs with Industrial Applications” celebrated the seventieth birthday of Professor Jacques Périaux of the University of Jyväskylä and Polytechnic University of Catalonia (Barcelona Tech), and the second conference, “Optimization and PDEs with Applications” celebrated the seventy-fi...
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
The present thesis is about optimization of recurrent neural networks applied to time series modeling. In particular is considered fully recurrent networks working from only a single external input, one layer of nonlinear hidden units and a li near output unit applied to prediction of discrete time...... series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...
Mathematical model of the metal mould surface temperature optimization
International Nuclear Information System (INIS)
Mlynek, Jaroslav; Knobloch, Roman; Srb, Radek
2015-01-01
The article is focused on the problem of generating a uniform temperature field on the inner surface of shell metal moulds. Such moulds are used e.g. in the automotive industry for artificial leather production. To produce artificial leather with uniform surface structure and colour shade the temperature on the inner surface of the mould has to be as homogeneous as possible. The heating of the mould is realized by infrared heaters located above the outer mould surface. The conceived mathematical model allows us to optimize the locations of infrared heaters over the mould, so that approximately uniform heat radiation intensity is generated. A version of differential evolution algorithm programmed in Matlab development environment was created by the authors for the optimization process. For temperate calculations software system ANSYS was used. A practical example of optimization of heaters locations and calculation of the temperature of the mould is included at the end of the article
Fuel cycle optimization using the nonlinear reactivity model
International Nuclear Information System (INIS)
Yueksel, Z.; Cecen, Y.; Tombakoglu, M.
2002-01-01
Fuel cycle optimization is one of the key subjects of reactor operation. In this study, fuel cycles of Spectral Shift PWR and Pebble Bed HTGR are optimized by using nonlinear reactivity model. The Spectral Shift concept is based on the adjustments of fuel to moderator ratio as a function of burnup. For n-batch fuel cycle, where n is equal to 3 and 4, the fuel to moderator ratio is determined as a function of burnup to maximize discharge burnup, Bd. Results show that it is possible to increase discharge burnup up to 25 percent compared to typical commercial PWR designs. Another problem arises in the design of PB-HTGR's fuel pebbles and mixing ratio. The optimization of the composition of fuel pebbles and mixing ratio for direct and n-pass fuel cycles are analyzed to maximize discharge burnup. We compared our results with the current design parameters of HTR-10 and PBMR.(author)
Optimization model of vaccination strategy for dengue transmission
Widayani, H.; Kallista, M.; Nuraini, N.; Sari, M. Y.
2014-02-01
Dengue fever is emerging tropical and subtropical disease caused by dengue virus infection. The vaccination should be done as a prevention of epidemic in population. The host-vector model are modified with consider a vaccination factor to prevent the occurrence of epidemic dengue in a population. An optimal vaccination strategy using non-linear objective function was proposed. The genetic algorithm programming techniques are combined with fourth-order Runge-Kutta method to construct the optimal vaccination. In this paper, the appropriate vaccination strategy by using the optimal minimum cost function which can reduce the number of epidemic was analyzed. The numerical simulation for some specific cases of vaccination strategy is shown.
Optimization of a semiconductor manufacturing process using a reentrant model
Directory of Open Access Journals (Sweden)
Sarah Abuhab Valente
2015-01-01
Full Text Available The scope of this work is the simulation of a semiconductor manufacturing model in Arena® software and subsequent optimization and sensitivity analysis of this model. The process is considered extremely complex given the amount of steps, machinery, parameters, and highly reentrant characteristics, which makes it difficult to reach stability of production process. The production model used was the Intel Five-Machine Six-Step Mini-fab developed by Karl Kempf (1994. It was programmed in Arena® and optimized by OptQuest®, an add-on. We concluded that variation in the number of machines and operators reflects on cycle time only if there is an increase of one unit of resource more than obtained in the optimization. As a result, we highlighted the scenario where a reduction in cycle time stood out, in which one extra unit was added in the second machine group, representing a 7.41% reduction in cycle time.
MENENTUKAN PORTOFOLIO OPTIMAL MENGGUNAKAN MODEL CONDITIONAL MEAN VARIANCE
Directory of Open Access Journals (Sweden)
I GEDE ERY NISCAHYANA
2016-08-01
Full Text Available When the returns of stock prices show the existence of autocorrelation and heteroscedasticity, then conditional mean variance models are suitable method to model the behavior of the stocks. In this thesis, the implementation of the conditional mean variance model to the autocorrelated and heteroscedastic return was discussed. The aim of this thesis was to assess the effect of the autocorrelated and heteroscedastic returns to the optimal solution of a portfolio. The margin of four stocks, Fortune Mate Indonesia Tbk (FMII.JK, Bank Permata Tbk (BNLI.JK, Suryamas Dutamakmur Tbk (SMDM.JK dan Semen Gresik Indonesia Tbk (SMGR.JK were estimated by GARCH(1,1 model with standard innovations following the standard normal distribution and the t-distribution. The estimations were used to construct a portfolio. The portfolio optimal was found when the standard innovation used was t-distribution with the standard deviation of 1.4532 and the mean of 0.8023 consisting of 0.9429 (94% of FMII stock, 0.0473 (5% of BNLI stock, 0% of SMDM stock, 1% of SMGR stock.
Maintenance modeling and optimization integrating human and material resources
Energy Technology Data Exchange (ETDEWEB)
Martorell, S., E-mail: smartore@iqn.upv.e [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Villamizar, M.; Carlos, S. [Dpto. Ingenieria Quimica y Nuclear, Universidad Politecnica Valencia (Spain); Sanchez, A. [Dpto. Estadistica e Investigacion Operativa Aplicadas y Calidad, Universidad Politecnica Valencia (Spain)
2010-12-15
Maintenance planning is a subject of concern to many industrial sectors as plant safety and business depend on it. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization (MOP) problem where reliability, availability, maintainability and cost (RAM+C) act as decision criteria and maintenance strategies (i.e. maintenance tasks intervals) act as the only decision variables. However the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources (human and material) available to implement such strategies. Thus, the effect of the necessary resources on RAM+C needs to be modeled and accounted for in formulating the MOP affecting the set of objectives and constraints. In this paper RAM+C models to explicitly address the effect of human resources and material resources (spare parts) on RAM+C criteria are proposed. This extended model allows accounting for explicitly how the above decision criteria depends on the basic model parameters representing the type of strategies, maintenance intervals, durations, human resources and material resources. Finally, an application case is performed to optimize the maintenance plan of a motor-driven pump equipment considering as decision variables maintenance and test intervals and human and material resources.
Validation, Optimization and Simulation of a Solar Thermoelectric Generator Model
Madkhali, Hadi Ali; Hamil, Ali; Lee, HoSung
2017-12-01
This study explores thermoelectrics as a viable option for small-scale solar thermal applications. Thermoelectric technology is based on the Seebeck effect, which states that a voltage is induced when a temperature gradient is applied to the junctions of two differing materials. This research proposes to analyze, validate, simulate, and optimize a prototype solar thermoelectric generator (STEG) model in order to increase efficiency. The intent is to further develop STEGs as a viable and productive energy source that limits pollution and reduces the cost of energy production. An empirical study (Kraemer et al. in Nat Mater 10:532, 2011) on the solar thermoelectric generator reported a high efficiency performance of 4.6%. The system had a vacuum glass enclosure, a flat panel (absorber), thermoelectric generator and water circulation for the cold side. The theoretical and numerical approach of this current study validated the experimental results from Kraemer's study to a high degree. The numerical simulation process utilizes a two-stage approach in ANSYS software for Fluent and Thermal-Electric Systems. The solar load model technique uses solar radiation under AM 1.5G conditions in Fluent. This analytical model applies Dr. Ho Sung Lee's theory of optimal design to improve the performance of the STEG system by using dimensionless parameters. Applying this theory, using two cover glasses and radiation shields, the STEG model can achieve a highest efficiency of 7%.
Maintenance modeling and optimization integrating human and material resources
International Nuclear Information System (INIS)
Martorell, S.; Villamizar, M.; Carlos, S.; Sanchez, A.
2010-01-01
Maintenance planning is a subject of concern to many industrial sectors as plant safety and business depend on it. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization (MOP) problem where reliability, availability, maintainability and cost (RAM+C) act as decision criteria and maintenance strategies (i.e. maintenance tasks intervals) act as the only decision variables. However the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources (human and material) available to implement such strategies. Thus, the effect of the necessary resources on RAM+C needs to be modeled and accounted for in formulating the MOP affecting the set of objectives and constraints. In this paper RAM+C models to explicitly address the effect of human resources and material resources (spare parts) on RAM+C criteria are proposed. This extended model allows accounting for explicitly how the above decision criteria depends on the basic model parameters representing the type of strategies, maintenance intervals, durations, human resources and material resources. Finally, an application case is performed to optimize the maintenance plan of a motor-driven pump equipment considering as decision variables maintenance and test intervals and human and material resources.
Optimization of Regional Geodynamic Models for Mantle Dynamics
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.
Optimal Filtering in Mass Transport Modeling From Satellite Gravimetry Data
Ditmar, P.; Hashemi Farahani, H.; Klees, R.
2011-12-01
Monitoring natural mass transport in the Earth's system, which has marked a new era in Earth observation, is largely based on the data collected by the GRACE satellite mission. Unfortunately, this mission is not free from certain limitations, two of which are especially critical. Firstly, its sensitivity is strongly anisotropic: it senses the north-south component of the mass re-distribution gradient much better than the east-west component. Secondly, it suffers from a trade-off between temporal and spatial resolution: a high (e.g., daily) temporal resolution is only possible if the spatial resolution is sacrificed. To make things even worse, the GRACE satellites enter occasionally a phase when their orbit is characterized by a short repeat period, which makes it impossible to reach a high spatial resolution at all. A way to mitigate limitations of GRACE measurements is to design optimal data processing procedures, so that all available information is fully exploited when modeling mass transport. This implies, in particular, that an unconstrained model directly derived from satellite gravimetry data needs to be optimally filtered. In principle, this can be realized with a Wiener filter, which is built on the basis of covariance matrices of noise and signal. In practice, however, a compilation of both matrices (and, therefore, of the filter itself) is not a trivial task. To build the covariance matrix of noise in a mass transport model, it is necessary to start from a realistic model of noise in the level-1B data. Furthermore, a routine satellite gravimetry data processing includes, in particular, the subtraction of nuisance signals (for instance, associated with atmosphere and ocean), for which appropriate background models are used. Such models are not error-free, which has to be taken into account when the noise covariance matrix is constructed. In addition, both signal and noise covariance matrices depend on the type of mass transport processes under
Fractional and multivariable calculus model building and optimization problems
Mathai, A M
2017-01-01
This textbook presents a rigorous approach to multivariable calculus in the context of model building and optimization problems. This comprehensive overview is based on lectures given at five SERC Schools from 2008 to 2012 and covers a broad range of topics that will enable readers to understand and create deterministic and nondeterministic models. Researchers, advanced undergraduate, and graduate students in mathematics, statistics, physics, engineering, and biological sciences will find this book to be a valuable resource for finding appropriate models to describe real-life situations. The first chapter begins with an introduction to fractional calculus moving on to discuss fractional integrals, fractional derivatives, fractional differential equations and their solutions. Multivariable calculus is covered in the second chapter and introduces the fundamentals of multivariable calculus (multivariable functions, limits and continuity, differentiability, directional derivatives and expansions of multivariable ...
Model checks for Cox-type regression models based on optimally weighted martingale residuals.
Gandy, Axel; Jensen, Uwe
2009-12-01
We introduce directed goodness-of-fit tests for Cox-type regression models in survival analysis. "Directed" means that one may choose against which alternatives the tests are particularly powerful. The tests are based on sums of weighted martingale residuals and their asymptotic distributions.We derive optimal tests against certain competing models which include Cox-type regression models with different covariates and/or a different link function. We report results from several simulation studies and apply our test to a real dataset.
Keesman, K.J.; Walter, E.
2014-01-01
The paper presents a methodology for an optimal input design for model discrimination. To allow analytical solutions, the method, using Pontryagin’s maximum principle, is developed for non-linear single-state systems that are affine in their joint input. The method is demonstrated on a fed-batch
Optimization Model for Web Based Multimodal Interactive Simulations.
Halic, Tansel; Ahn, Woojin; De, Suvranu
2015-07-15
This paper presents a technique for optimizing the performance of web based multimodal interactive simulations. For such applications where visual quality and the performance of simulations directly influence user experience, overloading of hardware resources may result in unsatisfactory reduction in the quality of the simulation and user satisfaction. However, optimization of simulation performance on individual hardware platforms is not practical. Hence, we present a mixed integer programming model to optimize the performance of graphical rendering and simulation performance while satisfying application specific constraints. Our approach includes three distinct phases: identification, optimization and update . In the identification phase, the computing and rendering capabilities of the client device are evaluated using an exploratory proxy code. This data is utilized in conjunction with user specified design requirements in the optimization phase to ensure best possible computational resource allocation. The optimum solution is used for rendering (e.g. texture size, canvas resolution) and simulation parameters (e.g. simulation domain) in the update phase. Test results are presented on multiple hardware platforms with diverse computing and graphics capabilities to demonstrate the effectiveness of our approach.
Transfer prices assignment with integrated production and marketing optimization models
Directory of Open Access Journals (Sweden)
Enrique Parra
2018-04-01
Full Text Available Purpose: In decentralized organizations (today a great majority of the large multinational groups, much of the decision-making power is in its individual business units-BUs-. In these cases, the management control system (MCS uses transfer prices to coordinate actions of the BUs and to evaluate their performance with the goal of guaranteeing the whole corporation optimum. The purpose of the investigation is to design transfer prices that suit this goal. Design/methodology/approach: Considering the results of the whole company supply chain optimization models (in the presence of seasonality of demand the question is to design a mechanism that creates optimal incentives for the managers of each business unit to drive the corporation to the optimal performance. Mathematical programming models are used as a start point. Findings: Different transfer prices computation methods are introduced in this paper for decentralised organizations with two divisions (production and marketing. The methods take into account the results of the solution of the whole company supply chain optimization model, if exists, and can be adapted to the type of information available in the company. It is mainly focused on transport costs assignment. Practical implications: Using the methods proposed in this paper a decentralized corporation can implement more accurate transfer prices to drive the whole organization to the global optimum performance. Originality/value: The methods proposed are a new contribution to the literature on transfer prices with special emphasis on the practical and easy implementation in a modern corporation with several business units and with high seasonality of demand. Also, the methods proposed are very flexible and can be tuned depending on the type of information available in the company.
Numerical modeling and optimization of machining duplex stainless steels
Directory of Open Access Journals (Sweden)
Rastee D. Koyee
2015-01-01
Full Text Available The shortcomings of the machining analytical and empirical models in combination with the industry demands have to be fulfilled. A three-dimensional finite element modeling (FEM introduces an attractive alternative to bridge the gap between pure empirical and fundamental scientific quantities, and fulfill the industry needs. However, the challenging aspects which hinder the successful adoption of FEM in the machining sector of manufacturing industry have to be solved first. One of the greatest challenges is the identification of the correct set of machining simulation input parameters. This study presents a new methodology to inversely calculate the input parameters when simulating the machining of standard duplex EN 1.4462 and super duplex EN 1.4410 stainless steels. JMatPro software is first used to model elastic–viscoplastic and physical work material behavior. In order to effectively obtain an optimum set of inversely identified friction coefficients, thermal contact conductance, Cockcroft–Latham critical damage value, percentage reduction in flow stress, and Taylor–Quinney coefficient, Taguchi-VIKOR coupled with Firefly Algorithm Neural Network System is applied. The optimization procedure effectively minimizes the overall differences between the experimentally measured performances such as cutting forces, tool nose temperature and chip thickness, and the numerically obtained ones at any specified cutting condition. The optimum set of input parameter is verified and used for the next step of 3D-FEM application. In the next stage of the study, design of experiments, numerical simulations, and fuzzy rule modeling approaches are employed to optimize types of chip breaker, insert shapes, process conditions, cutting parameters, and tool orientation angles based on many important performances. Through this study, not only a new methodology in defining the optimal set of controllable parameters for turning simulations is introduced, but also
WE-D-BRE-04: Modeling Optimal Concurrent Chemotherapy Schedules
International Nuclear Information System (INIS)
Jeong, J; Deasy, J O
2014-01-01
Purpose: Concurrent chemo-radiation therapy (CCRT) has become a more common cancer treatment option with a better tumor control rate for several tumor sites, including head and neck and lung cancer. In this work, possible optimal chemotherapy schedules were investigated by implementing chemotherapy cell-kill into a tumor response model of RT. Methods: The chemotherapy effect has been added into a published model (Jeong et al., PMB (2013) 58:4897), in which the tumor response to RT can be simulated with the effects of hypoxia and proliferation. Based on the two-compartment pharmacokinetic model, the temporal concentration of chemotherapy agent was estimated. Log cell-kill was assumed and the cell-kill constant was estimated from the observed increase in local control due to concurrent chemotherapy. For a simplified two cycle CCRT regime, several different starting times and intervals were simulated with conventional RT regime (2Gy/fx, 5fx/wk). The effectiveness of CCRT was evaluated in terms of reduction in radiation dose required for 50% of control to find the optimal chemotherapy schedule. Results: Assuming the typical slope of dose response curve (γ50=2), the observed 10% increase in local control rate was evaluated to be equivalent to an extra RT dose of about 4 Gy, from which the cell-kill rate of chemotherapy was derived to be about 0.35. Best response was obtained when chemotherapy was started at about 3 weeks after RT began. As the interval between two cycles decreases, the efficacy of chemotherapy increases with broader range of optimal starting times. Conclusion: The effect of chemotherapy has been implemented into the resource-conservation tumor response model to investigate CCRT. The results suggest that the concurrent chemotherapy might be more effective when delayed for about 3 weeks, due to lower tumor burden and a larger fraction of proliferating cells after reoxygenation
Sitting biomechanics, part II: optimal car driver's seat and optimal driver's spinal model.
Harrison, D D; Harrison, S O; Croft, A C; Harrison, D E; Troyanovich, S J
2000-01-01
Driving has been associated with signs and symptoms caused by vibrations. Sitting causes the pelvis to rotate backwards and the lumbar lordosis to reduce. Lumbar support and armrests reduce disc pressure and electromyographically recorded values. However, the ideal driver's seat and an optimal seated spinal model have not been described. To determine an optimal automobile seat and an ideal spinal model of a driver. Information was obtained from peer-reviewed scientific journals and texts, automotive engineering reports, and the National Library of Medicine. Driving predisposes vehicle operators to low-back pain and degeneration. The optimal seat would have an adjustable seat back incline of 100 degrees from horizontal, a changeable depth of seat back to front edge of seat bottom, adjustable height, an adjustable seat bottom incline, firm (dense) foam in the seat bottom cushion, horizontally and vertically adjustable lumbar support, adjustable bilateral arm rests, adjustable head restraint with lordosis pad, seat shock absorbers to dampen frequencies in the 1 to 20 Hz range, and linear front-back travel of the seat enabling drivers of all sizes to reach the pedals. The lumbar support should be pulsating in depth to reduce static load. The seat back should be damped to reduce rebounding of the torso in rear-end impacts. The optimal driver's spinal model would be the average Harrison model in a 10 degrees posterior inclining seat back angle.
Modeling and optimization of a hybrid solar combined cycle (HYCS)
Eter, Ahmad Adel
2011-12-01
The main objective of this thesis is to investigate the feasibility of integrating concentrated solar power (CSP) technology with the conventional combined cycle technology for electric generation in Saudi Arabia. The generated electricity can be used locally to meet the annual increasing demand. Specifically, it can be utilized to meet the demand during the hours 10 am-3 pm and prevent blackout hours, of some industrial sectors. The proposed CSP design gives flexibility in the operation system. Since, it works as a conventional combined cycle during night time and it switches to work as a hybrid solar combined cycle during day time. The first objective of the thesis is to develop a thermo-economical mathematical model that can simulate the performance of a hybrid solar-fossil fuel combined cycle. The second objective is to develop a computer simulation code that can solve the thermo-economical mathematical model using available software such as E.E.S. The developed simulation code is used to analyze the thermo-economic performance of different configurations of integrating the CSP with the conventional fossil fuel combined cycle to achieve the optimal integration configuration. This optimal integration configuration has been investigated further to achieve the optimal design of the solar field that gives the optimal solar share. Thermo-economical performance metrics which are available in the literature have been used in the present work to assess the thermo-economic performance of the investigated configurations. The economical and environmental impact of integration CSP with the conventional fossil fuel combined cycle are estimated and discussed. Finally, the optimal integration configuration is found to be solarization steam side in conventional combined cycle with solar multiple 0.38 which needs 29 hectare and LEC of HYCS is 63.17 $/MWh under Dhahran weather conditions.
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.
Modeling, hybridization, and optimal charging of electrical energy storage systems
Parvini, Yasha
The rising rate of global energy demand alongside the dwindling fossil fuel resources has motivated research for alternative and sustainable solutions. Within this area of research, electrical energy storage systems are pivotal in applications including electrified vehicles, renewable power generation, and electronic devices. The approach of this dissertation is to elucidate the bottlenecks of integrating supercapacitors and batteries in energy systems and propose solutions by the means of modeling, control, and experimental techniques. In the first step, the supercapacitor cell is modeled in order to gain fundamental understanding of its electrical and thermal dynamics. The dependence of electrical parameters on state of charge (SOC), current direction and magnitude (20-200 A), and temperatures ranging from -40°C to 60°C was embedded in this computationally efficient model. The coupled electro-thermal model was parameterized using specifically designed temporal experiments and then validated by the application of real world duty cycles. Driving range is one of the major challenges of electric vehicles compared to combustion vehicles. In order to shed light on the benefits of hybridizing a lead-acid driven electric vehicle via supercapacitors, a model was parameterized for the lead-acid battery and combined with the model already developed for the supercapacitor, to build the hybrid battery-supercapacitor model. A hardware in the loop (HIL) setup consisting of a custom built DC/DC converter, micro-controller (muC) to implement the power management strategy, 12V lead-acid battery, and a 16.2V supercapacitor module was built to perform the validation experiments. Charging electrical energy storage systems in an efficient and quick manner, motivated to solve an optimal control problem with the objective of maximizing the charging efficiency for supercapacitors, lead-acid, and lithium ion batteries. Pontryagins minimum principle was used to solve the problems
Transport Routes Optimization Model Through Application of Fuzzy Logic
Directory of Open Access Journals (Sweden)
Ivan Bortas
2018-03-01
Full Text Available The transport policy of the European Union is based on the mission of restructuring road traffic into other and energy-favourable transport modes which have not been sufficiently represented yet. Therefore, the development of the inland waterway and rail transport, and connectivity in the intermodal transport network are development planning priorities of the European transport strategy. The aim of this research study was to apply the scientific methodology and thus analyse the factors that affect the distribution of the goods flows and by using the fuzzy logic to make an optimization model, according to the criteria of minimizing the costs and negative impact on the environment, for the selection of the optimal transport route. Testing of the model by simulation, was performed on the basis of evaluating the criteria of the influential parameters with unprecise and indefinite input parameters. The testing results show that by the distribution of the goods flow from road transport network to inland waterways or rail transport, can be predicted in advance and determine the transport route with optimal characteristics. The results of the performed research study will be used to improve the process of planning the transport service, with the aim of reducing the transport costs and environmental pollution.
Protein homology model refinement by large-scale energy optimization.
Park, Hahnbeom; Ovchinnikov, Sergey; Kim, David E; DiMaio, Frank; Baker, David
2018-03-20
Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.
Traveling waves in an optimal velocity model of freeway traffic
Berg, Peter; Woods, Andrew
2001-03-01
Car-following models provide both a tool to describe traffic flow and algorithms for autonomous cruise control systems. Recently developed optimal velocity models contain a relaxation term that assigns a desirable speed to each headway and a response time over which drivers adjust to optimal velocity conditions. These models predict traffic breakdown phenomena analogous to real traffic instabilities. In order to deepen our understanding of these models, in this paper, we examine the transition from a linear stable stream of cars of one headway into a linear stable stream of a second headway. Numerical results of the governing equations identify a range of transition phenomena, including monotonic and oscillating travelling waves and a time- dependent dispersive adjustment wave. However, for certain conditions, we find that the adjustment takes the form of a nonlinear traveling wave from the upstream headway to a third, intermediate headway, followed by either another traveling wave or a dispersive wave further downstream matching the downstream headway. This intermediate value of the headway is selected such that the nonlinear traveling wave is the fastest stable traveling wave which is observed to develop in the numerical calculations. The development of these nonlinear waves, connecting linear stable flows of two different headways, is somewhat reminiscent of stop-start waves in congested flow on freeways. The different types of adjustments are classified in a phase diagram depending on the upstream and downstream headway and the response time of the model. The results have profound consequences for autonomous cruise control systems. For an autocade of both identical and different vehicles, the control system itself may trigger formations of nonlinear, steep wave transitions. Further information is available [Y. Sugiyama, Traffic and Granular Flow (World Scientific, Singapore, 1995), p. 137].
Optimizing DNA assembly based on statistical language modelling.
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.
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.
Optimization models using fuzzy sets and possibility theory
Orlovski, S
1987-01-01
Optimization is of central concern to a number of discip lines. Operations Research and Decision Theory are often consi dered to be identical with optimizationo But also in other areas such as engineering design, regional policy, logistics and many others, the search for optimal solutions is one of the prime goals. The methods and models which have been used over the last decades in these areas have primarily been "hard" or "crisp", i. e. the solutions were considered to be either fea sible or unfeasible, either above a certain aspiration level or below. This dichotomous structure of methods very often forced the modeller to approximate real problem situations of the more-or-less type by yes-or-no-type models, the solutions of which might turn out not to be the solutions to the real prob lems. This is particularly true if the problem under considera tion includes vaguely defined relationships, human evaluations, uncertainty due to inconsistent or incomplete evidence, if na tural language has to be...
An optimal decision making model for supporting week hospital management.
Conforti, Domenico; Guerriero, Francesca; Guido, Rosita; Cerinic, Marco Matucci; Conforti, Maria Letizia
2011-03-01
Week Hospital is an innovative inpatient health care organization and management, by which hospital stay services are planned in advance and delivered on week-time basis to elective patients. In this context, a strategic decision is the optimal clinical management of patients, and, in particular, devising efficient and effective admission and scheduling procedures, by tackling different requirements such as beds' availability, diagnostic resources, and treatment capabilities. The main aim is to maximize the patient flow, by ensuring the delivery of all clinical services during the week. In this paper, the optimal management of Week Hospital patients is considered. We have developed and validated an innovative integer programming model, based on clinical resources allocation and beds utilization. In particular, the model aims at scheduling Week Hospital patients' admission/discharge, possibly reducing the length of stay on the basis of an available timetable of clinical services. The performance of the model has been evaluated, in terms of efficiency and robustness, by considering real data coming from a Week Hospital Rheumatology Division. The experimental results have been satisfactory and demonstrate the effectiveness of the proposed approach.
Optimization of benzoxazinones as natural herbicide models by lipophilicity enhancement.
Macías, Francisco A; Marín, David; Oliveros-Bastidas, Alberto; Molinillo, José M G
2006-12-13
Benzoxazinones are plant allelochemicals well-known for their phytotoxic activity and for taking part in the defense strategies of Gramineae, Ranunculaceae, and Scrophulariceae plants. These properties, in addition to the recently optimized methodologies for their large-scale isolation and synthesis, have made some derivatives of natural products, 2,4-dihydroxy-(2H)-1,4-benzoxazin-3-(4H)-one (DIBOA) and its 7-methoxy analogue (DIMBOA), successful templates in the search for natural herbicide models. These new chemicals should be part of integrated methodologies for weed control. In ongoing research about the structure-activity relationships of benzoxazinones and the structural requirements for their phytotoxicity enhancement and after characterization of the optimal structural features, a new generation of chemicals with enhanced lipophilicity was developed. They were tested on selected standard target species and weeds in the search for the optimal aqueous solubility-lipophilicity rate for phytotoxicity. This physical parameter is known to be crucial in modern drug and agrochemical design strategies. The new compounds obtained in this way had interesting phytotoxicity profiles, empowering the phytotoxic effect of the starting benzoxazinone template in some cases. Quantitative structure-activity relationships were obtained by bioactivity-molecular parameters correlations. Because optimal lipophilicity values for phytotoxicity vary with the tested plant, these new derivatives constitute a more selective way to take advantage of benzoxazinone phytotoxic capabilities.
Vehicle Propulsion Systems Introduction to Modeling and Optimization
Guzzella, Lino
2013-01-01
This text provides an introduction to the mathematical modeling and subsequent optimization of vehicle propulsion systems and their supervisory control algorithms. Automobiles are responsible for a substantial part of the world's consumption of primary energy, mostly fossil liquid hydrocarbons and the reduction of the fuel consumption of these vehicles has become a top priority. Increasing concerns over fossil fuel consumption and the associated environmental impacts have motivated many groups in industry and academia to propose new propulsion systems and to explore new optimization methodologies. This third edition has been prepared to include many of these developments. In the third edition, exercises are included at the end of each chapter and the solutions are available on the web.
Using Cotton Model Simulations to Estimate Optimally Profitable Irrigation Strategies
Mauget, S. A.; Leiker, G.; Sapkota, P.; Johnson, J.; Maas, S.
2011-12-01
In recent decades irrigation pumping from the Ogallala Aquifer has led to declines in saturated thickness that have not been compensated for by natural recharge, which has led to questions about the long-term viability of agriculture in the cotton producing areas of west Texas. Adopting irrigation management strategies that optimize profitability while reducing irrigation waste is one way of conserving the aquifer's water resource. Here, a database of modeled cotton yields generated under drip and center pivot irrigated and dryland production scenarios is used in a stochastic dominance analysis that identifies such strategies under varying commodity price and pumping cost conditions. This database and analysis approach will serve as the foundation for a web-based decision support tool that will help producers identify optimal irrigation treatments under specified cotton price, electricity cost, and depth to water table conditions.
Optimal difference-based estimation for partially linear models
Zhou, Yuejin
2017-12-16
Difference-based methods have attracted increasing attention for analyzing partially linear models in the recent literature. In this paper, we first propose to solve the optimal sequence selection problem in difference-based estimation for the linear component. To achieve the goal, a family of new sequences and a cross-validation method for selecting the adaptive sequence are proposed. We demonstrate that the existing sequences are only extreme cases in the proposed family. Secondly, we propose a new estimator for the residual variance by fitting a linear regression method to some difference-based estimators. Our proposed estimator achieves the asymptotic optimal rate of mean squared error. Simulation studies also demonstrate that our proposed estimator performs better than the existing estimator, especially when the sample size is small and the nonparametric function is rough.
Optimal experiment design for identification of grey-box models
DEFF Research Database (Denmark)
Sadegh, Payman; Melgaard, Henrik; Madsen, Henrik
1994-01-01
Optimal experiment design is investigated for stochastic dynamic systems where the prior partial information about the system is given as a probability distribution function in the system parameters. The concept of information is related to entropy reduction in the system through Lindley's measure...... estimation results in a considerable reduction of the experimental length. Besides, it is established that the physical knowledge of the system enables us to design experiments, with the goal of maximizing information about the physical parameters of interest....... of average information, and the relationship between the choice of information related criteria and some estimators (MAP and MLE) is established. A continuous time physical model of the heat dynamics of a building is considered and the results show that performing an optimal experiment corresponding to a MAP...
Optimal dividends in the Brownian motion risk model with interest
Fang, Ying; Wu, Rong
2009-07-01
In this paper, we consider a Brownian motion risk model, and in addition, the surplus earns investment income at a constant force of interest. The objective is to find a dividend policy so as to maximize the expected discounted value of dividend payments. It is well known that optimality is achieved by using a barrier strategy for unrestricted dividend rate. However, ultimate ruin of the company is certain if a barrier strategy is applied. In many circumstances this is not desirable. This consideration leads us to impose a restriction on the dividend stream. We assume that dividends are paid to the shareholders according to admissible strategies whose dividend rate is bounded by a constant. Under this additional constraint, we show that the optimal dividend strategy is formed by a threshold strategy.
Learning optimized features for hierarchical models of invariant object recognition.
Wersing, Heiko; Körner, Edgar
2003-07-01
There is an ongoing debate over the capabilities of hierarchical neural feedforward architectures for performing real-world invariant object recognition. Although a variety of hierarchical models exists, appropriate supervised and unsupervised learning methods are still an issue of intense research. We propose a feedforward model for recognition that shares components like weight sharing, pooling stages, and competitive nonlinearities with earlier approaches but focuses on new methods for learning optimal feature-detecting cells in intermediate stages of the hierarchical network. We show that principles of sparse coding, which were previously mostly applied to the initial feature detection stages, can also be employed to obtain optimized intermediate complex features. We suggest a new approach to optimize the learning of sparse features under the constraints of a weight-sharing or convolutional architecture that uses pooling operations to achieve gradual invariance in the feature hierarchy. The approach explicitly enforces symmetry constraints like translation invariance on the feature set. This leads to a dimension reduction in the search space of optimal features and allows determining more efficiently the basis representatives, which achieve a sparse decomposition of the input. We analyze the quality of the learned feature representation by investigating the recognition performance of the resulting hierarchical network on object and face databases. We show that a hierarchy with features learned on a single object data set can also be applied to face recognition without parameter changes and is competitive with other recent machine learning recognition approaches. To investigate the effect of the interplay between sparse coding and processing nonlinearities, we also consider alternative feedforward pooling nonlinearities such as presynaptic maximum selection and sum-of-squares integration. The comparison shows that a combination of strong competitive
Generalized PSF modeling for optimized quantitation in PET imaging
Ashrafinia, Saeed; Mohy-ud-Din, Hassan; Karakatsanis, Nicolas A.; Jha, Abhinav K.; Casey, Michael E.; Kadrmas, Dan J.; Rahmim, Arman
2017-06-01
modeling does not offer optimized PET quantitation, and that PSF overestimation may provide enhanced SUV quantitation. Furthermore, generalized PSF modeling may provide a valuable approach for quantitative tasks such as treatment-response assessment and prognostication.
Off-road vehicle dynamics analysis, modelling and optimization
Taghavifar, Hamid
2017-01-01
This book deals with the analysis of off-road vehicle dynamics from kinetics and kinematics perspectives and the performance of vehicle traversing over rough and irregular terrain. The authors consider the wheel performance, soil-tire interactions and their interface, tractive performance of the vehicle, ride comfort, stability over maneuvering, transient and steady state conditions of the vehicle traversing, modeling the aforementioned aspects and optimization from energetic and vehicle mobility perspectives. This book brings novel figures for the transient dynamics and original wheel terrain dynamics at on-the-go condition.
Modeling and optimization of parallel and distributed embedded systems
Munir, Arslan; Ranka, Sanjay
2016-01-01
This book introduces the state-of-the-art in research in parallel and distributed embedded systems, which have been enabled by developments in silicon technology, micro-electro-mechanical systems (MEMS), wireless communications, computer networking, and digital electronics. These systems have diverse applications in domains including military and defense, medical, automotive, and unmanned autonomous vehicles. The emphasis of the book is on the modeling and optimization of emerging parallel and distributed embedded systems in relation to the three key design metrics of performance, power and dependability.
Modeling Microinverters and DC Power Optimizers in PVWatts
Energy Technology Data Exchange (ETDEWEB)
MacAlpine, S. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Deline, C. [National Renewable Energy Lab. (NREL), Golden, CO (United States)
2015-02-01
Module-level distributed power electronics including microinverters and DC power optimizers are increasingly popular in residential and commercial PV systems. Consumers are realizing their potential to increase design flexibility, monitor system performance, and improve energy capture. It is becoming increasingly important to accurately model PV systems employing these devices. This document summarizes existing published documents to provide uniform, impartial recommendations for how the performance of distributed power electronics can be reflected in NREL's PVWatts calculator (http://pvwatts.nrel.gov/).
Energy balance of forage consumption by phyllophagous insects: optimization model
O. V. Tarasova; I. I. Kalashnikova; V. V. Kuznecsova
2015-01-01
The model of optimal food consumption by phytophagous insects proposed, in which the metabolic costs are presented in the form of two components – the cost of food utilization and costs for proper metabolism of the individuals. Two measures were introduced – the «price» of food conversion and the «price» of biomass synthesis of individuals to assess the effectiveness of food consumption by caterpillars. The proposed approach to the description of food consumption by insects provides the exact...
Power Consumption in Refrigeration Systems - Modeling for Optimization
DEFF Research Database (Denmark)
Hovgaard, Tobias Gybel; Larsen, Lars F. S.; Skovrup, Morten Juel
2011-01-01
Refrigeration systems consume a substantial amount of energy. Taking for instance supermarket refrigeration systems as an example they can account for up to 50−80% of the total energy consumption in the supermarket. Due to the thermal capacity made up by the refrigerated goods in the system...... there is a possibility for optimizing the power consumption by utilizing load shifting strategies. This paper describes the dynamics and the modeling of a vapor compression refrigeration system needed for sufficiently realistic estimation of the power consumption and its minimization. This leads to a non-convex function...
The reproductive value in distributed optimal control models.
Wrzaczek, Stefan; Kuhn, Michael; Prskawetz, Alexia; Feichtinger, Gustav
2010-05-01
We show that in a large class of distributed optimal control models (DOCM), where population is described by a McKendrick type equation with an endogenous number of newborns, the reproductive value of Fisher shows up as part of the shadow price of the population. Depending on the objective function, the reproductive value may be negative. Moreover, we show results of the reproductive value for changing vital rates. To motivate and demonstrate the general framework, we provide examples in health economics, epidemiology, and population biology.
In vitro placental model optimization for nanoparticle transport studies
Directory of Open Access Journals (Sweden)
Cartwright L
2012-01-01
Full Text Available Laura Cartwright1, Marie Sønnegaard Poulsen2, Hanne Mørck Nielsen3, Giulio Pojana4, Lisbeth E Knudsen2, Margaret Saunders1, Erik Rytting2,51Bristol Initiative for Research of Child Health (BIRCH, Biophysics Research Unit, St Michael's Hospital, UH Bristol NHS Foundation Trust, Bristol, UK; 2University of Copenhagen, Faculty of Health Sciences, Department of Public Health, 3University of Copenhagen, Faculty of Pharmaceutical Sciences, Department of Pharmaceutics and Analytical Chemistry, Copenhagen, Denmark; 4Department of Environmental Sciences, Informatics and Statistics, University Ca' Foscari Venice, Venice, Italy; 5Department of Obstetrics and Gynecology, University of Texas Medical Branch, Galveston, Texas, USABackground: Advances in biomedical nanotechnology raise hopes in patient populations but may also raise questions regarding biodistribution and biocompatibility, especially during pregnancy. Special consideration must be given to the placenta as a biological barrier because a pregnant woman's exposure to nanoparticles could have significant effects on the fetus developing in the womb. Therefore, the purpose of this study is to optimize an in vitro model for characterizing the transport of nanoparticles across human placental trophoblast cells.Methods: The growth of BeWo (clone b30 human placental choriocarcinoma cells for nanoparticle transport studies was characterized in terms of optimized Transwell® insert type and pore size, the investigation of barrier properties by transmission electron microscopy, tight junction staining, transepithelial electrical resistance, and fluorescein sodium transport. Following the determination of nontoxic concentrations of fluorescent polystyrene nanoparticles, the cellular uptake and transport of 50 nm and 100 nm diameter particles was measured using the in vitro BeWo cell model.Results: Particle size measurements, fluorescence readings, and confocal microscopy indicated both cellular uptake of
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
Computational modeling, optimization and manufacturing simulation of advanced engineering materials
2016-01-01
This volume presents recent research work focused in the development of adequate theoretical and numerical formulations to describe the behavior of advanced engineering materials. Particular emphasis is devoted to applications in the fields of biological tissues, phase changing and porous materials, polymers and to micro/nano scale modeling. Sensitivity analysis, gradient and non-gradient based optimization procedures are involved in many of the chapters, aiming at the solution of constitutive inverse problems and parameter identification. All these relevant topics are exposed by experienced international and inter institutional research teams resulting in a high level compilation. The book is a valuable research reference for scientists, senior undergraduate and graduate students, as well as for engineers acting in the area of computational material modeling.
Optimization Model for Headway of a Suburban Bus Route
Directory of Open Access Journals (Sweden)
Xiaohong Jiang
2014-01-01
Full Text Available Due to relatively low passenger demand, headways of suburban bus route are usually longer than those of urban bus route. Actually it is also difficult to balance the benefits between passengers and operators, subject to the service standards from the government. Hence the headway of a suburban bus route is usually determined on the empirical experience of transport planners. To cope with this problem, this paper proposes an optimization model for designing the headways of suburban bus routes by minimizing the operating and user costs. The user costs take into account both the waiting time cost and the crowding cost. The feasibility and validity of the proposed model are shown by applying it to the Route 206 in Jiangning district, Nanjing city of China. Weightages of passengers’ cost and operating cost are further discussed, considering different passenger flows. It is found that the headway and objective function are affected by the weightages largely.
An Optimization Model for Product Placement on Product Listing Pages
Directory of Open Access Journals (Sweden)
Yan-Kwang Chen
2014-01-01
Full Text Available The design of product listing pages is a key component of Website design because it has significant influence on the sales volume on a Website. This study focuses on product placement in designing product listing pages. Product placement concerns how venders of online stores place their products over the product listing pages for maximization of profit. This problem is very similar to the offline shelf management problem. Since product information sources on a Web page are typically communicated through the text and image, visual stimuli such as color, shape, size, and spatial arrangement often have an effect on the visual attention of online shoppers and, in turn, influence their eventual purchase decisions. In view of the above, this study synthesizes the visual attention literature and theory of shelf-space allocation to develop a mathematical programming model with genetic algorithms for finding optimal solutions to the focused issue. The validity of the model is illustrated with example problems.
Spädtke, P
2013-01-01
Modeling of technical machines became a standard technique since computer became powerful enough to handle the amount of data relevant to the specific system. Simulation of an existing physical device requires the knowledge of all relevant quantities. Electric fields given by the surrounding boundary as well as magnetic fields caused by coils or permanent magnets have to be known. Internal sources for both fields are sometimes taken into account, such as space charge forces or the internal magnetic field of a moving bunch of charged particles. Used solver routines are briefly described and some bench-marking is shown to estimate necessary computing times for different problems. Different types of charged particle sources will be shown together with a suitable model to describe the physical model. Electron guns are covered as well as different ion sources (volume ion sources, laser ion sources, Penning ion sources, electron resonance ion sources, and H$^-$-sources) together with some remarks on beam transport.
Three essays on multi-level optimization models and applications
Rahdar, Mohammad
The general form of a multi-level mathematical programming problem is a set of nested optimization problems, in which each level controls a series of decision variables independently. However, the value of decision variables may also impact the objective function of other levels. A two-level model is called a bilevel model and can be considered as a Stackelberg game with a leader and a follower. The leader anticipates the response of the follower and optimizes its objective function, and then the follower reacts to the leader's action. The multi-level decision-making model has many real-world applications such as government decisions, energy policies, market economy, network design, etc. However, there is a lack of capable algorithms to solve medium and large scale these types of problems. The dissertation is devoted to both theoretical research and applications of multi-level mathematical programming models, which consists of three parts, each in a paper format. The first part studies the renewable energy portfolio under two major renewable energy policies. The potential competition for biomass for the growth of the renewable energy portfolio in the United States and other interactions between two policies over the next twenty years are investigated. This problem mainly has two levels of decision makers: the government/policy makers and biofuel producers/electricity generators/farmers. We focus on the lower-level problem to predict the amount of capacity expansions, fuel production, and power generation. In the second part, we address uncertainty over demand and lead time in a multi-stage mathematical programming problem. We propose a two-stage tri-level optimization model in the concept of rolling horizon approach to reducing the dimensionality of the multi-stage problem. In the third part of the dissertation, we introduce a new branch and bound algorithm to solve bilevel linear programming problems. The total time is reduced by solving a smaller relaxation
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
Optimizing agent-based transmission models for infectious diseases.
Willem, Lander; Stijven, Sean; Tijskens, Engelbert; Beutels, Philippe; Hens, Niel; Broeckhove, Jan
2015-06-02
Infectious disease modeling and computational power have evolved such that large-scale agent-based models (ABMs) have become feasible. However, the increasing hardware complexity requires adapted software designs to achieve the full potential of current high-performance workstations. We have found large performance differences with a discrete-time ABM for close-contact disease transmission due to data locality. Sorting the population according to the social contact clusters reduced simulation time by a factor of two. Data locality and model performance can also be improved by storing person attributes separately instead of using person objects. Next, decreasing the number of operations by sorting people by health status before processing disease transmission has also a large impact on model performance. Depending of the clinical attack rate, target population and computer hardware, the introduction of the sort phase decreased the run time from 26% up to more than 70%. We have investigated the application of parallel programming techniques and found that the speedup is significant but it drops quickly with the number of cores. We observed that the effect of scheduling and workload chunk size is model specific and can make a large difference. Investment in performance optimization of ABM simulator code can lead to significant run time reductions. The key steps are straightforward: the data structure for the population and sorting people on health status before effecting disease propagation. We believe these conclusions to be valid for a wide range of infectious disease ABMs. We recommend that future studies evaluate the impact of data management, algorithmic procedures and parallelization on model performance.
Optimal hemodynamic response model for functional near-infrared spectroscopy
Directory of Open Access Journals (Sweden)
Muhammad Ahmad Kamran
2015-06-01
Full Text Available Functional near-infrared spectroscopy (fNIRS is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650-950 nm wavelengths. The cortical hemodynamic response (HR differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF is modeled by using two Gamma functions with six unknown parameters. The HRF model is supposed to be linear combination of HRF, baseline and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown. An objective function is developed as a square of the residuals with constraints on twelve free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using ten real and fifteen simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis, i.e., (t-value >tcritical and p-value < 0.05.
Efficient hybrid evolutionary optimization of interatomic potential models.
Brown, W Michael; Thompson, Aidan P; Schultz, Peter A
2010-01-14
The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimization strategy intended for the automated development of material specific interatomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorization can help to address this issue. Finally, we show that with the use of this method, we are able to "rediscover" the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations.
Augmenting Parametric Optimal Ascent Trajectory Modeling with Graph Theory
Dees, Patrick D.; Zwack, Matthew R.; Edwards, Stephen; Steffens, Michael
2016-01-01
It has been well documented that decisions made in the early stages of Conceptual and Pre-Conceptual design commit up to 80% of total Life-Cycle Cost (LCC) while engineers know the least about the product they are designing [1]. Once within Preliminary and Detailed design however, making changes to the design becomes far more difficult to enact in both cost and schedule. Primarily this has been due to a lack of detailed data usually uncovered later during the Preliminary and Detailed design phases. In our current budget-constrained environment, making decisions within Conceptual and Pre-Conceptual design which minimize LCC while meeting requirements is paramount to a program's success. Within the arena of launch vehicle design, optimizing the ascent trajectory is critical for minimizing the costs present within such concerns as propellant, aerodynamic, aeroheating, and acceleration loads while meeting requirements such as payload delivered to a desired orbit. In order to optimize the vehicle design its constraints and requirements must be known, however as the design cycle proceeds it is all but inevitable that the conditions will change. Upon that change, the previously optimized trajectory may no longer be optimal, or meet design requirements. The current paradigm for adjusting to these updates is generating point solutions for every change in the design's requirements [2]. This can be a tedious, time-consuming task as changes in virtually any piece of a launch vehicle's design can have a disproportionately large effect on the ascent trajectory, as the solution space of the trajectory optimization problem is both non-linear and multimodal [3]. In addition, an industry standard tool, Program to Optimize Simulated Trajectories (POST), requires an expert analyst to produce simulated trajectories that are feasible and optimal [4]. In a previous publication the authors presented a method for combatting these challenges [5]. In order to bring more detailed information
Modeling marine surface microplastic transport to assess optimal removal locations
International Nuclear Information System (INIS)
Sherman, Peter; Van Sebille, Erik
2016-01-01
Marine plastic pollution is an ever-increasing problem that demands immediate mitigation and reduction plans. Here, a model based on satellite-tracked buoy observations and scaled to a large data set of observations on microplastic from surface trawls was used to simulate the transport of plastics floating on the ocean surface from 2015 to 2025, with the goal to assess the optimal marine microplastic removal locations for two scenarios: removing the most surface microplastic and reducing the impact on ecosystems, using plankton growth as a proxy. The simulations show that the optimal removal locations are primarily located off the coast of China and in the Indonesian Archipelago for both scenarios. Our estimates show that 31% of the modeled microplastic mass can be removed by 2025 using 29 plastic collectors operating at a 45% capture efficiency from these locations, compared to only 17% when the 29 plastic collectors are moored in the North Pacific garbage patch, between Hawaii and California. The overlap of ocean surface microplastics and phytoplankton growth can be reduced by 46% at our proposed locations, while sinks in the North Pacific can only reduce the overlap by 14%. These results are an indication that oceanic plastic removal might be more effective in removing a greater microplastic mass and in reducing potential harm to marine life when closer to shore than inside the plastic accumulation zones in the centers of the gyres. (letter)
Stochastic Modeling and Optimization in a Microgrid: A Survey
Directory of Open Access Journals (Sweden)
Hao Liang
2014-03-01
Full Text Available The future smart grid is expected to be an interconnected network of small-scale and self-contained microgrids, in addition to a large-scale electric power backbone. By utilizing microsources, such as renewable energy sources and combined heat and power plants, microgrids can supply electrical and heat loads in local areas in an economic and environment friendly way. To better adopt the intermittent and weather-dependent renewable power generation, energy storage devices, such as batteries, heat buffers and plug-in electric vehicles (PEVs with vehicle-to-grid systems can be integrated in microgrids. However, significant technical challenges arise in the planning, operation and control of microgrids, due to the randomness in renewable power generation, the buffering effect of energy storage devices and the high mobility of PEVs. The two-way communication functionalities of the future smart grid provide an opportunity to address these challenges, by offering the communication links for microgrid status information collection. However, how to utilize stochastic modeling and optimization tools for efficient, reliable and economic planning, operation and control of microgrids remains an open issue. In this paper, we investigate the key features of microgrids and provide a comprehensive literature survey on the stochastic modeling and optimization tools for a microgrid. Future research directions are also identified.
Modeling and multidimensional optimization of a tapered free electron laser
Directory of Open Access Journals (Sweden)
Y. Jiao
2012-05-01
Full Text Available Energy extraction efficiency of a free electron laser (FEL can be greatly increased using a tapered undulator and self-seeding. However, the extraction rate is limited by various effects that eventually lead to saturation of the peak intensity and power. To better understand these effects, we develop a model extending the Kroll-Morton-Rosenbluth, one-dimensional theory to include the physics of diffraction, optical guiding, and radially resolved particle trapping. The predictions of the model agree well with that of the GENESIS single-frequency numerical simulations. In particular, we discuss the evolution of the electron-radiation interaction along the tapered undulator and show that the decreasing of refractive guiding is the major cause of the efficiency reduction, particle detrapping, and then saturation of the radiation power. With this understanding, we develop a multidimensional optimization scheme based on GENESIS simulations to increase the energy extraction efficiency via an improved taper profile and variation in electron beam radius. We present optimization results for hard x-ray tapered FELs, and the dependence of the maximum extractable radiation power on various parameters of the initial electron beam, radiation field, and the undulator system. We also study the effect of the sideband growth in a tapered FEL. Such growth induces increased particle detrapping and thus decreased refractive guiding that together strongly limit the overall energy extraction efficiency.
Optimizing Crawler4j using MapReduce Programming Model
Siddesh, G. M.; Suresh, Kavya; Madhuri, K. Y.; Nijagal, Madhushree; Rakshitha, B. R.; Srinivasa, K. G.
2017-06-01
World wide web is a decentralized system that consists of a repository of information on the basis of web pages. These web pages act as a source of information or data in the present analytics world. Web crawlers are used for extracting useful information from web pages for different purposes. Firstly, it is used in web search engines where the web pages are indexed to form a corpus of information and allows the users to query on the web pages. Secondly, it is used for web archiving where the web pages are stored for later analysis phases. Thirdly, it can be used for web mining where the web pages are monitored for copyright purposes. The amount of information processed by the web crawler needs to be improved by using the capabilities of modern parallel processing technologies. In order to solve the problem of parallelism and the throughput of crawling this work proposes to optimize the Crawler4j using the Hadoop MapReduce programming model by parallelizing the processing of large input data. Crawler4j is a web crawler that retrieves useful information about the pages that it visits. The crawler Crawler4j coupled with data and computational parallelism of Hadoop MapReduce programming model improves the throughput and accuracy of web crawling. The experimental results demonstrate that the proposed solution achieves significant improvements with respect to performance and throughput. Hence the proposed approach intends to carve out a new methodology towards optimizing web crawling by achieving significant performance gain.
Modeling marine surface microplastic transport to assess optimal removal locations
Sherman, Peter; van Sebille, Erik
2016-01-01
Marine plastic pollution is an ever-increasing problem that demands immediate mitigation and reduction plans. Here, a model based on satellite-tracked buoy observations and scaled to a large data set of observations on microplastic from surface trawls was used to simulate the transport of plastics floating on the ocean surface from 2015 to 2025, with the goal to assess the optimal marine microplastic removal locations for two scenarios: removing the most surface microplastic and reducing the impact on ecosystems, using plankton growth as a proxy. The simulations show that the optimal removal locations are primarily located off the coast of China and in the Indonesian Archipelago for both scenarios. Our estimates show that 31% of the modeled microplastic mass can be removed by 2025 using 29 plastic collectors operating at a 45% capture efficiency from these locations, compared to only 17% when the 29 plastic collectors are moored in the North Pacific garbage patch, between Hawaii and California. The overlap of ocean surface microplastics and phytoplankton growth can be reduced by 46% at our proposed locations, while sinks in the North Pacific can only reduce the overlap by 14%. These results are an indication that oceanic plastic removal might be more effective in removing a greater microplastic mass and in reducing potential harm to marine life when closer to shore than inside the plastic accumulation zones in the centers of the gyres.
Optimization of the artificial urinary sphincter: modelling and experimental validation
International Nuclear Information System (INIS)
Marti, Florian; Leippold, Thomas; John, Hubert; Blunschi, Nadine; Mueller, Bert
2006-01-01
The artificial urinary sphincter should be long enough to prevent strangulation effects of the urethral tissue and short enough to avoid the improper dissection of the surrounding tissue. To optimize the sphincter length, the empirical three-parameter urethra compression model is proposed based on the mechanical properties of the urethra: wall pressure, tissue response rim force and sphincter periphery length. In vitro studies using explanted animal or human urethras and different artificial sphincters demonstrate its applicability. The pressure of the sphincter to close the urethra is shown to be a linear function of the bladder pressure. The force to close the urethra depends on the sphincter length linearly. Human urethras display the same dependences as the urethras of pig, dog, sheep and calf. Quantitatively, however, sow urethras resemble best the human ones. For the human urethras, the mean wall pressure corresponds to (-12.6 ± 0.9) cmH 2 O and (-8.7 ± 1.1) cmH 2 O, the rim length to (3.0 ± 0.3) mm and (5.1 ± 0.3) mm and the rim force to (60 ± 20) mN and (100 ± 20) mN for urethra opening and closing, respectively. Assuming an intravesical pressure of 40 cmH 2 O, and an external pressure on the urethra of 60 cmH 2 O, the model leads to the optimized sphincter length of (17.3 ± 3.8) mm
A new mathematical model in space optimization: A case study
Abdullah, Kamilah; Kamis, Nor Hanimah; Sha'ari, Nor Shahida; Muhammad Halim, Nurul Suhada; Hashim, Syaril Naqiah
2013-04-01
Most of higher education institutions provide certain area known as learning centre for their students to study or having group discussions. However, some of the learning centers are not provided by optimum number of tables and seats to accommodate the students sufficiently. This study proposed a new mathematical model in optimizing the number of tables and seats at Laman Najib, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM) Shah Alam. An improvement of space capacity with maximum number of students who can facilitate the Laman Najib at the same time has been made by considering the type and size of tables that are appropriate for student's discussions. Our finding is compared with the result of Simplex method of linear programming to ensure that our new model is valid and consistent with other existing approaches. As a conclusion, we found that the round-type tables with six seats provide the maximum number of students who can use Laman Najib for their discussions or group studying. Both methods are also practical to use as alternative approaches in solving other space optimization problems.
Economically optimized electricity trade modeling: Iran-Turkey case
International Nuclear Information System (INIS)
Shakouri, G.H.; Eghlimi, M.; Manzoor, D.
2009-01-01
The advantages of power trade between countries, which are attainable for various facts, are distinguished now. Daily differences in the peak-load times of neighboring countries commonly occur for differences in the longitudes of their location. Seasonal differences are also caused by differences in the latitudes leading to different climates. Consequently, different load curves help to have such a production schedule that reduces blackouts and investments for power generation by planning for a proper trade between countries in a region. This paper firstly describes the methodology and framework for the power trade and then the results of an optimal power trade model between Iran and Turkey, which shows a potential benefit for both countries by peak shaving, are presented. The results, in the worst case design, represent optimality of about 1500 MW electricity export from Iran to Turkey at the Turkish peak times, as well as 447 MW electricity import from Turkey at the Iranian peak times. In addition, results derived from running a Long-Run model show that there will be greater potential for power export from Iran to Turkey, which is a guideline of an energy conservation strategy for both countries in the future
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.
Use of advanced modeling techniques to optimize thermal packaging designs.
Formato, Richard M; Potami, Raffaele; Ahmed, Iftekhar
2010-01-01
Through a detailed case study the authors demonstrate, for the first time, the capability of using advanced modeling techniques to correctly simulate the transient temperature response of a convective flow-based thermal shipper design. The objective of this case study was to demonstrate that simulation could be utilized to design a 2-inch-wall polyurethane (PUR) shipper to hold its product box temperature between 2 and 8 °C over the prescribed 96-h summer profile (product box is the portion of the shipper that is occupied by the payload). Results obtained from numerical simulation are in excellent agreement with empirical chamber data (within ±1 °C at all times), and geometrical locations of simulation maximum and minimum temperature match well with the corresponding chamber temperature measurements. Furthermore, a control simulation test case was run (results taken from identical product box locations) to compare the coupled conduction-convection model with a conduction-only model, which to date has been the state-of-the-art method. For the conduction-only simulation, all fluid elements were replaced with "solid" elements of identical size and assigned thermal properties of air. While results from the coupled thermal/fluid model closely correlated with the empirical data (±1 °C), the conduction-only model was unable to correctly capture the payload temperature trends, showing a sizeable error compared to empirical values (ΔT > 6 °C). A modeling technique capable of correctly capturing the thermal behavior of passively refrigerated shippers can be used to quickly evaluate and optimize new packaging designs. Such a capability provides a means to reduce the cost and required design time of shippers while simultaneously improving their performance. Another advantage comes from using thermal modeling (assuming a validated model is available) to predict the temperature distribution in a shipper that is exposed to ambient temperatures which were not bracketed
Optimizing design of converters using power cycling lifetime models
DEFF Research Database (Denmark)
Nielsen, Rasmus Ørndrup; Munk-Nielsen, Stig
2015-01-01
Converter power cycling lifetime depends heavily on converter operation point. A lifetime model of a single power module switched mode power supply with wide input voltage range is shown. A lifetime model is created using a power loss model, a thermal model and a model for power cycling capability...
An Efficient Framework Model for Optimizing Routing Performance in VANETs
Zulkarnain, Zuriati Ahmad; Subramaniam, Shamala
2018-01-01
Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED). PMID:29462884
Optimization of Forward Wave Modeling on Contemporary HPC Architectures
Energy Technology Data Exchange (ETDEWEB)
Krueger, Jens [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Micikevicius, Paulius [NVIDIA, Santa Clara, CA (United States); Williams, Samuel [Fraunhofer ITWM, Kaiserslautern (Germany)
2012-07-20
Reverse Time Migration (RTM) is one of the main approaches in the seismic processing industry for imaging the subsurface structure of the Earth. While RTM provides qualitative advantages over its predecessors, it has a high computational cost warranting implementation on HPC architectures. We focus on three progressively more complex kernels extracted from RTM: for isotropic (ISO), vertical transverse isotropic (VTI) and tilted transverse isotropic (TTI) media. In this work, we examine performance optimization of forward wave modeling, which describes the computational kernels used in RTM, on emerging multi- and manycore processors and introduce a novel common subexpression elimination optimization for TTI kernels. We compare attained performance and energy efficiency in both the single-node and distributed memory environments in order to satisfy industry’s demands for fidelity, performance, and energy efficiency. Moreover, we discuss the interplay between architecture (chip and system) and optimizations (both on-node computation) highlighting the importance of NUMA-aware approaches to MPI communication. Ultimately, our results show we can improve CPU energy efficiency by more than 10× on Magny Cours nodes while acceleration via multiple GPUs can surpass the energy-efficient Intel Sandy Bridge by as much as 3.6×.
Thermal modeling and optimization of a thermally matched energy harvester
Boughaleb, J.; Arnaud, A.; Cottinet, P. J.; Monfray, S.; Gelenne, P.; Kermel, P.; Quenard, S.; Boeuf, F.; Guyomar, D.; Skotnicki, T.
2015-08-01
The interest in energy harvesting devices has grown with the development of wireless sensors requiring small amounts of energy to function. The present article addresses the thermal investigation of a coupled piezoelectric and bimetal-based heat engine. The thermal energy harvester in question converts low-grade heat flows into electrical charges by achieving a two-step conversion mechanism for which the key point is the ability to maintain a significant thermal gradient without any heat sink. Many studies have previously focused on the electrical properties of this innovative device for energy harvesting but until now, no thermal modeling has been able to describe the device specificities or improve its thermal performances. The research reported in this paper focuses on the modeling of the harvester using an equivalent electrical circuit approach. It is shown that the knowledge of the thermal properties inside the device and a good comprehension of its heat exchange with the surrounding play a key role in the optimization procedure. To validate the thermal modeling, finite element analyses as well as experimental measurements on a hot plate were carried out and the techniques were compared. The proposed model provided a practical guideline for improving the generator design to obtain a thermally matched energy harvester that can function over a wide range of hot source temperatures for the same bimetal. A direct application of this study has been implemented on scaled structures to maintain an important temperature difference between the cold surface and the hot reservoir. Using the equations of the thermal model, predictions of the thermal properties were evaluated depending on the scaling factor and solutions for future thermal improvements are presented.
A Suitable Artificial Intelligence Model for Inventory Level Optimization
Directory of Open Access Journals (Sweden)
Tereza Sustrova
2016-05-01
Full Text Available Purpose of the article: To examine suitable methods of artificial neural networks and their application in business operations, specifically to the supply chain management. The article discusses construction of an artificial neural networks model that can be used to facilitate optimization of inventory level and thus improve the ordering system and inventory management. For the data analysis from the area of wholesale trade with connecting material is used. Methodology/methods: Methods used in the paper consists especially of artificial neural networks and ANN-based modelling. For data analysis and preprocessing, MS Office Excel software is used. As an instrument for neural network forecasting MathWorks MATLAB Neural Network Tool was used. Deductive quantitative methods for research are also used. Scientific aim: The effort is directed at finding whether the method of prediction using artificial neural networks is suitable as a tool for enhancing the ordering system of an enterprise. The research also focuses on finding what architecture of the artificial neural networks model is the most suitable for subsequent prediction. Findings: Artificial neural networks models can be used for inventory management and lot-sizing problem successfully. A network with the TRAINGDX training function and TANSIG transfer function and 6-8-1 architecture can be considered the most suitable for artificial neural network, as it shows the best results for subsequent prediction. Conclusions: It can be concluded that the created model of artificial neural network can be successfully used for predicting order size and therefore for improving the order cycle of an enterprise. Conclusions resulting from the paper are beneficial for further research.
Optimal hemodynamic response model for functional near-infrared spectroscopy.
Kamran, Muhammad A; Jeong, Myung Yung; Mannan, Malik M N
2015-01-01
Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650-950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > t critical and p-value < 0.05).
A canopy-type similarity model for wind farm optimization
Markfort, Corey D.; Zhang, Wei; Porté-Agel, Fernando
2013-04-01
The atmospheric boundary layer (ABL) flow through and over wind farms has been found to be similar to canopy-type flows, with characteristic flow development and shear penetration length scales (Markfort et al., 2012). Wind farms capture momentum from the ABL both at the leading edge and from above. We examine this further with an analytical canopy-type model. Within the flow development region, momentum is advected into the wind farm and wake turbulence draws excess momentum in from between turbines. This spatial heterogeneity of momentum within the wind farm is characterized by large dispersive momentum fluxes. Once the flow within the farm is developed, the area-averaged velocity profile exhibits a characteristic inflection point near the top of the wind farm, similar to that of canopy-type flows. The inflected velocity profile is associated with the presence of a dominant characteristic turbulence scale, which may be responsible for a significant portion of the vertical momentum flux. Prediction of this scale is useful for determining the amount of available power for harvesting. The new model is tested with results from wind tunnel experiments, which were conducted to characterize the turbulent flow in and above model wind farms in aligned and staggered configurations. The model is useful for representing wind farms in regional scale models, for the optimization of wind farms considering wind turbine spacing and layout configuration, and for assessing the impacts of upwind wind farms on nearby wind resources. Markfort CD, W Zhang and F Porté-Agel. 2012. Turbulent flow and scalar transport through and over aligned and staggered wind farms. Journal of Turbulence. 13(1) N33: 1-36. doi:10.1080/14685248.2012.709635.
Irregular Shaped Building Design Optimization with Building Information Modelling
Directory of Open Access Journals (Sweden)
Lee Xia Sheng
2016-01-01
Full Text Available This research is to recognise the function of Building Information Modelling (BIM in design optimization for irregular shaped buildings. The study focuses on a conceptual irregular shaped “twisted” building design similar to some existing sculpture-like architectures. Form and function are the two most important aspects of new buildings, which are becoming more sophisticated as parts of equally sophisticated “systems” that we are living in. Nowadays, it is common to have irregular shaped or sculpture-like buildings which are very different when compared to regular buildings. Construction industry stakeholders are facing stiff challenges in many aspects such as buildability, cost effectiveness, delivery time and facility management when dealing with irregular shaped building projects. Building Information Modelling (BIM is being utilized to enable architects, engineers and constructors to gain improved visualization for irregular shaped buildings; this has a purpose of identifying critical issues before initiating physical construction work. In this study, three variations of design options differing in rotating angle: 30 degrees, 60 degrees and 90 degrees are created to conduct quantifiable comparisons. Discussions are focused on three major aspects including structural planning, usable building space, and structural constructability. This research concludes that Building Information Modelling is instrumental in facilitating design optimization for irregular shaped building. In the process of comparing different design variations, instead of just giving “yes or no” type of response, stakeholders can now easily visualize, evaluate and decide to achieve the right balance based on their own criteria. Therefore, construction project stakeholders are empowered with superior evaluation and decision making capability.
Sherry, Lance; Ferguson, John; Hoffman, Karla; Donohue, George; Beradino, Frank
2012-01-01
This report describes the Airline Fleet, Route, and Schedule Optimization Model (AFRS-OM) that is designed to provide insights into airline decision-making with regards to markets served, schedule of flights on these markets, the type of aircraft assigned to each scheduled flight, load factors, airfares, and airline profits. The main inputs to the model are hedged fuel prices, airport capacity limits, and candidate markets. Embedded in the model are aircraft performance and associated cost factors, and willingness-to-pay (i.e. demand vs. airfare curves). Case studies demonstrate the application of the model for analysis of the effects of increased capacity and changes in operating costs (e.g. fuel prices). Although there are differences between airports (due to differences in the magnitude of travel demand and sensitivity to airfare), the system is more sensitive to changes in fuel prices than capacity. Further, the benefits of modernization in the form of increased capacity could be undermined by increases in hedged fuel prices
Modeling digital breast tomosynthesis imaging systems for optimization studies
Lau, Beverly Amy
Digital breast tomosynthesis (DBT) is a new imaging modality for breast imaging. In tomosynthesis, multiple images of the compressed breast are acquired at different angles, and the projection view images are reconstructed to yield images of slices through the breast. One of the main problems to be addressed in the development of DBT is the optimal parameter settings to obtain images ideal for detection of cancer. Since it would be unethical to irradiate women multiple times to explore potentially optimum geometries for tomosynthesis, it is ideal to use a computer simulation to generate projection images. Existing tomosynthesis models have modeled scatter and detector without accounting for oblique angles of incidence that tomosynthesis introduces. Moreover, these models frequently use geometry-specific physical factors measured from real systems, which severely limits the robustness of their algorithms for optimization. The goal of this dissertation was to design the framework for a computer simulation of tomosynthesis that would produce images that are sensitive to changes in acquisition parameters, so an optimization study would be feasible. A computer physics simulation of the tomosynthesis system was developed. The x-ray source was modeled as a polychromatic spectrum based on published spectral data, and inverse-square law was applied. Scatter was applied using a convolution method with angle-dependent scatter point spread functions (sPSFs), followed by scaling using an angle-dependent scatter-to-primary ratio (SPR). Monte Carlo simulations were used to generate sPSFs for a 5-cm breast with a 1-cm air gap. Detector effects were included through geometric propagation of the image onto layers of the detector, which were blurred using depth-dependent detector point-spread functions (PRFs). Depth-dependent PRFs were calculated every 5-microns through a 200-micron thick CsI detector using Monte Carlo simulations. Electronic noise was added as Gaussian noise as a
Software Piracy Detection Model Using Ant Colony Optimization Algorithm
Astiqah Omar, Nor; Zakuan, Zeti Zuryani Mohd; Saian, Rizauddin
2017-06-01
Internet enables information to be accessible anytime and anywhere. This scenario creates an environment whereby information can be easily copied. Easy access to the internet is one of the factors which contribute towards piracy in Malaysia as well as the rest of the world. According to a survey conducted by Compliance Gap BSA Global Software Survey in 2013 on software piracy, found out that 43 percent of the software installed on PCs around the world was not properly licensed, the commercial value of the unlicensed installations worldwide was reported to be 62.7 billion. Piracy can happen anywhere including universities. Malaysia as well as other countries in the world is faced with issues of piracy committed by the students in universities. Piracy in universities concern about acts of stealing intellectual property. It can be in the form of software piracy, music piracy, movies piracy and piracy of intellectual materials such as books, articles and journals. This scenario affected the owner of intellectual property as their property is in jeopardy. This study has developed a classification model for detecting software piracy. The model was developed using a swarm intelligence algorithm called the Ant Colony Optimization algorithm. The data for training was collected by a study conducted in Universiti Teknologi MARA (Perlis). Experimental results show that the model detection accuracy rate is better as compared to J48 algorithm.
Productivity simulation model for optimization of maritime container terminals
Directory of Open Access Journals (Sweden)
Elen TWRDY
2009-01-01
Full Text Available This article describes a proposed productivity simulation model enabling container terminal operators to find optimization possibilities. A research of more than forty terminals has been done, in order to provide a helping tool for maritime container terminals. By applying an adequate simulation model, it is possible to measure and increase the productivity in all subsystem of the maritime container terminal. Management of a maritime container terminal includes a vast number of different financial and operational decisions. Financial decisions are often in a direct connection with investments in infrastructure and handling equipment. Such investments are very expensive. Therefore, they must give back the invested money as soon as possible. On the other hand, some terminals are limited by the physical extension and are forced to increase annual throughput only with sophisticated equipment on the berth side and on the yard as well. Considering all these important facts in container and shipping industry, the proposed simulation model gives a helping tool for checking the productivity and its time variation and monitoring competitiveness of a certain maritime terminal with terminals from the same group.
Modeling the minimum enzymatic requirements for optimal cellulose conversion
International Nuclear Information System (INIS)
Den Haan, R; Van Zyl, W H; Van Zyl, J M; Harms, T M
2013-01-01
Hydrolysis of cellulose is achieved by the synergistic action of endoglucanases, exoglucanases and β-glucosidases. Most cellulolytic microorganisms produce a varied array of these enzymes and the relative roles of the components are not easily defined or quantified. In this study we have used partially purified cellulases produced heterologously in the yeast Saccharomyces cerevisiae to increase our understanding of the roles of some of these components. CBH1 (Cel7), CBH2 (Cel6) and EG2 (Cel5) were separately produced in recombinant yeast strains, allowing their isolation free of any contaminating cellulolytic activity. Binary and ternary mixtures of the enzymes at loadings ranging between 3 and 100 mg g −1 Avicel allowed us to illustrate the relative roles of the enzymes and their levels of synergy. A mathematical model was created to simulate the interactions of these enzymes on crystalline cellulose, under both isolated and synergistic conditions. Laboratory results from the various mixtures at a range of loadings of recombinant enzymes allowed refinement of the mathematical model. The model can further be used to predict the optimal synergistic mixes of the enzymes. This information can subsequently be applied to help to determine the minimum protein requirement for complete hydrolysis of cellulose. Such knowledge will be greatly informative for the design of better enzymatic cocktails or processing organisms for the conversion of cellulosic biomass to commodity products. (letter)
Optimized carbon dioxide removal model for gas fired power plant
Arachchige, Udara Sampath P.; Mohsin, Muhammad; Melaaen, Morten Christian
2012-01-01
The carbon capture process model was developed for 500MW gas-fired power plant flue gas treating. Three different efficiencies, 85%, 90%, and 95%, were used to implement the model in Aspen Plus. The electrolyte NRTL rate base model was used to develop the model. The selected solvent properties were used to develop and implemented model is used for further simulations. The implemented open loop base case model of 85% removal efficiency is used to check the parameters' effect on removal efficie...
Open source Modeling and optimization tools for Planning
Energy Technology Data Exchange (ETDEWEB)
Peles, S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2017-02-10
Open source modeling and optimization tools for planning The existing tools and software used for planning and analysis in California are either expensive, difficult to use, or not generally accessible to a large number of participants. These limitations restrict the availability of participants for larger scale energy and grid studies in the state. The proposed initiative would build upon federal and state investments in open source software, and create and improve open source tools for use in the state planning and analysis activities. Computational analysis and simulation frameworks in development at national labs and universities can be brought forward to complement existing tools. An open source platform would provide a path for novel techniques and strategies to be brought into the larger community and reviewed by a broad set of stakeholders.
Finite Element Modeling and Optimization of Mechanical Joining Technology
Chenot, Jean-Loup; Bouchard, Pierre-Olivier; Massoni, Elisabeth; Mocellin, Katia; Lasne, Patrice
2011-05-01
The main scientific ingredients are recalled for developing a general finite element code and model accurately large plastic deformation of metallic materials during joining processes. Multi material contact is treated using the classical master and slave approach. Rupture may occur in joining processes or even be imposed in self piercing riveting and it must be predicted to evaluate the ultimate strength of joins. Damage is introduced with a generalized uncoupled damage criterion, or by utilizing a coupled formulation with a Lemaître law. Several joining processes are briefly analyzed in term of specific scientific issues: riveting, self piercing riveting, clinching, crimping, hemming and screwing. It is shown that not only the joining process can be successfully simulated and optimized, but also the strength of the assembly can be predicted in tension and in shearing.
Optimizing nitrogen fertilizer use: Current approaches and simulation models
International Nuclear Information System (INIS)
Baethgen, W.E.
2000-01-01
Nitrogen (N) is the most common limiting nutrient in agricultural systems throughout the world. Crops need sufficient available N to achieve optimum yields and adequate grain-protein content. Consequently, sub-optimal rates of N fertilizers typically cause lower economical benefits for farmers. On the other hand, excessive N fertilizer use may result in environmental problems such as nitrate contamination of groundwater and emission of N 2 O and NO. In spite of the economical and environmental importance of good N fertilizer management, the development of optimum fertilizer recommendations is still a major challenge in most agricultural systems. This article reviews the approaches most commonly used for making N recommendations: expected yield level, soil testing and plant analysis (including quick tests). The paper introduces the application of simulation models that complement traditional approaches, and includes some examples of current applications in Africa and South America. (author)
Determining of the Optimal Device Lifetime using Mathematical Renewal Models
Directory of Open Access Journals (Sweden)
Knežo Dušan
2016-05-01
Full Text Available Paper deals with the operations and equipment of the machine in the process of organizing production. During operation machines require maintenance and repairs, while in case of failure or machine wears it is necessary to replace them with new ones. For the process of replacement of old machines with new ones the term renewal is used. Qualitative aspects of the renewal process observe renewal theory, which is mainly based on the theory of probability and mathematical statistics. Devices lifetimes are closely related to the renewal of the devices. Presented article is focused on mathematical deduction of mathematical renewal models and determining optimal lifetime of the devices from the aspect of expenditures on renewal process.
Comparison of operation optimization methods in energy system modelling
DEFF Research Database (Denmark)
Ommen, Torben Schmidt; Markussen, Wiebke Brix; Elmegaard, Brian
2013-01-01
In areas with large shares of Combined Heat and Power (CHP) production, significant introduction of intermittent renewable power production may lead to an increased number of operational constraints. As the operation pattern of each utility plant is determined by optimization of economics......, possibilities for decoupling production constraints may be valuable. Introduction of heat pumps in the district heating network may pose this ability. In order to evaluate if the introduction of heat pumps is economically viable, we develop calculation methods for the operation patterns of each of the used...... operation constraints, while the third approach uses nonlinear programming. In the present case the non-linearity occurs in the boiler efficiency of power plants and the cv-value of an extraction plant. The linear programming model is used as a benchmark, as this type is frequently used, and has the lowest...
Yang, Guoxiang; Best, Elly P H
2015-09-15
Best management practices (BMPs) can be used effectively to reduce nutrient loads transported from non-point sources to receiving water bodies. However, methodologies of BMP selection and placement in a cost-effective way are needed to assist watershed management planners and stakeholders. We developed a novel modeling-optimization framework that can be used to find cost-effective solutions of BMP placement to attain nutrient load reduction targets. This was accomplished by integrating a GIS-based BMP siting method, a WQM-TMDL-N modeling approach to estimate total nitrogen (TN) loading, and a multi-objective optimization algorithm. Wetland restoration and buffer strip implementation were the two BMP categories used to explore the performance of this framework, both differing greatly in complexity of spatial analysis for site identification. Minimizing TN load and BMP cost were the two objective functions for the optimization process. The performance of this framework was demonstrated in the Tippecanoe River watershed, Indiana, USA. Optimized scenario-based load reduction indicated that the wetland subset selected by the minimum scenario had the greatest N removal efficiency. Buffer strips were more effective for load removal than wetlands. The optimized solutions provided a range of trade-offs between the two objective functions for both BMPs. This framework can be expanded conveniently to a regional scale because the NHDPlus catchment serves as its spatial computational unit. The present study demonstrated the potential of this framework to find cost-effective solutions to meet a water quality target, such as a 20% TN load reduction, under different conditions. Copyright © 2015 Elsevier Ltd. All rights reserved.
Modelling of powder die compaction for press cycle optimization
Directory of Open Access Journals (Sweden)
Bayle Jean-Philippe
2016-01-01
Full Text Available A new electromechanical press for fuel pellet manufacturing was built last year in partnership between CEA-Marcoule and ChampalleAlcen. This press was developed to shape pellets in a hot cell via remote handling. It has been qualified to show its robustness and to optimize the compaction cycle, thus obtaining a better sintered pellet profile and limiting damage. We will show you how 400 annular pellets have been produced with good geometry's parameters, based on press settings management. These results are according to a good phenomenological pressing knowledge with Finite Element Modeling calculation. Therefore, during die pressing, a modification in the punch displacement sequence induces fluctuation in the axial distribution of frictional forces. The green pellet stress and density gradients are based on these frictional forces between powder and tool, and between grains in the powder, influencing the shape of the pellet after sintering. The pellet shape and diameter tolerances must be minimized to avoid the need for grinding operations. To find the best parameters for the press settings, which enable optimization, FEM calculations were used and different compaction models compared to give the best calculation/physical trial comparisons. These simulations were then used to predict the impact of different parameters when there is a change in the type of powder and the pellet size, or when the behavior of the press changes during the compaction time. In 2016, it is planned to set up the press in a glove box for UO2 manufacturing qualification based on our simulation methodology, before actual hot cell trials in the future.
Inverse modeling of FIB milling by dose profile optimization
International Nuclear Information System (INIS)
Lindsey, S.; Waid, S.; Hobler, G.; Wanzenböck, H.D.; Bertagnolli, E.
2014-01-01
FIB technologies possess a unique ability to form topographies that are difficult or impossible to generate with binary etching through typical photo-lithography. The ability to arbitrarily vary the spatial dose distribution and therefore the amount of milling opens possibilities for the production of a wide range of functional structures with applications in biology, chemistry, and optics. However in practice, the realization of these goals is made difficult by the angular dependence of the sputtering yield and redeposition effects that vary as the topography evolves. An inverse modeling algorithm that optimizes dose profiles, defined as the superposition of time invariant pixel dose profiles (determined from the beam parameters and pixel dwell times), is presented. The response of the target to a set of pixel dwell times in modeled by numerical continuum simulations utilizing 1st and 2nd order sputtering and redeposition, the resulting surfaces are evaluated with respect to a target topography in an error minimization routine. Two algorithms for the parameterization of pixel dwell times are presented, a direct pixel dwell time method, and an abstracted method that uses a refineable piecewise linear cage function to generate pixel dwell times from a minimal number of parameters. The cage function method demonstrates great flexibility and efficiency as compared to the direct fitting method with performance enhancements exceeding ∼10× as compared to direct fitting for medium to large simulation sets. Furthermore, the refineable nature of the cage function enables solutions to adapt to the desired target function. The optimization algorithm, although working with stationary dose profiles, is demonstrated to be applicable also outside the quasi-static approximation. Experimental data confirms the viability of the solutions for 5 × 7 μm deep lens like structures defined by 90 pixel dwell times
Optimization of tobacco variety model to increase seed productivity
Directory of Open Access Journals (Sweden)
О. І. Савіна
2017-03-01
Full Text Available Purpose. To develop a tobacco variety model with optimal inflorescence traits such as size and shape that will allow to increase seed productivity of the crop. Methods. Statistical and mathematical (correlative, regressive ones. Results. Basic collection consisting of 282 variety samples registered in the National Genetics Center was evaluated, optimal parameters of inflorescence were defined that can provide a high seed yield. During statistical analysis, correlation matrix was developed with the purpose to highlight traits that correlate with inflorescence productivity. According to the results of correlation analysis, a strong relationship between the width and height of inflorescence (r = 0,773±0,038 was established. Somewhat weaker correlation was observed when modeling regressive relation between inflorescence height and width, where regression showed the medium relationship. Regression equation of these traits is as follows: y = 0,5585x + 8,4649. Inflorescence density (r = 0,646, height (r = 0,556 and width (r = 0,527 also had quite a high positive effect on seed productivity. The results of regression analysis pointed to the fact that there were a linear relationship between inflorescence size and seed productivity. Conclusions. Among 282 samples of basic tobacco collection, 29 varieties with high seed productivity was defined which can be used in the breeding process, and ‘Sobolchskyi 15/21’, ‘Ergo 23’, ‘C-11’, ‘Sygarnyi 99’ varieties were selected for large-scale implementation into the production of raw material of cigar type.
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.
Multi-model Simulation for Optimal Control of Aeroacoustics.
Energy Technology Data Exchange (ETDEWEB)
Collis, Samuel Scott; Chen, Guoquan
2005-05-01
Flow-generated noise, especially rotorcraft noise has been a serious concern for bothcommercial and military applications. A particular important noise source for rotor-craft is Blade-Vortex-Interaction (BVI)noise, a high amplitude, impulsive sound thatoften dominates other rotorcraft noise sources. Usually BVI noise is caused by theunsteady flow changes around various rotor blades due to interactions with vorticespreviously shed by the blades. A promising approach for reducing the BVI noise isto use on-blade controls, such as suction/blowing, micro-flaps/jets, and smart struc-tures. Because the design and implementation of such experiments to evaluate suchsystems are very expensive, efficient computational tools coupled with optimal con-trol systems are required to explore the relevant physics and evaluate the feasibilityof using various micro-fluidic devices before committing to hardware.In this thesis the research is to formulate and implement efficient computationaltools for the development and study of optimal control and design strategies for com-plex flow and acoustic systems with emphasis on rotorcraft applications, especiallyBVI noise control problem. The main purpose of aeroacoustic computations is todetermine the sound intensity and directivity far away from the noise source. How-ever, the computational cost of using a high-fidelity flow-physics model across thefull domain is usually prohibitive and itmight also be less accurate because of thenumerical diffusion and other problems. Taking advantage of the multi-physics andmulti-scale structure of this aeroacoustic problem, we develop a multi-model, multi-domain (near-field/far-field) method based on a discontinuous Galerkin discretiza-tion. In this approach the coupling of multi-domains and multi-models is achievedby weakly enforcing continuity of normal fluxes across a coupling surface. For ourinterested aeroacoustics control problem, the adjoint equations that determine thesensitivity of the cost
Source term identification in atmospheric modelling via sparse optimization
Adam, Lukas; Branda, Martin; Hamburger, Thomas
2015-04-01
Inverse modelling plays an important role in identifying the amount of harmful substances released into atmosphere during major incidents such as power plant accidents or volcano eruptions. Another possible application of inverse modelling lies in the monitoring the CO2 emission limits where only observations at certain places are available and the task is to estimate the total releases at given locations. This gives rise to minimizing the discrepancy between the observations and the model predictions. There are two standard ways of solving such problems. In the first one, this discrepancy is regularized by adding additional terms. Such terms may include Tikhonov regularization, distance from a priori information or a smoothing term. The resulting, usually quadratic, problem is then solved via standard optimization solvers. The second approach assumes that the error term has a (normal) distribution and makes use of Bayesian modelling to identify the source term. Instead of following the above-mentioned approaches, we utilize techniques from the field of compressive sensing. Such techniques look for a sparsest solution (solution with the smallest number of nonzeros) of a linear system, where a maximal allowed error term may be added to this system. Even though this field is a developed one with many possible solution techniques, most of them do not consider even the simplest constraints which are naturally present in atmospheric modelling. One of such examples is the nonnegativity of release amounts. We believe that the concept of a sparse solution is natural in both problems of identification of the source location and of the time process of the source release. In the first case, it is usually assumed that there are only few release points and the task is to find them. In the second case, the time window is usually much longer than the duration of the actual release. In both cases, the optimal solution should contain a large amount of zeros, giving rise to the
The optimal achievement model and underachievement in Hong Kong: an application of the Rasch model
Directory of Open Access Journals (Sweden)
SHANE N. PHILLIPSON
2008-06-01
Full Text Available Termed the optimal achievement model, a new method for the estimation of underachievement relies on measuring student potential (P and achievement (A using Rasch models and calculating an achievement index, IA, for each individual. This study extends a previous report (Phillipson & Tse, 2007 that estimated the proportion of Hong Kong students in Primary 5 to now include a sample of students from Primary 3 (n = 1406, Secondary 1 (n = 756 and Secondary 3 (n = 578, across six districts of Hong Kong. The students were administered a standardized test of mathematical achievement and the Ravens Progressive Matrices Test. Using the optimal achievement model, estimates of underachievement at six percentile bands showed that the proportion of students who were underachieving ranged from 10 % at the 50-59th percentile band up to 30 % at the >95th percentile band for Primary 3, Primary 5 and Secondary 1 students, and 50 % of Secondary 3 students. The estimation of IA at the level of the individual allows the researcher the possibility to directly study the interaction of the environment on student potential.
Model for determining and optimizing delivery performance in industrial systems
Directory of Open Access Journals (Sweden)
Fechete Flavia
2017-01-01
Full Text Available Performance means achieving organizational objectives regardless of their nature and variety, and even overcoming them. Improving performance is one of the major goals of any company. Achieving the global performance means not only obtaining the economic performance, it is a must to take into account other functions like: function of quality, delivery, costs and even the employees satisfaction. This paper aims to improve the delivery performance of an industrial system due to their very low results. The delivery performance took into account all categories of performance indicators, such as on time delivery, backlog efficiency or transport efficiency. The research was focused on optimizing the delivery performance of the industrial system, using linear programming. Modeling the delivery function using linear programming led to obtaining precise quantities to be produced and delivered each month by the industrial system in order to minimize their transport cost, satisfying their customers orders and to control their stock. The optimization led to a substantial improvement in all four performance indicators that concern deliveries.
Managing and learning with multiple models: Objectives and optimization algorithms
Probert, William J. M.; Hauser, C.E.; McDonald-Madden, E.; Runge, M.C.; Baxter, P.W.J.; Possingham, H.P.
2011-01-01
The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. ?? 2010 Elsevier Ltd.
Using a 4D-Variational Method to Optimize Model Parameters in an Intermediate Coupled Model of ENSO
Gao, C.; Zhang, R. H.
2017-12-01
Large biases exist in real-time ENSO prediction, which is attributed to uncertainties in initial conditions and model parameters. Previously, a four dimentional variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer (Te), which is empirically and explicitly related to sea level (SL) variation, written as Te=αTe×FTe (SL). The introduced parameter, αTe, represents the strength of the thermocline effect on sea surface temperature (SST; referred as the thermocline effect). A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having initial condition optimized only and having initial condition plus this additional model parameter optimized both are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameter and initial condition together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.
Gao, Chuan; Zhang, Rong-Hua; Wu, Xinrong; Sun, Jichang
2018-04-01
Large biases exist in real-time ENSO prediction, which can be attributed to uncertainties in initial conditions and model parameters. Previously, a 4D variational (4D-Var) data assimilation system was developed for an intermediate coupled model (ICM) and used to improve ENSO modeling through optimized initial conditions. In this paper, this system is further applied to optimize model parameters. In the ICM used, one important process for ENSO is related to the anomalous temperature of subsurface water entrained into the mixed layer ( T e), which is empirically and explicitly related to sea level (SL) variation. The strength of the thermocline effect on SST (referred to simply as "the thermocline effect") is represented by an introduced parameter, α Te. A numerical procedure is developed to optimize this model parameter through the 4D-Var assimilation of SST data in a twin experiment context with an idealized setting. Experiments having their initial condition optimized only, and having their initial condition plus this additional model parameter optimized, are compared. It is shown that ENSO evolution can be more effectively recovered by including the additional optimization of this parameter in ENSO modeling. The demonstrated feasibility of optimizing model parameters and initial conditions together through the 4D-Var method provides a modeling platform for ENSO studies. Further applications of the 4D-Var data assimilation system implemented in the ICM are also discussed.
Modeling and optimization of combined cytostatic and cytotoxic cancer chemotherapy.
Villasana, Minaya; Ochoa, Gabriela; Aguilar, Soraya
2010-11-01
This study extends a previous mathematical model of cancer cytotoxic chemotherapy, which considered cycling tumor cells and interactions with the immune system, by incorporating a different type of drug: a cytostatic agent. The effect of a cytostatic drug is to arrest cells in a phase of their cycle. In consequence, once tumor cells are arrested and synchronized they can be targeted with a cytotoxic agent, thus maximizing cell kill fraction and minimizing normal cell killing. The goal is to incorporate the new drug into the chemotherapy protocol and devise optimal delivery schedules. The problem of designing efficient combined chemotherapies is formulated as an optimal control problem and tackled using a state-of-the-art evolutionary algorithm for real-valued encoding, namely the covariance matrix adaptation evolution strategy. Alternative solution representations and three formulations of the underlying objective function are proposed and compared. The optimization problem was successfully solved by the proposed approach. The encoding that enforced non-overlapping (simultaneous) application of the two types of drugs produced competitive protocols with significant less amount of toxic drug, thus achieving better immune system health. When compared to treatment protocols that only consider a cytotoxic agent, the incorporation of a cytostatic drug dramatically improved the outcome and performance of the overall treatment, confirming in silico that the combination of a cytostatic with a cytotoxic agent improves the efficacy and efficiency of the chemotherapy. We conclude that the proposed approach can serve as a valuable decision support tool for the medical practitioner facing the complex problem of designing efficient combined chemotherapies. Copyright © 2010 Elsevier B.V. All rights reserved.
Modeling, design, and optimization of Mindwalker series elastic joint.
Wang, Shiqian; Meijneke, Cor; van der Kooij, Herman
2013-06-01
Weight and power autonomy are limiting the daily use of wearable exoskeleton. Lightweight, efficient and powerful actuation system are not easy to achieve. Choosing the right combinations of existing technologies, such as battery, gear and motor is not a trivial task. In this paper, we propose an optimization framework by setting up a power-based quasi-static model of the exoskeleton joint drivetrain. The goal is to find the most efficient and lightweight combinations. This framework can be generalized for other similar applications by extending or accommodating the model to their own needs. We also present the Mindwalker exoskeleton joint, for which a novel series elastic actuator, consisting of a ballscrew-driven linear actuator and a double spiral spring, was developed and tested. This linear actuator is capable of outputting 960 W power and the exoskeleton joint can output 100 Nm peak torque continuously. The double spiral spring can sense torque between 0.08Nm and 100 Nm and it exhibits linearity of 99.99%, with no backlash or hysteresis. The series elastic joint can track a chirp torque profile with amplitude of 100 Nm over 6 Hz (large torque bandwidth) and for small torque (2 Nm peak-to-peak), it has a bandwidth over 38 Hz. The integrated exoskeleton joint, including the ballscrew-driven linear actuator, the series spring, electronics and the metal housing which hosts these components, weighs 2.9 kg.
Sandfish numerical model reveals optimal swimming in sand
Maladen, Ryan; Ding, Yang; Kamor, Adam; Slatton, Andrew; Goldman, Daniel
2009-11-01
Motivated by experiment and theory examining the undulatory swimming of the sandfish lizard within granular media footnotetextMaladen et. al, Science, 325, 314, 2009, we study a numerical model of the sandfish as it swims within a validated soft sphere Molecular Dynamics granular media simulation. We hypothesize that features of its morphology and undulatory kinematics, and the granular media contribute to effective sand swimming. Our results agree with a resistive force model of the sandfish and show that speed and transport cost are optimized at a ratio of wave amplitude to wavelength of 0.2, irrespective of media properties and preparation. At this ratio, the entry of the animal into the media is fastest at an angle of 20^o, close to the angle of repose. We also find that the sandfish cross-sectional body shape reduces motion induced buoyancy within the granular media and that wave efficiency is sensitive to body-particle friction but independent of particle-particle friction.
Optimal waste load allocation using graph model for conflict resolution.
Saberi, Leila; Niksokhan, Mohammad Hossein
2017-03-01
In this paper, a new methodology is proposed for waste load allocation in river systems using the decision support system (DSS) for the graph model for conflict resolution II (GMCRII), multi-criteria decision making (MCDM) analysis and the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Minimization of total treatment and penalty costs and minimization of biological oxygen demand violation of standards at the check point are considered as the main objectives of this study. At first, the water quality along the river was simulated using the Streeter-Phelps (S-P) equation coupled with the MOPSO model. Thereby a trade-off curve between the objectives is obtained and a set of non-dominated solutions is selected. In the next step, the best alternative is chosen using MCDM techniques and the GMCRII DSS package and non-cooperative stability definitions. The applicability and efficiency of the methodology are examined in a real-world case study of the Sefidrud River in the northern part of Iran.
In silico strain optimization by adding reactions to metabolic models.
Correia, Sara; Rocha, Miguel
2012-07-24
Nowadays, the concerns about the environment and the needs to increase the productivity at low costs, demand for the search of new ways to produce compounds with industrial interest. Based on the increasing knowledge of biological processes, through genome sequencing projects, and high-throughput experimental techniques as well as the available computational tools, the use of microorganisms has been considered as an approach to produce desirable compounds. However, this usually requires to manipulate these organisms by genetic engineering and/ or changing the enviromental conditions to make the production of these compounds possible. In many cases, it is necessary to enrich the genetic material of those microbes with hereologous pathways from other species and consequently adding the potential to produce novel compounds. This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of selected microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The necessity of adding reactions to the metabolic model arises from existing gaps in the original model or motivated by the productions of new compounds by the organism. The optimization methods used are metaheuristics such as Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli.
A stochastic optimization model for shift scheduling in emergency departments.
El-Rifai, Omar; Garaix, Thierry; Augusto, Vincent; Xie, Xiaolan
2015-09-01
Excessive waiting time in Emergency Departments (ED) can be both a cause of frustration and more importantly, a health concern for patients. Waiting time arises when the demand for work goes beyond the facility's service capacity. ED service capacity mainly depends on human resources and on beds available for patients. In this paper, we focus on human resources organization in an ED and seek to best balance between service quality and working conditions. More specifically, we address the personnel scheduling problem in order to optimize the shift distribution among employees and minimize the total expected patients' waiting time. The problem is also characterized by a multi-stage re-entrant service process. With an appropriate approximation of patients' waiting times, we first propose a stochastic mixed-integer programming model that is solved by a sample average approximation (SAA) approach. The resulting personnel schedules are then evaluated using a discrete-event simulation model. Numerical experiments are then performed with data from a French hospital to compare different personnel scheduling strategies.
DEFF Research Database (Denmark)
Kristensen, Anders Ringgaard; Søllested, Thomas Algot
2004-01-01
improvements. The biological model of the replacement model is described in a previous paper and in this paper the optimization model is described. The model is developed as a prototype for use under practical conditions. The application of the model is demonstrated using data from two commercial Danish sow......Recent methodological improvements in replacement models comprising multi-level hierarchical Markov processes and Bayesian updating have hardly been implemented in any replacement model and the aim of this study is to present a sow replacement model that really uses these methodological...
Cancer Modeling: From Optimal Cell Renewal to Immunotherapy
Alvarado Alvarado, Cesar Leonardo
Cancer is a disease caused by mutations in normal cells. According to the National Cancer Institute, in 2016, an estimated 1.6 million people were diagnosed and approximately 0.5 million people died from the disease in the United States. There are many factors that shape cancer at the cellular and organismal level, including genetic, immunological, and environmental components. In this thesis, we show how mathematical modeling can be used to provide insight into some of the key mechanisms underlying cancer dynamics. First, we use mathematical modeling to investigate optimal homeostatic cell renewal in tissues such as the small intestine with an emphasis on division patterns and tissue architecture. We find that the division patterns that delay the accumulation of mutations are strictly associated with the population sizes of the tissue. In particular, patterns with long chains of differentiation delay the time to observe a second-hit mutant, which is important given that for many cancers two mutations are enough to initiate a tumor. We also investigated homeostatic cell renewal under a selective pressure and find that hierarchically organized tissues act as suppressors of selection; we find that an architecture with a small number of stem cells and larger pools of transit amplifying cells and mature differentiated cells, together with long chains of differentiation, form a robust evolutionary strategy to delay the time to observe a second-hit mutant when mutations acquire a fitness advantage or disadvantage. We also formulate a model of the immune response to cancer in the presence of costimulatory and inhibitory signals. We demonstrate that the coordination of such signals is crucial to initiate an effective immune response, and while immunotherapy has become a promising cancer treatment over the past decade, these results offer some explanations for why it can fail.
Energy balance of forage consumption by phyllophagous insects: optimization model
Directory of Open Access Journals (Sweden)
O. V. Tarasova
2015-06-01
Full Text Available The model of optimal food consumption by phytophagous insects proposed, in which the metabolic costs are presented in the form of two components – the cost of food utilization and costs for proper metabolism of the individuals. Two measures were introduced – the «price» of food conversion and the «price» of biomass synthesis of individuals to assess the effectiveness of food consumption by caterpillars. The proposed approach to the description of food consumption by insects provides the exact solutions of the equation of energy balance of food consumption and determining the effectiveness of consumption and the risk of death of the individual. Experiments on larvae’s feeding in laboratory conditions were carried out to verify the model. Caterpillars of Aporia crataegi L. (Lepidoptera, Pieridae were the research subjects. Supplydemand balance, calculated value of the environmental price of consumption and efficiency of food consumption for each individual were determined from experimental data. It was found that the fertility of the female does not depend on the weight of food consumed by it, but is linearly dependent on the food consumption efficiency index. The greater the efficiency of food consumption by an individual, the higher its fertility. The data obtained in the course of experiments on the feeding caterpillars Aporia crataegi were compared with the data presented in the works of other authors and counted in the proposed model of consumption. Calculations allowed estimation of the critical value of food conversion price below which the energy balance is negative and the existence of an individual is not possible.
Research on NC laser combined cutting optimization model of sheet metal parts
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.
Mansor, Zakwan; Zakaria, Mohd Zakimi; Nor, Azuwir Mohd; Saad, Mohd Sazli; Ahmad, Robiah; Jamaluddin, Hishamuddin
2017-09-01
This paper presents the black-box modelling of palm oil biodiesel engine (POB) using multi-objective optimization differential evolution (MOODE) algorithm. Two objective functions are considered in the algorithm for optimization; minimizing the number of term of a model structure and minimizing the mean square error between actual and predicted outputs. The mathematical model used in this study to represent the POB system is nonlinear auto-regressive moving average with exogenous input (NARMAX) model. Finally, model validity tests are applied in order to validate the possible models that was obtained from MOODE algorithm and lead to select an optimal model.
Diyana Rosli, Anis; Adenan, Nur Sabrina; Hashim, Hadzli; Ezan Abdullah, Noor; Sulaiman, Suhaimi; Baharudin, Rohaiza
2018-03-01
This paper shows findings of the application of Particle Swarm Optimization (PSO) algorithm in optimizing an Artificial Neural Network that could categorize between ripeness and unripeness stage of citrus suhuensis. The algorithm would adjust the network connections weights and adapt its values during training for best results at the output. Initially, citrus suhuensis fruit’s skin is measured using optically non-destructive method via spectrometer. The spectrometer would transmit VIS (visible spectrum) photonic light radiation to the surface (skin of citrus) of the sample. The reflected light from the sample’s surface would be received and measured by the same spectrometer in terms of reflectance percentage based on VIS range. These measured data are used to train and test the best optimized ANN model. The accuracy is based on receiver operating characteristic (ROC) performance. The result outcomes from this investigation have shown that the achieved accuracy for the optimized is 70.5% with a sensitivity and specificity of 60.1% and 80.0% respectively.
An optimization model for roadway pricing on rural freeways.
2012-02-01
The main objective of rural roadway pricing is revenue generation, rather than elimination of congestion externalities. This report presents a model that provides optimum tolls accounting for pavement deterioration and economic impacts. This model co...
Androgyny and Attachment Security: Two Related Models of Optimal Personality.
Shaver, Phillip R.; And Others
1996-01-01
Three studies explore similarities between attachment style typologies and sex role typologies. Both are defined by pairs of dimensions: self model and other model (attachment styles); masculinity, or agency, and femininity, or communion (sex role orientations). Discusses results. (KW)
A RECREATION OPTIMIZATION MODEL BASED ON THE TRAVEL COST METHOD
Hof, John G.; Loomis, John B.
1983-01-01
A recreation allocation model is developed which efficiently selects recreation areas and degree of development from an array of proposed and existing sites. The model does this by maximizing the difference between gross recreation benefits and travel, investment, management, and site-opportunity costs. The model presented uses the Travel Cost Method for estimating recreation benefits within an operations research framework. The model is applied to selection of potential wilderness areas in C...
Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
1993-01-01
Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...
Model reduction for dynamic real-time optimization of chemical processes
Van den Berg, J.
2005-01-01
The value of models in process industries becomes apparent in practice and literature where numerous successful applications are reported. Process models are being used for optimal plant design, simulation studies, for off-line and online process optimization. For online optimization applications
Modeling of aerodynamic Space-to-Surface flight with optimal trajectory for targeting
Gornev, Serge
2003-01-01
Modeling has been created for a Space-to-Surface system defined for an optimal trajectory for targeting in terminal phase. The modeling includes models for simulation atmosphere, speed of sound, aerodynamic flight and navigation by an infrared system. The modeling simulation includes statistical analysis of the modeling results.
Multi-objective optimization of the management of a waterworks using an integrated well field model
DEFF Research Database (Denmark)
Hansen, Annette Kirstine; Bauer-Gottwein, Peter; Rosbjerg, Dan
2012-01-01
This study uses multi-objective optimization of an integrated well field model to improve the management of a waterworks. The well field model, called WELLNES (WELL field Numerical Engine Shell) is a dynamic coupling of a groundwater model, a pipe network model, and a well model. WELLNES is capab...
Optimal Scaling of Interaction Effects in Generalized Linear Models
J.M. van Rosmalen (Joost); A.J. Koning (Alex); P.J.F. Groenen (Patrick)
2007-01-01
textabstractMultiplicative interaction models, such as Goodman's RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are only suitable for data sets with two or three predictor variables. Here, we discuss an
Parameter optimization method for the water quality dynamic model based on data-driven theory.
Liang, Shuxiu; Han, Songlin; Sun, Zhaochen
2015-09-15
Parameter optimization is important for developing a water quality dynamic model. In this study, we applied data-driven method to select and optimize parameters for a complex three-dimensional water quality model. First, a data-driven model was developed to train the response relationship between phytoplankton and environmental factors based on the measured data. Second, an eight-variable water quality dynamic model was established and coupled to a physical model. Parameter sensitivity analysis was investigated by changing parameter values individually in an assigned range. The above results served as guidelines for the control parameter selection and the simulated result verification. Finally, using the data-driven model to approximate the computational water quality model, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the control parameters. The optimization routines and results were analyzed and discussed based on the establishment of the water quality model in Xiangshan Bay (XSB). Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
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.
Optimal control of nutrition restricted dynamics model of Microalgae biomass growth model
Ratianingsih, R.; Azim; Nacong, N.; Resnawati; Mardlijah; Widodo, B.
2017-12-01
The biomass of the microalgae is very potential to be proposed as an alternative renewable energy resources because it could be extracted into lipid. Afterward, the lipid could be processed to get the biodiesel or bioethanol. The extraction of the biomass on lipid synthesis process is very important to be studied because the process just gives some amount of lipid. A mathematical model of restricted microalgae biomass growth just gives 1/3 proportion of lipid with respect to the biomass in the synthesis process. An optimal control is designed to raise the ratio between the number of lipid formation and the microalgae biomass to be used in synthesis process. The minimum/ Pontryagin maximum principle is used to get the optimal lipid production. The simulation shows that the optimal lipid formation could be reach by simultaneously controlling the carbon dioxide, in the respiration and photosynthesis the process, and intake nutrition rates of liquid waste and urea substrate. The production of controlled microalgae lipid could be increase 6.5 times comparing to the uncontrolled one.
Optimization of a new flow design for solid oxide cells using computational fluid dynamics modelling
DEFF Research Database (Denmark)
Duhn, Jakob Dragsbæk; Jensen, Anker Degn; Wedel, Stig
2016-01-01
Design of a gas distributor to distribute gas flow into parallel channels for Solid Oxide Cells (SOC) is optimized, with respect to flow distribution, using Computational Fluid Dynamics (CFD) modelling. The CFD model is based on a 3d geometric model and the optimized structural parameters include...
Ikkersheim, David; Tanke, Marit; van Schooten, Gwendy; de Bresser, Niels; Fleuren, Hein
2013-06-16
The majority of curative health care is organized in hospitals. As in most other countries, the current 94 hospital locations in the Netherlands offer almost all treatments, ranging from rather basic to very complex care. Recent studies show that concentration of care can lead to substantial quality improvements for complex conditions and that dispersion of care for chronic conditions may increase quality of care. In previous studies on allocation of hospital infrastructure, the allocation is usually only based on accessibility and/or efficiency of hospital care. In this paper, we explore the possibilities to include a quality function in the objective function, to give global directions to how the 'optimal' hospital infrastructure would be in the Dutch context. To create optimal societal value we have used a mathematical mixed integer programming (MIP) model that balances quality, efficiency and accessibility of care for 30 ICD-9 diagnosis groups. Typical aspects that are taken into account are the volume-outcome relationship, the maximum accepted travel times for diagnosis groups that may need emergency treatment and the minimum use of facilities. The optimal number of hospital locations per diagnosis group varies from 12-14 locations for diagnosis groups which have a strong volume-outcome relationship, such as neoplasms, to 150 locations for chronic diagnosis groups such as diabetes and chronic obstructive pulmonary disease (COPD). In conclusion, our study shows a new approach for allocating hospital infrastructure over a country or certain region that includes quality of care in relation to volume per provider that can be used in various countries or regions. In addition, our model shows that within the Dutch context chronic care may be too concentrated and complex and/or acute care may be too dispersed. Our approach can relatively easily be adopted towards other countries or regions and is very suitable to perform a 'what-if' analysis.
Decision Models for Determining the Optimal Life Test Sampling Plans
Nechval, Nicholas A.; Nechval, Konstantin N.; Purgailis, Maris; Berzins, Gundars; Strelchonok, Vladimir F.
2010-11-01
Life test sampling plan is a technique, which consists of sampling, inspection, and decision making in determining the acceptance or rejection of a batch of products by experiments for examining the continuous usage time of the products. In life testing studies, the lifetime is usually assumed to be distributed as either a one-parameter exponential distribution, or a two-parameter Weibull distribution with the assumption that the shape parameter is known. Such oversimplified assumptions can facilitate the follow-up analyses, but may overlook the fact that the lifetime distribution can significantly affect the estimation of the failure rate of a product. Moreover, sampling costs, inspection costs, warranty costs, and rejection costs are all essential, and ought to be considered in choosing an appropriate sampling plan. The choice of an appropriate life test sampling plan is a crucial decision problem because a good plan not only can help producers save testing time, and reduce testing cost; but it also can positively affect the image of the product, and thus attract more consumers to buy it. This paper develops the frequentist (non-Bayesian) decision models for determining the optimal life test sampling plans with an aim of cost minimization by identifying the appropriate number of product failures in a sample that should be used as a threshold in judging the rejection of a batch. The two-parameter exponential and Weibull distributions with two unknown parameters are assumed to be appropriate for modelling the lifetime of a product. A practical numerical application is employed to demonstrate the proposed approach.
Models as instruments for optimizing hospital processes: a systematic review.
van Sambeek, J R C; Cornelissen, F A; Bakker, P J M; Krabbendam, J J
2010-01-01
The purpose of this article is to find decision-making models for the design and control of processes regarding patient flows, considering various problem types, and to find out how usable these models are for managerial decision making. A systematic review of the literature was carried out. Relevant literature from three databases was selected based on inclusion and exclusion criteria and the results were analyzed. A total of 68 articles were selected. Of these, 31 contained computer simulation models, ten contained descriptive models, and 27 contained analytical models. The review showed that descriptive models are only applied to process design problems, and that analytical and computer simulation models are applied to all types of problems to approximately the same extent. Only a few models have been validated in practice, and it seems that most models are not used for their intended purpose: to support management in decision making. The comparability of the relevant databases appears to be limited and there is an insufficient number of suitable keywords and MeSH headings, which makes searching systematically within the broad field of health care management relatively hard to accomplish. The findings give managers insight into the characteristics of various types of decision-support models and into the kinds of situations in which they are used. This is the first time literature on various kinds of models for supporting managerial decision making in hospitals has been systematically collected and assessed.
Optimization model using Markowitz model approach for reducing the number of dengue cases in Bandung
Yong, Benny; Chin, Liem
2017-05-01
Dengue fever is one of the most serious diseases and this disease can cause death. Currently, Indonesia is a country with the highest cases of dengue disease in Southeast Asia. Bandung is one of the cities in Indonesia that is vulnerable to dengue disease. The sub-districts in Bandung had different levels of relative risk of dengue disease. Dengue disease is transmitted to people by the bite of an Aedesaegypti mosquito that is infected with a dengue virus. Prevention of dengue disease is by controlling the vector mosquito. It can be done by various methods, one of the methods is fogging. The efforts made by the Health Department of Bandung through fogging had constraints in terms of limited funds. This problem causes Health Department selective in fogging, which is only done for certain locations. As a result, many sub-districts are not handled properly by the Health Department because of the unequal distribution of activities to prevent the spread of dengue disease. Thus, it needs the proper allocation of funds to each sub-district in Bandung for preventing dengue transmission optimally. In this research, the optimization model using Markowitz model approach will be applied to determine the allocation of funds should be given to each sub-district in Bandung. Some constraints will be added to this model and the numerical solution will be solved with generalized reduced gradient method using Solver software. The expected result of this research is the proportion of funds given to each sub-district in Bandung correspond to the level of risk of dengue disease in each sub-district in Bandung so that the number of dengue cases in this city can be reduced significantly.
The Optimal Licensing Contract in a Differentiated Stackelberg Model
Hong, Xianpei; Yang, Lijun; Zhang, Huaige; Zhao, Dan
2014-01-01
This paper extends the work of Wang (2002) by considering a differentiated Stackelberg model, when the leader firm is an inside innovator and licenses its new technology by three options, that is, fixed-fee licensing, royalty licensing, and two-part tariff licensing. The main contributions and conclusions of this paper are threefold. First of all, this paper derives a very different result from Wang (2002). We show that, with a nondrastic innovation, royalty licensing is always better than fixed-fee licensing for the innovator; with a drastic innovation, royalty licensing is superior to fixed-fee licensing for small values of substitution coefficient d; however when d becomes closer to 1, neither fee nor royalty licensing will occur. Secondly, this paper shows that the innovator is always better off in case of two-part tariff licensing than fixed-fee licensing no matter what the innovation size is. Thirdly, the innovator always prefers to license its nondrastic innovation by means of a two-part tariff instead of licensing by means of a royalty; however, with a drastic innovation, the optimal licensing strategy can be either a two-part tariff or a royalty, depending upon the differentiation of the goods. PMID:24683342
OPTIMAL TRAINING POLICY FOR PROMOTION - STOCHASTIC MODELS OF MANPOWER SYSTEMS
Directory of Open Access Journals (Sweden)
V.S.S. Yadavalli
2012-01-01
Full Text Available In this paper, the optimal planning of manpower training programmes in a manpower system with two grades is discussed. The planning of manpower training within a given organization involves a trade-off between training costs and expected return. These planning problems are examined through models that reflect the random nature of manpower movement in two grades. To be specific, the system consists of two grades, grade 1 and grade 2. Any number of persons in grade 2 can be sent for training and after the completion of training, they will stay in grade 2 and will be given promotion as and when vacancies arise in grade 1. Vacancies arise in grade 1 only by wastage. A person in grade 1 can leave the system with probability p. Vacancies are filled with persons in grade 2 who have completed the training. It is assumed that there is a perfect passing rate and that the sizes of both grades are fixed. Assuming that the planning horizon is finite and is T, the underlying stochastic process is identified as a finite state Markov chain and using dynamic programming, a policy is evolved to determine how many persons should be sent for training at any time k so as to minimize the total expected cost for the entire planning period T.
Portfolio size as funktion of the premium: modeling and optimization
DEFF Research Database (Denmark)
Asmussen, Søren; Christensen, Bent Jesper; Taksar, Michael I
, where r is the risk-free interest rate. Based on calculations of the customers' present values of the alternative strategies of insuring and not insuring, the portfolio size n(p) is derived, and also the rate of claims from the insured customers is given. Further, the value of p which is optimal......An insurance company has a large number N of potential customers characterized by i.i.d. r.v.'s A1,…,AN giving the arrival rates of claims. Customers are risk averse, and a customer accepts an offered premium p according to his A-value. The modeling further involves a discount rate d>r of customers...... for minimizing the ruin probability is derived in a diffusion approximation to the Cramér-Lundberg risk process with an added liability rate L of the company. The solution involves the Lambert W function. Similar discussion is given for extensions involving customers having only partial information...
The Optimal Licensing Contract in a Differentiated Stackelberg Model
Directory of Open Access Journals (Sweden)
Xianpei Hong
2014-01-01
Full Text Available This paper extends the work of Wang (2002 by considering a differentiated Stackelberg model, when the leader firm is an inside innovator and licenses its new technology by three options, that is, fixed-fee licensing, royalty licensing, and two-part tariff licensing. The main contributions and conclusions of this paper are threefold. First of all, this paper derives a very different result from Wang (2002. We show that, with a nondrastic innovation, royalty licensing is always better than fixed-fee licensing for the innovator; with a drastic innovation, royalty licensing is superior to fixed-fee licensing for small values of substitution coefficient d; however when d becomes closer to 1, neither fee nor royalty licensing will occur. Secondly, this paper shows that the innovator is always better off in case of two-part tariff licensing than fixed-fee licensing no matter what the innovation size is. Thirdly, the innovator always prefers to license its nondrastic innovation by means of a two-part tariff instead of licensing by means of a royalty; however, with a drastic innovation, the optimal licensing strategy can be either a two-part tariff or a royalty, depending upon the differentiation of the goods.
The optimal licensing contract in a differentiated Stackelberg model.
Hong, Xianpei; Yang, Lijun; Zhang, Huaige; Zhao, Dan
2014-01-01
This paper extends the work of Wang (2002) by considering a differentiated Stackelberg model, when the leader firm is an inside innovator and licenses its new technology by three options, that is, fixed-fee licensing, royalty licensing, and two-part tariff licensing. The main contributions and conclusions of this paper are threefold. First of all, this paper derives a very different result from Wang (2002). We show that, with a nondrastic innovation, royalty licensing is always better than fixed-fee licensing for the innovator; with a drastic innovation, royalty licensing is superior to fixed-fee licensing for small values of substitution coefficient d; however when d becomes closer to 1, neither fee nor royalty licensing will occur. Secondly, this paper shows that the innovator is always better off in case of two-part tariff licensing than fixed-fee licensing no matter what the innovation size is. Thirdly, the innovator always prefers to license its nondrastic innovation by means of a two-part tariff instead of licensing by means of a royalty; however, with a drastic innovation, the optimal licensing strategy can be either a two-part tariff or a royalty, depending upon the differentiation of the goods.
Li, Zejing
2012-01-01
This dissertation is mainly devoted to the research of two problems - the continuous-time portfolio optimization in different Wishart models and the effects of discrete rebalancing on portfolio wealth distribution and optimal portfolio strategy.
Multiobjective Optimization for the Forecasting Models on the Base of the Strictly Binary Trees
Nadezhda Astakhova; Liliya Demidova; Evgeny Nikulchev
2016-01-01
The optimization problem dealing with the development of the forecasting models on the base of strictly binary trees has been considered. The aim of paper is the comparative analysis of two optimization variants which are applied for the development of the forecasting models. Herewith the first optimization variant assumes the application of one quality indicator of the forecasting model named as the affinity indicator and the second variant realizes the application of two quality indicators ...
On selection of optimal stochastic model for accelerated life testing
International Nuclear Information System (INIS)
Volf, P.; Timková, J.
2014-01-01
This paper deals with the problem of proper lifetime model selection in the context of statistical reliability analysis. Namely, we consider regression models describing the dependence of failure intensities on a covariate, for instance, a stressor. Testing the model fit is standardly based on the so-called martingale residuals. Their analysis has already been studied by many authors. Nevertheless, the Bayes approach to the problem, in spite of its advantages, is just developing. We shall present the Bayes procedure of estimation in several semi-parametric regression models of failure intensity. Then, our main concern is the Bayes construction of residual processes and goodness-of-fit tests based on them. The method is illustrated with both artificial and real-data examples. - Highlights: • Statistical survival and reliability analysis and Bayes approach. • Bayes semi-parametric regression modeling in Cox's and AFT models. • Bayes version of martingale residuals and goodness-of-fit test
Constitutional Justice Procedure in Lithuania: a Search for Optimal Model
Pūraitė-Andrikienė, Dovilė
2017-01-01
The dissertation systematically analyzes the preconditions for optimising the existing constitutional justice model, i.e. whether the current model meets the expectations of Lithuanian society and the legal community, corresponds to the capabilities of the legal system, and is in line with the tendencies of constitutional justice in European states, identifies the problematic aspects of the existing constitutional justice model and brings forward proposals regarding how the legal regulation c...
Supersparse Linear Integer Models for Optimized Medical Scoring Systems
Ustun, Berk; Rudin, Cynthia
2015-01-01
Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction. These models are in widespread use by the medical community, but are difficult to learn from data because they need to be accurate and sparse, have coprime integer coefficients, and satisfy multiple operational constraints. We present a new method for creating data-driven scoring systems called a Supersparse Linear Integer Model (SLIM). SLIM...
International Nuclear Information System (INIS)
Moosavi, K.; Setayeshi, S.; Maragheh, M.Gh.; Ahmadi, S.J.; Kardan, M.R.; Banaem, L.M.
2009-01-01
In this study, an experimental design using artificial neural networks for an optimization on the strontium separation model for fission products (inactive trace elements) is investigated. The goal is to optimize the separation parameters to achieve maximum amount of strontium that is separated from the fission products. The result of the optimization method causes a proper purity of Strontium-89 that was separated from the fission products. It is also shown that ANN may be establish a method to optimize the separation model.
One-Stage and Bayesian Two-Stage Optimal Designs for Mixture Models
Lin, Hefang
1999-01-01
In this research, Bayesian two-stage D-D optimal designs for mixture experiments with or without process variables under model uncertainty are developed. A Bayesian optimality criterion is used in the first stage to minimize the determinant of the posterior variances of the parameters. The second stage design is then generated according to an optimality procedure that collaborates with the improved model from first stage data. Our results show that the Bayesian two-stage D-D optimal design...
Optimization of dynamic MOSA model parameters using ATP/EMTP software tool
Directory of Open Access Journals (Sweden)
Jasika Ranko
2017-01-01
Full Text Available This paper demonstrates the procedure for estimating parameters of a dynamic metal-oxide surge arrester model by using a genetic algorithm, implemented in ATP/EMTP graphic preprocessor (ATPDraw optimization module. The advantages of new ATPDraw options that allow optimization of electric circuit elements are shown. The optimization process is applied to two frequency-dependent MOSA models. At the end of the work, a comparison of results obtained before and after optimization is given.
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)
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.
Dynamic ASE Modeling and Optimization of Aircraft with SpaRibs, Phase I
National Aeronautics and Space Administration — We propose development and demonstration of a dynamic aeroservoelastic modeling and optimization system based on curvilinear internal structural arrangements of...
Models and optimization of solar-control automotive glasses
Blume, Russell Dale
Efforts to develop automotive glasses with enhanced solar control characteristics have been motivated by the desire for increased consumer comfort, reduced air-conditioning loads, and improved fuel-economy associated with a reduction in the total solar energy transmitted into the automotive interior. In the current investigation, the base soda-lime-silicate glass (72.7 wt.% SiO 2, 14.2% Na2O, 10.0% CaO, 2.5% MgO, 0.6% Al2O 3 with 0.3 Na2SO4 added to the batch as a fining agent) was modified with Fe2O3 (0.0 to 0.8%), NiO (0.0 to 0.15%), CoO (0.0 to 0.15%), V2O5 (0.0 to 0.225%), TiO2 (0.0 to 1.5%), SnO (0.0 to 3.0%), ZnS (0.0 to 0.09%), ZnO (0.0 to 2.0%), CaF2 (0.0 to 2.0%), and P2O5 (0.0 to 2.0%) to exploit reported non-linear mechanistic interactions among the dopants by which the solar-control characteristics of the base glass can be modified. Due to the large number of experimental variables under consideration, a D-optimal experimental design methodology was utilized to model the solar-optical properties as a function of batch composition. The independent variables were defined as the calculated batch concentrations of the primary (Fe2O 3, NiO, CoO, V2O5) and interactive (CaF2 , P2O5, SnO, ZnS, ZnO, TiO2) dopants in the glass. The dependent variable was defined as the apparent optical density over a wavelength range of 300--2700 nm at 10 nm intervals. The model form relating the batch composition to the apparent optical density was a modified Lambert-Beer absorption law; which, in addition to the linear terms, contained quadratic terms of the primary dopants, and a series of binary and ternary non-linear interactions amongst the primary and interactive dopants. Utilizing the developed model, exceptional fit in terms of both the discrete response (the transmission curves) and the integrated response (visible and solar transmittance) were realized. Glasses utilizing Fe2O 3, CoO, NiO, V2O5, ZnO and P2O 5 have generated innovative glasses with substantially improved
Schwaab, Douglas G.
1991-01-01
A mathematical programing model is presented to optimize the selection of Orbital Replacement Unit on-orbit spares for the Space Station. The model maximizes system availability under the constraints of logistics resupply-cargo weight and volume allocations.
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
Modeling and Optimization of an Electrostatic Energy Harvesting Device
DEFF Research Database (Denmark)
Crovetto, Andrea; Wang, Fei; Hansen, Ole
2014-01-01
Modeling of energy harvesting devices is complicated by the coupling between electrical and mechanical domains. In this paper, we present a coupled electromechanical model for electret-based resonant energy harvesters where the two output pads are placed on the same device side (single...
Optimal Number of States in Hidden Markov Models and its ...
African Journals Online (AJOL)
In this paper, Hidden Markov Model is applied to model human movements as to facilitate an automatic detection of the same. A number of activities were simulated with the help of two persons. The four movements considered are walking, sitting down-getting up, fall while walking and fall while standing. The data is ...
Models as instruments for optimizing hospital processes: a systematic review
van Sambeek, J. R. C.; Cornelissen, F. A.; Bakker, P. J. M.; Krabbendam, J. J.
2010-01-01
PURPOSE: The purpose of this article is to find decision-making models for the design and control of processes regarding patient flows, considering various problem types, and to find out how usable these models are for managerial decision making. DESIGN/METHODOLOGY/APPROACH: A systematic review of
On the Utility of Island Models in Dynamic Optimization
DEFF Research Database (Denmark)
Lissovoi, Andrei; Witt, Carsten
2015-01-01
A simple island model with λ islands and migration occurring after every τ iterations is studied on the dynamic fitness function Maze. This model is equivalent to a (1+λ) EA if τ=1, i.e., migration occurs during every iteration. It is proved that even for an increased offspring population size up...
Using parallel computing in modeling and optimization of mineral ...
African Journals Online (AJOL)
UPIT), or a maximum weight closure problem. There are several method for solving this problem. We provide new approach, for solving ultimate pit limit problem using precedence model. Block model of open pit can be easily represented as an ...
Creating a Linear Model to Optimize Satellite Communication Bandwidth Utilization
National Research Council Canada - National Science Library
Stone, David A
2006-01-01
.... The paper then presents an example of a linear model that could be expanded for implementation and used for actual problem analysis. The final section of the paper describes areas that require further study and additional steps that must be taken to convert the concept presented in this paper to an actual model suitable for use.
Regression Model Optimization for the Analysis of Experimental Data
Ulbrich, N.
2009-01-01
A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.
Optimization and evaluation of probabilistic-logic sequence models
DEFF Research Database (Denmark)
Christiansen, Henning; Lassen, Ole Torp
Analysis of biological sequence data demands more and more sophisticated and fine-grained models, but these in turn introduce hard computational problems. A class of probabilistic-logic models is considered, which increases the expressibility from HMM's and SCFG's regular and context-free languages...
DEFF Research Database (Denmark)
Ding, Tao; Li, Cheng; Huang, Can
2018-01-01
In order to solve the reactive power optimization with joint transmission and distribution networks, a hierarchical modeling method is proposed in this paper. It allows the reactive power optimization of transmission and distribution networks to be performed separately, leading to a master–slave...... function of the slave model for the master model, which reflects the impacts of each slave model. Second, the transmission and distribution networks are decoupled at feeder buses, and all the distribution networks are coordinated by the master reactive power optimization model to achieve the global...
On application of optimal control to SEIR normalized models: Pros and cons.
de Pinho, Maria do Rosario; Nogueira, Filipa Nunes
2017-02-01
In this work we normalize a SEIR model that incorporates exponential natural birth and death, as well as disease-caused death. We use optimal control to control by vaccination the spread of a generic infectious disease described by a normalized model with L1 cost. We discuss the pros and cons of SEIR normalized models when compared with classical models when optimal control with L1 costs are considered. Our discussion highlights the role of the cost. Additionally, we partially validate our numerical solutions for our optimal control problem with normalized models using the Maximum Principle.
Optimized dynamical decoupling in a model quantum memory.
Biercuk, Michael J; Uys, Hermann; VanDevender, Aaron P; Shiga, Nobuyasu; Itano, Wayne M; Bollinger, John J
2009-04-23
Any quantum system, such as those used in quantum information or magnetic resonance, is subject to random phase errors that can dramatically affect the fidelity of a desired quantum operation or measurement. In the context of quantum information, quantum error correction techniques have been developed to correct these errors, but resource requirements are extraordinary. The realization of a physically tractable quantum information system will therefore be facilitated if qubit (quantum bit) error rates are far below the so-called fault-tolerance error threshold, predicted to be of the order of 10(-3)-10(-6). The need to realize such low error rates motivates a search for alternative strategies to suppress dephasing in quantum systems. Here we experimentally demonstrate massive suppression of qubit error rates by the application of optimized dynamical decoupling pulse sequences, using a model quantum system capable of simulating a variety of qubit technologies. We demonstrate an analytically derived pulse sequence, UDD, and find novel sequences through active, real-time experimental feedback. The latter sequences are tailored to maximize error suppression without the need for a priori knowledge of the ambient noise environment, and are capable of suppressing errors by orders of magnitude compared to other existing sequences (including the benchmark multi-pulse spin echo). Our work includes the extension of a treatment to predict qubit decoherence under realistic conditions, yielding strong agreement between experimental data and theory for arbitrary pulse sequences incorporating nonidealized control pulses. These results demonstrate the robustness of qubit memory error suppression through dynamical decoupling techniques across a variety of qubit technologies.
Oneida Tribe of Indians of Wisconsin Energy Optimization Model
Energy Technology Data Exchange (ETDEWEB)
Troge, Michael [Little Bear Development Center, Oneida, WI (United States)
2014-12-01
Oneida Nation is located in Northeast Wisconsin. The reservation is approximately 96 square miles (8 miles x 12 miles), or 65,000 acres. The greater Green Bay area is east and adjacent to the reservation. A county line roughly splits the reservation in half; the west half is in Outagamie County and the east half is in Brown County. Land use is predominantly agriculture on the west 2/3 and suburban on the east 1/3 of the reservation. Nearly 5,000 tribally enrolled members live in the reservation with a total population of about 21,000. Tribal ownership is scattered across the reservation and is about 23,000 acres. Currently, the Oneida Tribe of Indians of Wisconsin (OTIW) community members and facilities receive the vast majority of electrical and natural gas services from two of the largest investor-owned utilities in the state, WE Energies and Wisconsin Public Service. All urban and suburban buildings have access to natural gas. About 15% of the population and five Tribal facilities are in rural locations and therefore use propane as a primary heating fuel. Wood and oil are also used as primary or supplemental heat sources for a small percent of the population. Very few renewable energy systems, used to generate electricity and heat, have been installed on the Oneida Reservation. This project was an effort to develop a reasonable renewable energy portfolio that will help Oneida to provide a leadership role in developing a clean energy economy. The Energy Optimization Model (EOM) is an exploration of energy opportunities available to the Tribe and it is intended to provide a decision framework to allow the Tribe to make the wisest choices in energy investment with an organizational desire to establish a renewable portfolio standard (RPS).
Models and Methods for Structural Topology Optimization with Discrete Design Variables
DEFF Research Database (Denmark)
Stolpe, Mathias
Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used...... 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...
Dinh, Tao; Nguyen, Ngoc
2015-01-01
This proceedings set contains 85 selected full papers presented at the 3rd International Conference on Modelling, Computation and Optimization in Information Systems and Management Sciences - MCO 2015, held on May 11–13, 2015 at Lorraine University, France. The present part I of the 2 volume set includes articles devoted to Combinatorial optimization and applications, DC programming and DCA: thirty years of Developments, Dynamic Optimization, Modelling and Optimization in financial engineering, Multiobjective programming, Numerical Optimization, Spline Approximation and Optimization, as well as Variational Principles and Applications
Modeling and optimization of the lifetime of technologies
Hritonenko, Natali
1996-01-01
Modern economic growth is characterized by structural changes based on the introduction of new technologies into economics. The replacement and renova tion of technologies in industrial environments undergoing technical change is clearly one of the key aspects of economic development. The mathematical modeling of evolutionary economics under technical change (TC) has been rigorously considered by many authors during last decades. There is a wide variety of economic approaches and models describing different aspects of technical change. Among these are the models of embodied technical progress [19], [35], [70], [129], endogenous growth models [94], [102], the models of technological innovations [31], [32], [41], and others. The perspective self organization evolutionary approach is developed in [20], [38], [122], [123], [124], [126], which unites the aspects of diffusion of new technologies, technological and behavioral diversity of firms, learning mechanisms, age-dependent effects, and other important fea...
Research of Models for Evaluating the Optimal Level of Information Security Investment
Directory of Open Access Journals (Sweden)
A. S. Polyakova
2012-03-01
Full Text Available This article presents a research of Gordon—Loeb model for evaluating the optimal level of information security investment and a model of interdependent risks. One is provided by practical recommendations for choosing and applying models, for choosing a range of vulnerabilities to concentrate financial resources on, and weak points of models are discussed.
Physically based modelling and optimal operation for product drying during post-harvest processing.
Boxtel, van A.J.B.; Lukasse, L.; Farkas, I.; Rendik, Z.
1996-01-01
The development of new procedures for crop production and post-harvest processing requires models. Models based on physical backgrounds are most useful for this purpose because of their extrapolation potential. An optimal procedure is developed for alfalfa drying using a physical model. The model
An Optimization Model and Modified Harmony Search Algorithm for Microgrid Planning with ESS
Directory of Open Access Journals (Sweden)
Yang Jiao
2017-01-01
Full Text Available To solve problems such as the high cost of microgrids (MGs, balance between supply and demand, stability of system operation, and optimizing the MG planning model, the energy storage system (ESS and harmony search algorithm (HSA are proposed. First, the conventional MG planning optimization model is constructed and the constraint conditions are defined: the supply and demand balance and reserve requirements. Second, an ESS is integrated into the optimal model of MG planning. The model with an ESS can solve and identify parameters such as the optimal power, optimal capacity, and optimal installation year. Third, the convergence speed and robustness of the ESS are optimized and improved. A case study comprising three different cases concludes the paper. The results show that the modified HSA (MHSA can effectively improve the stability and economy of MG operation with an ESS.
Directory of Open Access Journals (Sweden)
Sie Long Kek
2015-01-01
Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.
Models and Methods for Structural Topology Optimization with Discrete Design Variables
DEFF Research Database (Denmark)
Stolpe, Mathias
Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used...... or stresses, or fundamental frequencies. The design variables are either continuous or discrete and model dimensions, thicknesses, densities, or material properties. Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures......, densities, or material properties. This thesis is devoted to the development of mathematical models, theory, and advanced numerical optimization methods for solving structural topology optimization problems with discrete design variables to proven global optimality. The thesis begins with an introduction...
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
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...... 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...... is found, confirming the physical validity of the utilized optimization model. Two topology optimized designs are exemplarily benchmarked against a size optimized parallel fin heat sink and an up to 13.6% lower thermal resistance is found to be realized by the topology optimization....
Modelling, simulating and optimizing boiler heating surfaces and evaporator circuits
DEFF Research Database (Denmark)
Sørensen, K.; Condra, T.; Houbak, Niels
2003-01-01
developed as a Differential-Algebraic-Equation system (DAE) and MATLAB has been applied for the integration of the models. In general MATLAB has proved to be very stable for these DAE systems. Experimental verification has been carried out at a full scale plant equipped with instrumentation to verify heat....... 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...
DEFF Research Database (Denmark)
Suárez, Carlos Gómez; Reigosa, Paula Diaz; Iannuzzo, Francesco
2016-01-01
An original tool for parameter extraction of PSpice models has been released, enabling a simple parameter identification. A physics-based IGBT model is used to demonstrate that the optimization tool is capable of generating a set of parameters which predicts the steady-state and switching behavior...
Membrane Modeling, Simulation and Optimization for Propylene/Propane Separation
Alshehri, Ali
2015-06-01
Energy efficiency is critical for sustainable industrial growth and the reduction of environmental impacts. Energy consumption by the industrial sector accounts for more than half of the total global energy usage and, therefore, greater attention is focused on enhancing this sector’s energy efficiency. It is predicted that by 2020, more than 20% of today’s energy consumption can be avoided in countries that have effectively implemented an action plan towards efficient energy utilization. Breakthroughs in material synthesis of high selective membranes have enabled the technology to be more energy efficient. Hence, high selective membranes are increasingly replacing conventional energy intensive separation processes, such as distillation and adsorption units. Moreover, the technology offers more special features (which are essential for special applications) and its small footprint makes membrane technology suitable for platform operations (e.g., nitrogen enrichment for oil and gas offshore sites). In addition, its low maintenance characteristics allow the technology to be applied to remote operations. For these reasons, amongst other, the membrane technology market is forecast to reach $16 billion by 2017. This thesis is concerned with the engineering aspects of membrane technology and covers modeling, simulation and optimization of membranes as a stand-alone process or as a unit operation within a hybrid system. Incorporating the membrane model into a process modeling software simplifies the simulation and optimization of the different membrane processes and hybrid configurations, since all other unit operations are pre-configured. Various parametric analyses demonstrated that only the membrane selectivity and transmembrane pressure ratio parameters define a membrane’s ability to accomplish a certain separation task. Moreover, it was found that both membrane selectivity and pressure ratio exhibit a minimum value that is only defined by the feed composition
Selecting an optimal mixed products using grey relationship model
Directory of Open Access Journals (Sweden)
Farshad Faezy Razi
2013-06-01
Full Text Available This paper presents an integrated supplier selection and inventory management using grey relationship model (GRM as well as multi-objective decision making process. The proposed model of this paper first ranks different suppliers based on GRM technique and then determines the optimum level of inventory by considering different objectives. To show the implementation of the proposed model, we use some benchmark data presented by Talluri and Baker [Talluri, S., & Baker, R. C. (2002. A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 141(3, 544-558.]. The preliminary results indicate that the proposed model of this paper is capable of handling different criteria for supplier selection.
An Algorithm for Optimally Fitting a Wiener Model
Directory of Open Access Journals (Sweden)
Lucas P. Beverlin
2011-01-01
Full Text Available The purpose of this work is to present a new methodology for fitting Wiener networks to datasets with a large number of variables. Wiener networks have the ability to model a wide range of data types, and their structures can yield parameters with phenomenological meaning. There are several challenges to fitting such a model: model stiffness, the nonlinear nature of a Wiener network, possible overfitting, and the large number of parameters inherent with large input sets. This work describes a methodology to overcome these challenges by using several iterative algorithms under supervised learning and fitting subsets of the parameters at a time. This methodology is applied to Wiener networks that are used to predict blood glucose concentrations. The predictions of validation sets from models fit to four subjects using this methodology yielded a higher correlation between observed and predicted observations than other algorithms, including the Gauss-Newton and Levenberg-Marquardt algorithms.
Modeling Illicit Drug Use Dynamics and Its Optimal Control Analysis
Directory of Open Access Journals (Sweden)
Steady Mushayabasa
2015-01-01
Full Text Available The global burden of death and disability attributable to illicit drug use, remains a significant threat to public health for both developed and developing nations. This paper presents a new mathematical modeling framework to investigate the effects of illicit drug use in the community. In our model the transmission process is captured as a social “contact” process between the susceptible individuals and illicit drug users. We conduct both epidemic and endemic analysis, with a focus on the threshold dynamics characterized by the basic reproduction number. Using our model, we present illustrative numerical results with a case study in Cape Town, Gauteng, Mpumalanga and Durban communities of South Africa. In addition, the basic model is extended to incorporate time dependent intervention strategies.
Optimization for decision making linear and quadratic models
Murty, Katta G
2010-01-01
While maintaining the rigorous linear programming instruction required, Murty's new book is unique in its focus on developing modeling skills to support valid decision-making for complex real world problems, and includes solutions to brand new algorithms.
Engineering models for merging wakes in wind farm optimization applications
DEFF Research Database (Denmark)
Machefaux, Ewan; Larsen, Gunner Chr.; Murcia Leon, Juan Pablo
2015-01-01
The present paper deals with validation of 4 different engineering wake superposition approaches against detailed CFD simulations and covering different turbine interspacing, ambient turbulence intensities and mean wind speeds. The first engineering model is a simple linear superposition of wake ...
Aerial Search Optimization Model (ASOM) for UAVs in Special Operations
National Research Council Canada - National Science Library
Kress, Moshe; Royset, Johannes O
2007-01-01
.... The special operations team is equipped with short-range surveillance UAVs. We combine intelligence regarding the targets with availability and capability of UAVs in an integer linear programming model...
Independence-Based Optimization of Epistemic Model Checking
2017-02-22
imperfect information of their environment. It has been applied to settings that include diagnosis [8], and reasoning in game - like settings [15, 16, 32...represents the model as a concurrent pro- gram, in which each of the agents executes a protocol in the context of an environment. We provide a symbolic ...handled by the symbolic model checking algorithms. The paper presents experimental results that demonstrate that the condi- tional independence
Optimal covariance selection for estimation using graphical models
Vichik, Sergey; Oshman, Yaakov
2011-01-01
We consider a problem encountered when trying to estimate a Gaussian random field using a distributed estimation approach based on Gaussian graphical models. Because of constraints imposed by estimation tools used in Gaussian graphical models, the a priori covariance of the random field is constrained to embed conditional independence constraints among a significant number of variables. The problem is, then: given the (unconstrained) a priori covariance of the random field, and the conditiona...
Optimal Retail Price Model for Partial Consignment to Multiple Retailers
Po-Yu Chen
2017-01-01
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 p...
Optimization of mathematical models for soil structure interaction
International Nuclear Information System (INIS)
Vallenas, J.M.; Wong, C.K.; Wong, D.L.
1993-01-01
Accounting for soil-structure interaction in the design and analysis of major structures for DOE facilities can involve significant costs in terms of modeling and computer time. Using computer programs like SASSI for modeling major structures, especially buried structures, requires the use of models with a large number of soil-structure interaction nodes. The computer time requirements (and costs) increase as a function of the number of interaction nodes to the third power. The added computer and labor cost for data manipulation and post-processing can further increase the total cost. This paper provides a methodology to significantly reduce the number of interaction nodes. This is achieved by selectively increasing the thickness of soil layers modeled based on the need for the mathematical model to capture as input only those frequencies that can actually be transmitted by the soil media. The authors have rarely found that a model needs to capture frequencies as high as 33 Hz. Typically coarser meshes (and a lesser number of interaction nodes) are adequate
Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach
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.
Wu, Jason H.
2013-01-01
This study was designed to examine the construct of academic optimism and its relationship with collective responsibility in a sample of Taiwan elementary schools. The construct of academic optimism was tested using confirmatory factor analysis, and the whole structural model was tested with a structural equation modeling analysis. The data were…
Optimal management of ecosystem services with pollution traps : The lake model revisited
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
The analysis of optimal singular controls for SEIR model of tuberculosis
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.
International Nuclear Information System (INIS)
Smith, L.D.; Moses, S.W.
1991-01-01
Mathematical models of market share are constructed to describe the distribution of petroleum products from pipeline terminals, water terminals and refineries in the midcontinental United States. Network distribution models are developed to analyse the constraints and economics of alternative distribution systems. This paper describes how the two types of models were integrated for strategic planning in an oil pipeline company
Optimal Calculation of Residuals for ARMAX Models with Applications to Model Verification
DEFF Research Database (Denmark)
Knudsen, Torben
1997-01-01
Residual tests for sufficient model orders are based on the assumption that prediction errors are white when the model is correct. If an ARMAX system has zeros in the MA part which are close to the unit circle, then the standard predictor can have large transients. Even when the correct model...
Heat transfer optimization of SCO2 porous flow based on Brinkman model
Directory of Open Access Journals (Sweden)
Lin David T.W.
2016-01-01
Full Text Available The purpose of this study is to obtain the optimal operating condition in order to find the maximum supercritical CO2 heat extraction in the enhanced geothermal system (EGS. In this study, the heat transfer model conjugated with the Brinkman model is used to evaluate the thermal behavior in the reservoir of the EGS. This numerical model is validated by experiment. Optimization is processed based on the Nelder-Mead approach. The optimal operating conditions are proposed with different pressure, porosity. This study will build the optimal platform of heat source of geothermal power plant.
An optimal control model for load shifting - With application in the energy management of a colliery
International Nuclear Information System (INIS)
Middelberg, Arno; Zhang Jiangfeng; Xia Xiaohua
2009-01-01
This paper presents an optimal control model for the load shifting problem in energy management and its application in a South African colliery. It is illustrated in the colliery scenario that how the optimal control model can be applied to optimize load shifting and improve energy efficiency through the control of conveyor belts. The time-of-use electricity tariff is used as an input to the objective function in order to obtain a solution that minimizes electricity costs and thus maximizes load shifting. The case study yields promising results that show the potential of applying this optimal control model to other industrial Demand Side Management initiatives
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
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...
Energy-saving management modelling and optimization for lead-acid battery formation process
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.
DEFF Research Database (Denmark)
Kristensen, Anders Ringgaard; Søllested, Thomas Algot
2004-01-01
improvements. The biological model of the replacement model is described in a previous paper and in this paper the optimization model is described. The model is developed as a prototype for use under practical conditions. The application of the model is demonstrated using data from two commercial Danish sow......Recent methodological improvements in replacement models comprising multi-level hierarchical Markov processes and Bayesian updating have hardly been implemented in any replacement model and the aim of this study is to present a sow replacement model that really uses these methodological...... herds. It is concluded that the Bayesian updating technique and the hierarchical structure decrease the size of the state space dramatically. Since parameter estimates vary considerably among herds it is concluded that decision support concerning sow replacement only makes sense with parameters...
A novel medical information management and decision model for uncertain demand optimization.
Bi, Ya
2015-01-01
Accurately planning the procurement volume is an effective measure for controlling the medicine inventory cost. Due to uncertain demand it is difficult to make accurate decision on procurement volume. As to the biomedicine sensitive to time and season demand, the uncertain demand fitted by the fuzzy mathematics method is obviously better than general random distribution functions. To establish a novel medical information management and decision model for uncertain demand optimization. A novel optimal management and decision model under uncertain demand has been presented based on fuzzy mathematics and a new comprehensive improved particle swarm algorithm. The optimal management and decision model can effectively reduce the medicine inventory cost. The proposed improved particle swarm optimization is a simple and effective algorithm to improve the Fuzzy interference and hence effectively reduce the calculation complexity of the optimal management and decision model. Therefore the new model can be used for accurate decision on procurement volume under uncertain demand.
Review of the Space Mapping Approach to Engineering Optimization and Modeling
DEFF Research Database (Denmark)
Bakr, M. H.; Bandler, J. W.; Madsen, Kaj
2000-01-01
We review the Space Mapping (SM) concept and its applications in engineering optimization and modeling. The aim of SM is to avoid computationally expensive calculations encountered in simulating an engineering system. The existence of less accurate but fast physically-based models is exploited. SM...... drives the optimization iterates of the time-intensive model using the fast model. Several algorithms have been developed for SM optimization, including the original SM algorithm, Aggressive Space Mapping (ASM), Trust Region Aggressive Space Mapping (TRASM) and Hybrid Aggressive Space Mapping (HASM...
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.
Directory of Open Access Journals (Sweden)
Anna Michalak
2014-01-01
Full Text Available In this paper we examine the concept of Pareto optimality in a simplified Gale economic model without assuming continuity of the utility functions. We apply some existing results on higher-order optimality conditions to get necessary and sufficient conditions for a locally Pareto optimal allocation.
Process Cost Modeling for Multi-Disciplinary Design Optimization
Bao, Han P.; Freeman, William (Technical Monitor)
2002-01-01
For early design concepts, the conventional approach to cost is normally some kind of parametric weight-based cost model. There is now ample evidence that this approach can be misleading and inaccurate. By the nature of its development, a parametric cost model requires historical data and is valid only if the new design is analogous to those for which the model was derived. Advanced aerospace vehicles have no historical production data and are nowhere near the vehicles of the past. Using an existing weight-based cost model would only lead to errors and distortions of the true production cost. This report outlines the development of a process-based cost model in which the physical elements of the vehicle are costed according to a first-order dynamics model. This theoretical cost model, first advocated by early work at MIT, has been expanded to cover the basic structures of an advanced aerospace vehicle. Elemental costs based on the geometry of the design can be summed up to provide an overall estimation of the total production cost for a design configuration. This capability to directly link any design configuration to realistic cost estimation is a key requirement for high payoff MDO problems. Another important consideration in this report is the handling of part or product complexity. Here the concept of cost modulus is introduced to take into account variability due to different materials, sizes, shapes, precision of fabrication, and equipment requirements. The most important implication of the development of the proposed process-based cost model is that different design configurations can now be quickly related to their cost estimates in a seamless calculation process easily implemented on any spreadsheet tool. In successive sections, the report addresses the issues of cost modeling as follows. First, an introduction is presented to provide the background for the research work. Next, a quick review of cost estimation techniques is made with the intention to
Modelling and multi objective optimization of laser peening process using Taguchi utility concept
Ranjith Kumar, G.; Rajyalakshmi, G.
2017-11-01
Laser peening is considered as one of the innovative surface treatment technique. This work focuses on determining the optimal peening parameters for finding optimal responses like residual stresses and deformation. The modelling was done using ANSYS and values are optimised using Taguchi Utility concept for simultaneous optimization of responses. Three parameters viz. overlap; Pulse duration and Pulse density are considered as process parameters for modelling and optimization. Through Multi objective optimization, it is showing that Overlap is showing maximum influence on Stress and deformation followed by Power density and pulse duration.
Spatially balanced topological interaction grants optimal cohesion in flocking models.
Camperi, Marcelo; Cavagna, Andrea; Giardina, Irene; Parisi, Giorgio; Silvestri, Edmondo
2012-12-06
Models of self-propelled particles (SPPs) are an indispensable tool to investigate collective animal behaviour. Originally, SPP models were proposed with metric interactions, where each individual coordinates with neighbours within a fixed metric radius. However, recent experiments on bird flocks indicate that interactions are topological: each individual interacts with a fixed number of neighbours, irrespective of their distance. It has been argued that topological interactions are more robust than metric ones against external perturbations, a significant evolutionary advantage for systems under constant predatory pressure. Here, we test this hypothesis by comparing the stability of metric versus topological SPP models in three dimensions. We show that topological models are more stable than metric ones. We also show that a significantly better stability is achieved when neighbours are selected according to a spatially balanced topological rule, namely when interacting neighbours are evenly distributed in angle around the focal individual. Finally, we find that the minimal number of interacting neighbours needed to achieve fully stable cohesion in a spatially balanced model is compatible with the value observed in field experiments on starling flocks.
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
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.
Reproducing Phenomenology of Peroxidation Kinetics via Model Optimization
Ruslanov, Anatole D.; Bashylau, Anton V.
2010-06-01
We studied mathematical modeling of lipid peroxidation using a biochemical model system of iron (II)-ascorbate-dependent lipid peroxidation of rat hepatocyte mitochondrial fractions. We found that antioxidants extracted from plants demonstrate a high intensity of peroxidation inhibition. We simplified the system of differential equations that describes the kinetics of the mathematical model to a first order equation, which can be solved analytically. Moreover, we endeavor to algorithmically and heuristically recreate the processes and construct an environment that closely resembles the corresponding natural system. Our results demonstrate that it is possible to theoretically predict both the kinetics of oxidation and the intensity of inhibition without resorting to analytical and biochemical research, which is important for cost-effective discovery and development of medical agents with antioxidant action from the medicinal plants.
Optimization of Multipurpose Reservoir Systems Using Power Market Models
DEFF Research Database (Denmark)
Pereira-Cardenal, S. J.; Mo, B.; Riegels, N.
2015-01-01
and vice versa. To improve the representation of hydropower benefits in hydroeconomic models, an application of stochastic dynamic programming, known as the water value method, was used to maximize irrigation benefits while minimizing the costs of power generation within a power market. The method yields......Hydroeconomic models have been used to determine policies for efficient allocation of scarce water resources. Hydropower benefits are typically represented through exogenous electricity prices, but these do not consider the effect that the power market can have on the hydropower release policy...... realistic and sensitive to hydrological variability. Internally calculated hydropower prices provided better results than exogenous hydropower prices and can therefore improve the representation of hydropower benefits in hydroeconomic models. (C) 2014 American Society of Civil Engineers....
Optimal parameters for the FFA-Beddoes dynamic stall model
Energy Technology Data Exchange (ETDEWEB)
Bjoerck, A.; Mert, M. [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H.A. [Risoe National Lab., Roskilde (Denmark)
1999-03-01
Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)
Optimizing romanian maritime coastline using mathematical model Litpack
Anton, I. A.; Panaitescu, M.; Panaitescu, F. V.
2017-08-01
There are many methods and tools to study shoreline change in coastal engineering. LITPACK is a numerical model included in MIKE software developed by DHI (Danish Hydraulic Institute). With this matehematical model we can simulate coastline evolution and profile along beach. Research and methodology: the paper contents location of the study area, the current status of Midia-Mangalia shoreline, protection objectives, the changes of shoreline after having protected constructions. In this paper are presented numerical and graphycal results obtained with this model for studying the romanian maritime coastline in area MIDIA-MANGALIA: non-cohesive sediment transport, long-shore current and littoral drift, coastline evolution, crossshore profile evolution, the development of the coastline position in time.
Optimization Model for Capacity Management and Bed Scheduling for Hospital
Sitepu, Suryati; Mawengkang, Herman; Husein, Ismail
2018-01-01
Hospital is a very important institution to provide health care for people. It is not surprising that nowadays the people’s demands for hospital is increasing.. However, due to the rising cost of healthcare services, hospitals need to consider efficiencies in order to overcome these two problems. This paper deals with an integrated strategy of staff capacity management and bed allocation planning to tackle these problems. Mathematically, the strategy can be modeled as an integer linear programming problem. We solve the model using a direct neighborhood search approach, based on the notion of superbasic variables.
Medium Term Hydroelectric Production Planning - A Multistage Stochastic Optimization Model
Directory of Open Access Journals (Sweden)
BITA ANALUI
2014-06-01
Full Text Available Multistage stochastic programming is a key technology for making decisions over time in an uncertain environment. One of the promising areas in which this technology is implementable, is medium term planning of electricity production and trading where decision makers are typically faced with uncertain parameters (such as future demands and market prices that can be described by stochastic processes in discrete time. We apply this methodology to hydrosystem operation assuming random electricity prices and random inflows to the reservoir system. After describing the multistage stochastic model a simple case study is presented. In particular we use the model for pricing an electricity delivery contract in the framework of indifference pricing.
Optimal model of radiocarbon residence time in exchange reservoir
International Nuclear Information System (INIS)
Dergachev, V.A.
1977-01-01
Radiocarbon content variations in the earth atmosphere were studied using a mathematical model. The so-called exchange reservoir was considered consisting of layers, and the radiocarbon exchange rate at the interfaces between these layers was supposed to be constant. The process of 14 C mixing and exchange in a dynamic system is described by a system of nonhomogeneous 1st order differential equations. The model also accounts for the change in rate of radiocarbon formation in the earth atmosphere due to cosmic and geophysical effects (solar activity, solar cycle, etc.). (J.P.)
Optimal Operational Monetary Policy Rules in an Endogenous Growth Model: a calibrated analysis
Arato, Hiroki
2009-01-01
This paper constructs an endogenous growth New Keynesian model and considers growth and welfare effect of Taylor-type (operational) monetary policy rules. The Ramsey equilibrium and optimal operational monetary policy rule is also computed. In the calibrated model, the Ramseyoptimal volatility of inflation rate is smaller than that in standard exogenous growth New Keynesian model with physical capital accumulation. Optimal operational monetary policy rule makes nominal interest rate respond s...
Optimal Control of a Delay-Varying Computer Virus Propagation Model
Ren, Jianguo; Xu, Yonghong; Zhang, Chunming
2013-01-01
By incorporating the objective of keeping a low number of infected nodes and a high number of recovered nodes at a lower cost into a known computer virus model (the delay-varying SIRC model) extended by introducing quarantine, a novel model is described by means of the optimal control strategy and theoretically analyzed. Through the comparison of simulation results, it is shown that the propagation of computer virus with varying latency period can be suppressed effectively by the optimal cont...
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
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.
DEFF Research Database (Denmark)
Creixell Mediante, Ester; Jensen, Jakob Søndergaard; Naets, Frank
2018-01-01
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......-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....
Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.
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.
Modeling of Optimal Power Generation using Multiple Kites
Williams, P.; Lansdorp, B.; Ockels, W.J.
2008-01-01
Kite systems have the potential to revolutionize energy generation. Large scale systems are envisioned that can fly autonomously in “power generation” cycles which drive a ground-based generator. In order for such systems to produce power efficiently, good models of the system are required. This
Modeling, simulation and parametric optimization of wire EDM ...
African Journals Online (AJOL)
In the present work, quadratic mathematical models have been derived to represent the process behavior of wire electrical discharge machining (WEDM) operation. Experiments have been conducted with six process parameters: discharge current, pulse duration, pulse frequency, wire speed, wire tension and dielectric flow ...
Mathematical programming model for the optimization of nutritional ...
African Journals Online (AJOL)
In addition, a possible range of milk production between 10and 45 I has been allowed for. In this study the 'Cedara' model (Jones & Stewart, 1987) was used to determine the nutrient requirements and dry matter in- take of any cow under consideration at the various levels of milk production, although any preferred source of.
mathematical model for the optimization of compressive strength of ...
African Journals Online (AJOL)
ES Obe
contaminated sandcrete block. This response function serves as a model whose predictions can then be tested for adequacy against con- trol results obtained in the laboratory. These now form the basis of a new method of sandcrete block components mix. It will do match this against the primordial arbitrari- ness (they call it ...
Particle swarm optimization of a neural network model in a ...
Indian Academy of Sciences (India)
... trained ANN not only gives better prediction results and reduced computational times compared to the BPNN, it is also a more robust model, being free of getting trapped in local optimum solutions unlike the latter. Besides, it offers the advantages of a straight-forward logic, simple realization and underlying intelligence.
A model for the optimal risk management of (farm) firms
DEFF Research Database (Denmark)
Rasmussen, Svend
Current methods of risk management focus on efficiency and do not provide operational answers to the basic question of how to optimise and balance the two objectives, maximisation of expected income and minimisation of risk. This paper uses the Capital Asset Pricing Model (CAPM) to derive...
Optimizing technology investments: a broad mission model approach
Shishko, R.
2003-01-01
A long-standing problem in NASA is how to allocate scarce technology development resources across advanced technologies in order to best support a large set of future potential missions. Within NASA, two orthogonal paradigms have received attention in recent years: the real-options approach and the broad mission model approach. This paper focuses on the latter.
Modeling marine surface microplastic transport to assess optimal removal locations
Sherman, Peter; Van Sebille, Erik
2016-01-01
Marine plastic pollution is an ever-increasing problem that demands immediate mitigation and reduction plans. Here, a model based on satellite-tracked buoy observations and scaled to a large data set of observations on microplastic from surface trawls was used to simulate the transport of plastics
Optimal item discrimination and maximum information for logistic IRT models
Veerkamp, W.J.J.; Veerkamp, Wim J.J.; Berger, Martijn P.F.; Berger, Martijn
1999-01-01
Items with the highest discrimination parameter values in a logistic item response theory model do not necessarily give maximum information. This paper derives discrimination parameter values, as functions of the guessing parameter and distances between person parameters and item difficulty, that
Constrained Optimization Approaches to Estimation of Structural Models
DEFF Research Database (Denmark)
Iskhakov, Fedor; Rust, John; Schjerning, Bertel
2015-01-01
We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (SJ, 2012). They used an inefficient version of the nested fixed point algorithm that relies on successive app...
Constrained Optimization Approaches to Estimation of Structural Models
DEFF Research Database (Denmark)
Iskhakov, Fedor; Jinhyuk, Lee; Rust, John
2016-01-01
We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (SJ, 2012). Their implementation of the nested fixed point algorithm used successive approximations to solve t...
Optimization Modelling for Multi-Objective Supply Chains, A Case ...
African Journals Online (AJOL)
In this study a mathematical model was developed for minimizing the distribution cost in a multi-product supply chain system. The oil and gas sector was studied to understand the underlying supply chain system. Attempt was made to identify system parameters, variables, limitations, criteria so as to be able to define the ...
Optimization of Weibull deteriorating items inventory model under ...
Indian Academy of Sciences (India)
In this study, we have discussed the development of an inventory model when the deterioration rate of the item follows Weibull two parameter distributions under the effect of selling price and time dependent demand, since, not only the selling price, but also the time is a crucial factor to enhance the demand in the market as ...
Thermodynamic modelling and optimization of a dual pressure ...
Indian Academy of Sciences (India)
The exergetic losses in CC system are compared with each other. The present DPRH HRSG model has been compared and validated with the plant and published data. Keywords. Combined cycle; dual pressure; deaerator; exergy analysis; heat recovery steam generator. 1. Introduction. Combined cycle (CC) power plants ...
Directory of Open Access Journals (Sweden)
RISKA YUNITA
2015-06-01
Full Text Available Model of stock price movements that follow stochastic process can be formulated in Stochastic Diferential Equation (SDE. The exact solution of SDE model is called Geometric Brownian Motion (GBM model. Determination the optimal portfolio of three asset that follows Multidimensional GBM model is to be carried out in this research.Multidimensional GBM model represents stock price in the future is affected by three parameter, there are expectation of stock return, risk stock, and correlation between stock return. Therefore, theory of portfolio Markowitz is used on formation of optimal portfolio. Portfolio Markowitz formulates three of same parameter that is calculated on Multidimensional GBM model. The result of this research are optimal portfolio reaches with the proportion of fund are 39,38% for stock BBCA, 59,82% for stock ICBP, and 0,80% for stock INTP. This proportion of fund represents value of parameters that is calculated on modelling stock price.
Electrical Parasitics and Thermal Modeling for Optimized Layout Design of High Power SiC Modules
DEFF Research Database (Denmark)
Bahman, Amir Sajjad; Blaabjerg, Frede; Dutta, Atanu
2016-01-01
and parasitic inductance models of SiC power modules. These models can replace the models by Finite Element Methods (FEM) to predict temperatures and electrical parasitics of power modules with much faster speed and acceptable errors and will be used for study of real operation of power modules. As a case study......, the presented models are verified by a conventional and an optimized power module layout. The optimized layout is designed based on the reduction of stray inductance and temperature in a P-cell and N-cell half-bridge module. The presented models are verified by FEM simulations and also experiment....
DEFF Research Database (Denmark)
Mørkholt, Jakob
1997-01-01
Optimal feedback control of broadband sound radiation from a rectangular baffled panel has been investigated through computer simulations. Special emphasis has been put on the sensitivity of the optimal feedback control to uncertainties in the modelling of the system under control.A model...... in terms of a set of radiation filters modelling the radiation dynamics.Linear quadratic feedback control applied to the panel in order to minimise the radiated sound power has then been simulated. The sensitivity of the model based controller to modelling uncertainties when using feedback from actual...
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.
3D Model Optimization of Four-Facet Drill for 3D Drilling Simulation
Buranský, Ivan; Necpal, Martin; Bračík, Matej
2016-09-01
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.
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 optimal promotion cost control model for a Markovian manpower ...
African Journals Online (AJOL)
A theory concerning the existence of an optimal promotion control strategy for controlling a Markovian manpower system is developed. Control interventions are made at controller's specific time point with cost as an optimality performance criterion. A dynamic programming technique is adopted for the optimal solution and ...
An optimal promotion cost control model for a markovian manpower ...
African Journals Online (AJOL)
A theory concerning the existence of an optimal promotion control strategy for controlling a Markovian manpower system is developed. Control interventions are made at controller's specific time point with cost as an optimality performance criterion. A dynamic programming technique is adopted for the optimal solution and ...
Nonlinear programming models to optimize uneven-aged loblolly pine management
Benedict J. Schulte; Joseph. Buongiorno; Kenneth Skog
1999-01-01
Nonlinear programming models of uneven-aged loblolly pine (Pinus taeda L.) management were developed to identify sustainable management regimes which optimize: 1) soil expectation value (SEV), 2) tree diversity, or 3) annual sawtimber yields. The models use the equations of SouthPro, a site- and density-dependent, multi-species matrix growth and yield model that...
Do different methods of modeling statin treatment effectiveness influence the optimal decision?
B.J.H. van Kempen (Bob); B.S. Ferket (Bart); A. Hofman (Albert); S. Spronk (Sandra); E.W. Steyerberg (Ewout); M.G.M. Hunink (Myriam)
2012-01-01
textabstractPurpose. Modeling studies that evaluate statin treatment for the prevention of cardiovascular disease (CVD) use different methods to model the effect of statins. The aim of this study was to evaluate the impact of using different modeling methods on the optimal decision found in such
Optimization of maintenance for power system equipment using a predictive health model
Bajracharya, G.; Koltunowicz, T.; Negenborn, R.R.; Papp, Z.; Djairam, D.; Schutter, B.D. de; Smit, J.J.
2009-01-01
In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based
Retout, Sylvie; Comets, Emmanuelle; Bazzoli, Caroline; Mentré, France
2009-01-01
International audience; We address the problem of design optimization using cost functions in nonlinear mixed effects models with multiple responses. We focus on the relative feasibility of the optimized designs, in term of sampling times and of number of subjects. To do that, we extend the Fedorov–Wynn algorithm—a dedicated design optimization algorithm—to include a cost function that penalizes less feasible designs as well as to take into account multiple responses. We apply this extension ...
Analyses of Methods and Algorithms for Modelling and Optimization of Biotechnological Processes
Directory of Open Access Journals (Sweden)
Stoyan Stoyanov
2009-08-01
Full Text Available A review of the problems in modeling, optimization and control of biotechnological processes and systems is given in this paper. An analysis of existing and some new practical optimization methods for searching global optimum based on various advanced strategies - heuristic, stochastic, genetic and combined are presented in the paper. Methods based on the sensitivity theory, stochastic and mix strategies for optimization with partial knowledge about kinetic, technical and economic parameters in optimization problems are discussed. Several approaches for the multi-criteria optimization tasks are analyzed. The problems concerning optimal controls of biotechnological systems are also discussed.
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...
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.
Wang, Zhen-yu; Yu, Jian-cheng; Zhang, Ai-qun; Wang, Ya-xing; Zhao, Wen-tao
2017-12-01
Combining high precision numerical analysis methods with optimization algorithms to make a systematic exploration of a design space has become an important topic in the modern design methods. During the design process of an underwater glider's flying-wing structure, a surrogate model is introduced to decrease the computation time for a high precision analysis. By these means, the contradiction between precision and efficiency is solved effectively. Based on the parametric geometry modeling, mesh generation and computational fluid dynamics analysis, a surrogate model is constructed by adopting the design of experiment (DOE) theory to solve the multi-objects design optimization problem of the underwater glider. The procedure of a surrogate model construction is presented, and the Gaussian kernel function is specifically discussed. The Particle Swarm Optimization (PSO) algorithm is applied to hydrodynamic design optimization. The hydrodynamic performance of the optimized flying-wing structure underwater glider increases by 9.1%.
Applying Mathematical Optimization Methods to an ACT-R Instance-Based Learning Model.
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.
Using models for the optimization of hydrologic monitoring
Fienen, Michael N.; Hunt, Randall J.; Doherty, John E.; Reeves, Howard W.
2011-01-01
Hydrologists are often asked what kind of monitoring network can most effectively support science-based water-resources management decisions. Currently (2011), hydrologic monitoring locations often are selected by addressing observation gaps in the existing network or non-science issues such as site access. A model might then be calibrated to available data and applied to a prediction of interest (regardless of how well-suited that model is for the prediction). However, modeling tools are available that can inform which locations and types of data provide the most 'bang for the buck' for a specified prediction. Put another way, the hydrologist can determine which observation data most reduce the model uncertainty around a specified prediction. An advantage of such an approach is the maximization of limited monitoring resources because it focuses on the difference in prediction uncertainty with or without additional collection of field data. Data worth can be calculated either through the addition of new data or subtraction of existing information by reducing monitoring efforts (Beven, 1993). The latter generally is not widely requested as there is explicit recognition that the worth calculated is fundamentally dependent on the prediction specified. If a water manager needs a new prediction, the benefits of reducing the scope of a monitoring effort, based on an old prediction, may be erased by the loss of information important for the new prediction. This fact sheet focuses on the worth or value of new data collection by quantifying the reduction in prediction uncertainty achieved be adding a monitoring observation. This calculation of worth can be performed for multiple potential locations (and types) of observations, which then can be ranked for their effectiveness for reducing uncertainty around the specified prediction. This is implemented using a Bayesian approach with the PREDUNC utility in the parameter estimation software suite PEST (Doherty, 2010). The
Optimization of cell seeding in a 2D bio-scaffold system using computational models.
Ho, Nicholas; Chua, Matthew; Chui, Chee-Kong
2017-05-01
The cell expansion process is a crucial part of generating cells on a large-scale level in a bioreactor system. Hence, it is important to set operating conditions (e.g. initial cell seeding distribution, culture medium flow rate) to an optimal level. Often, the initial cell seeding distribution factor is neglected and/or overlooked in the design of a bioreactor using conventional seeding distribution methods. This paper proposes a novel seeding distribution method that aims to maximize cell growth and minimize production time/cost. The proposed method utilizes two computational models; the first model represents cell growth patterns whereas the second model determines optimal initial cell seeding positions for adherent cell expansions. Cell growth simulation from the first model demonstrates that the model can be a representation of various cell types with known probabilities. The second model involves a combination of combinatorial optimization, Monte Carlo and concepts of the first model, and is used to design a multi-layer 2D bio-scaffold system that increases cell production efficiency in bioreactor applications. Simulation results have shown that the recommended input configurations obtained from the proposed optimization method are the most optimal configurations. The results have also illustrated the effectiveness of the proposed optimization method. The potential of the proposed seeding distribution method as a useful tool to optimize the cell expansion process in modern bioreactor system applications is highlighted. Copyright © 2017 Elsevier Ltd. All rights reserved.
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
Portfolio optimization by using linear programing models based on genetic algorithm
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.
An extended continuum model considering optimal velocity change with memory and numerical tests
Qingtao, Zhai; Hongxia, Ge; Rongjun, Cheng
2018-01-01
In this paper, an extended continuum model of traffic flow is proposed with the consideration of optimal velocity changes with memory. The new model's stability condition and KdV-Burgers equation considering the optimal velocities change with memory are deduced through linear stability theory and nonlinear analysis, respectively. Numerical simulation is carried out to study the extended continuum model, which explores how optimal velocity changes with memory affected velocity, density and energy consumption. Numerical results show that when considering the effects of optimal velocity changes with memory, the traffic jams can be suppressed efficiently. Both the memory step and sensitivity parameters of optimal velocity changes with memory will enhance the stability of traffic flow efficiently. Furthermore, numerical results demonstrates that the effect of optimal velocity changes with memory can avoid the disadvantage of historical information, which increases the stability of traffic flow on road, and so it improve the traffic flow stability and minimize cars' energy consumptions.
Developments in model-based optimization and control distributed control and industrial applications
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...
Prediction Model of Battery State of Charge and Control Parameter Optimization for Electric Vehicle
Directory of Open Access Journals (Sweden)
Bambang Wahono
2015-07-01
Full Text Available This paper presents the construction of a battery state of charge (SOC prediction model and the optimization method of the said model to appropriately control the number of parameters in compliance with the SOC as the battery output objectives. Research Centre for Electrical Power and Mechatronics, Indonesian Institute of Sciences has tested its electric vehicle research prototype on the road, monitoring its voltage, current, temperature, time, vehicle velocity, motor speed, and SOC during the operation. Using this experimental data, the prediction model of battery SOC was built. Stepwise method considering multicollinearity was able to efficiently develops the battery prediction model that describes the multiple control parameters in relation to the characteristic values such as SOC. It was demonstrated that particle swarm optimization (PSO succesfully and efficiently calculated optimal control parameters to optimize evaluation item such as SOC based on the model.
Power Grid Construction Project Portfolio Optimization Based on Bi-level programming model
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.