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Saturday, November 21, 2020 | History

1 edition of Stochastic simulation optimization found in the catalog.

Stochastic simulation optimization

Chun-hung Chen

Stochastic simulation optimization

an optimal computing budget allocation

by Chun-hung Chen

  • 173 Want to read
  • 38 Currently reading

Published by World Scientific in New Jersey .
Written in English

    Subjects:
  • Systems engineering,
  • Simulation methods,
  • Mathematical optimization,
  • Stochastic processes

  • Edition Notes

    Includes bibliographical references and index.

    StatementChun-Hung Chen, Loo Hay Lee
    SeriesSystem engineering and operations research -- v. 1
    ContributionsLee, Loo Hay
    Classifications
    LC ClassificationsTA168 .C473 2011
    The Physical Object
    Paginationxviii, 227 p. :
    Number of Pages227
    ID Numbers
    Open LibraryOL25555456M
    ISBN 109814282642
    ISBN 109789814282642
    OCLC/WorldCa456170891


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Stochastic simulation optimization by Chun-hung Chen Download PDF EPUB FB2

This book addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners a comprehensive coverage of OCBA approach for stochastic simulation by:   This book will introduce two important techniques initially proposed and developed by Professor Y C Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art.

Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization.

Simulation is the only practicable way of getting insight into such models. Thus, the problem of optimal decisions can be seen as getting simulation and optimization effectively combined. The field is quite new and yet the number of publications is enormous.

This book. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control James C. Spall John Wiley & Sons, - Mathematics - pagesReviews: 1. • The book links simulation and optimization through numerical analyses and stochastic optimization techniques • Includes use of examples to illustrate the application of the concepts and specific guidance on the use of software (Aspen ® Plus, Excel, MATLB) to set up and solve models representing complex problems.

For researchers, this book offers a series of promising approaches for efficiency enhancement in computer simulation, stochastic optimization, statistical sampling, and ranking and selection. The generalized framework may lead to numerous new lines of researches.

For courses, this book could serve as a textbook for advanced. The book includes over examples, Web links to software and data sets, more than exercises for the reader, and an extensive list of references. These features help make the text an invaluable resource for those interested in the theory or practice of stochastic search and optimization.

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Figure 1 was produced using a modular stochastic LLG simulation element with the input current swept from −2 μA to 2 μA in increments of nA. The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey Stochastic simulation optimization book the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology.

Leading contributors cover such topics as discrete optimization via simulation, ranking and selection. Download Citation | Introduction to Stochastic Search and Optimization. Estimation, Simulation, and Control Stochastic simulation optimization book This comprehensive book offers main pages divided into 17 chapters.

In addition. Stochastic optimization seems to be a "dark corner" for the fields of optimization and of Monte Carlo methods. Spall brings a quantitative engineering perspective to the problems, yet gives theory its proper dues. Moreover, he weaves a consistent interpretation among these algorithms which deserve greater attention and by: Abhijit Gosavi is a leading international authority on reinforcement learning, stochastic dynamic programming and simulation-based first edition of his Springer book “Simulation-Based Optimization” that appeared in was the first text to have appeared on that topic.

Consider a stochastic system of such complexity that its performance can only be evaluated by using simulation or direct experimentation. To optimize the Stochastic simulation optimization book performance of such systems as a function of several continuous input parameters (decision variables), we present a “scaled” stochastic approximation algorithm for finding the zero (root) of the gradient of the response function.

Simulation-Based Optimization II: Stochastic Gradient and Sample Path Methods. Markov Chain Monte Carlo. collection of topics ” (Short Book Reviews, August ) "Rather than simply present various stochastic search and optimization algorithms as a collection of distinct techniques, the book compares and contrasts the algorithms within.

A stochastic simulation is a simulation of a system that has variables that can change stochastically (randomly) with individual probabilities. Realizations of these random variables are generated and inserted into a model of the system. Outputs of the model are recorded, and then the process is repeated with a new set of random values.

These steps are repeated until a sufficient amount of. Intended as a reference for researchers and a textbook for students, this book discusses a broad range of methods in stochastic search and optimization.

Methods covered include random search, recursive least squares, stochastic approximation, simulated annealing, evolutionary computation, and reinforcement learning. His research focuses on the simulation-based optimization, maritime logistics which includes port operations and the modeling and analysis for the logistics and supply chain system.

He has co-authored a book: Stochastic Simulation Optimization — An. Stochastic Modeling & Simulation. The Ohio State University hosts an exciting research program on stochastic modeling, stochastic optimization, and simulation. Much of the research is on modeling, analysis, and optimization of real-world systems involving uncertainty.

Optimization of Stochastic Models: The Interface Between Simulation and Optimization is suitable as a text for a graduate level course on Stochastic Models or as a secondary text for a graduate level course in Operations Research.

Books with Buzz Discover the latest buzz-worthy books, from mysteries and romance to humor and nonfiction. 5/5(1). This book contains actual techniques in use for water resources planning and management, incorporating randomness into the decision making process.

Optimization and simulation, the classical systems-analysis technologies, are revisited under up-to-date statistical hydrology findings backed by real world applications. This book is the first of its kind, presenting state-of-the-art stochastic simulation and optimization techniques and step-by-step case studies.

Quantification of geological uncertainty through new efficient conditional simulation techniques for large deposits, integration of uncertainty to stochastic optimization formulations for design and.

the sciences. The book of Shapiro et al. [54] provides a more comprehensive picture of stochastic modeling problems and optimization algorithms than we have been able to in our lectures, as stochastic optimization is by itself a major field.

Several recent surveys on online learning and online convex optimization. specifying—via a SQL extension—and processing stochastic package queries (SPQs), in order to solve optimization prob-lems over uncertain data, right where the data resides.

Prior work in stochastic programming uses Monte Carlo meth-ods where the original stochastic optimization problem is approximated by a large deterministic optimization. The paper presents a simplified simulation model of the operation of a taxi system. The model retains the main features of a real taxi transportation system and despite its simplicity examines the system behavior in different conditions.

It was shown that for every request generation rate a critical number of taxis in disposal could be determined. If the real number of taxis is lower than the. Discrete simulation-based optimization methods for industrial engineering problems: A systematic literature review Wilson Trigueiro de Sousa Junior, José Arnaldo Barra Montevechi, Rafael de Carvalho Miranda and Afonso Teberga Campos.

* Simulation-based optimization * Markov chain Monte Carlo * Optimal experimental design The book includes over examples, Web links to software and data sets, more than exercises for the reader, and an extensive list of references.

These features help make the text an invaluable resource for those interested in the theory or practice of Price: $ Introduction to Stochastic Search and Optimization book. Read reviews from world’s largest community for readers. * Unique in its survey of the range of 4/5(5). While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds.

Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, Second Edition. Stochastic optimization (SO) methods are optimization methods that generate and use random stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints.

Stochastic optimization methods also include methods with random iterates. Simulation optimization (SO) refers to the optimization of the performance of a system, of which the objective and constraints can only be estimated by stochastic simulations.

Unlike algebraic model-based mathematical programming, SO usually involves black-box simulations; that is, closed-form simulation expressions are not available to. Research and Teaching Interests. computer simulation of stochastic systems stochastic processes statistical learning Nelson studies the design and analysis of computer simulation experiments, particularly issues of statistical efficiency (such as variance-reduction techniques), multivariate output analysis (such as multiple-comparison procedures and simulation optimization).

In this article, we test the performance of a multiobjective simulation optimization algorithm that contains two crucial elements: the search phase implements stochastic kriging to account for the inherent noise in the outputs when constructing the metamodel, and the accuracy phase uses a well-known multiobjective ranking and selection.

What this Book is about --Part A: General Methods and Algorithms --Generating Random Objects --Output Analysis --Steady-State Simulation --Variance Reduction Methods --Rare Event Simulation --Gradient Estimation --Stochastic Optimization --Part B: Algorithms for Special Models --Numerical Integration --Stochastic Differential Equations.

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The proposed SMA has several new features with a unique. A coherent introduction to the techniques for modeling dynamic stochastic systems, this volume also offers a guide to the mathematical, numerical, and simulation tools of systems analysis.

Each chapter opens with an illustrative case study, and comprehensive presentations include formulation of models, determination of parameters, analysis, and interpretation of results. edition. The main topic of this book is optimization problems involving uncertain parameters, for which stochastic models are available.

Although many ways have been proposed to model uncertain quantities, stochastic models have proved their flexibility and usefulness in diverse areas of science.

This is mainly due to solid mathematical foundations and. Carl Sandrock (April 1st ). Identification and Generation of Realistic Input Sequences for Stochastic Simulation with Markov Processes, Modeling Simulation and Optimization - Tolerance and Optimal Control, Shkelzen Cakaj, IntechOpen, DOI: / Available from.

Get this from a library! Stochastic simulation optimization: an optimal computing budget allocation. [Chun-hung Chen; Loo Hay Lee] -- With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do.7 Sensitivity Analysis and Monte Carlo Optimization Introduction Score Function Method for Sensitivity Analysis of DESS Simulation-Based Optimization of DESS Stochastic Approximation Stochastic Counterpart Method Sensitivity Analysis of DEDS Problems References 8 Cross.OCBA Books.

1. A new book about OCBA has been published in The name of the book is "Stochastic Simulation Optimization: An Optimal Computing Budget Allocation". This book gives a comprehensive and extensive coverage on this efficient simulation optimization methodology, from basic idea, formal development, to the state-of-art.