Stochastic Simulation Optimization

Stochastic Simulation Optimization Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Stochastic Simulation Optimization book. This book definitely worth reading, it is an incredibly well-written.

Stochastic Simulation Optimization

Chun-hung Chen,Loo Hay Lee
Stochastic Simulation Optimization Book PDF

Author : Chun-hung Chen,Loo Hay Lee
Publisher : World Scientific
Page : 246 pages
File Size : 54,6 Mb
Release : 2011
Category : Computers
ISBN : 9789814282642

Get Book

Stochastic Simulation Optimization by Chun-hung Chen,Loo Hay Lee Pdf

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 not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive. 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. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation.

Stochastic Simulation Optimization for Discrete Event Systems

Chun-Hung Chen,Qing-Shan Jia,Loo Hay Lee
Stochastic Simulation Optimization for Discrete Event Systems Book PDF

Author : Chun-Hung Chen,Qing-Shan Jia,Loo Hay Lee
Publisher : World Scientific
Page : 274 pages
File Size : 52,6 Mb
Release : 2013-07-03
Category : Technology & Engineering
ISBN : 9789814513029

Get Book

Stochastic Simulation Optimization for Discrete Event Systems by Chun-Hung Chen,Qing-Shan Jia,Loo Hay Lee Pdf

Discrete event systems (DES) have become pervasive in our daily lives. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a “hard nut to crack”. The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. 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 technology, and their future research directions. Contents:Part I: Perturbation Analysis:The IPA Calculus for Hybrid SystemsSmoothed Perturbation Analysis: A Retrospective and Prospective LookPerturbation Analysis and Variance Reduction in Monte Carlo SimulationAdjoints and AveragingInfinitesimal Perturbation Analysis and Optimization AlgorithmsSimulation-based Optimization of Failure-prone Continuous Flow LinesPerturbation Analysis, Dynamic Programming, and BeyondPart II: Ordinal Optimization:Fundamentals of Ordinal OptimizationOptimal Computing Budget Allocation FrameworkNested PartitionsApplications of Ordinal Optimization Readership: Professionals in industrial and systems engineering, graduate reference for probability & statistics, stochastic analysis and general computer science, and research. Keywords:Simulation;Optimization;Stochastic Systems;Discrete-Even Systems;Perturbation Analysis;Ordinal Optimization

Optimization of Stochastic Models

Georg Ch. Pflug
Optimization of Stochastic Models Book PDF

Author : Georg Ch. Pflug
Publisher : Springer Science & Business Media
Page : 384 pages
File Size : 45,6 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9781461314493

Get Book

Optimization of Stochastic Models by Georg Ch. Pflug Pdf

Stochastic models are everywhere. In manufacturing, queuing models are used for modeling production processes, realistic inventory models are stochastic in nature. Stochastic models are considered in transportation and communication. Marketing models use stochastic descriptions of the demands and buyer's behaviors. In finance, market prices and exchange rates are assumed to be certain stochastic processes, and insurance claims appear at random times with random amounts. To each decision problem, a cost function is associated. Costs may be direct or indirect, like loss of time, quality deterioration, loss in production or dissatisfaction of customers. In decision making under uncertainty, the goal is to minimize the expected costs. However, in practically all realistic models, the calculation of the expected costs is impossible due to the model complexity. 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 does not even try to touch all work done in this area. Instead, many concepts are presented and treated with mathematical rigor and necessary conditions for the correctness of various approaches are stated. 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.

Foundations and Methods of Stochastic Simulation

Barry Nelson
Foundations and Methods of Stochastic Simulation Book PDF

Author : Barry Nelson
Publisher : Springer Science & Business Media
Page : 285 pages
File Size : 53,8 Mb
Release : 2013-01-31
Category : Business & Economics
ISBN : 9781461461609

Get Book

Foundations and Methods of Stochastic Simulation by Barry Nelson Pdf

This graduate-level text covers modeling, programming and analysis of simulation experiments and provides a rigorous treatment of the foundations of simulation and why it works. It introduces object-oriented programming for simulation, covers both the probabilistic and statistical basis for simulation in a rigorous but accessible manner (providing all necessary background material); and provides a modern treatment of experiment design and analysis that goes beyond classical statistics. The book emphasizes essential foundations throughout, rather than providing a compendium of algorithms and theorems and prepares the reader to use simulation in research as well as practice. The book is a rigorous, but concise treatment, emphasizing lasting principles but also providing specific training in modeling, programming and analysis. In addition to teaching readers how to do simulation, it also prepares them to use simulation in their research; no other book does this. An online solutions manual for end of chapter exercises is also provided.​

Handbook of Simulation Optimization

Michael C Fu
Handbook of Simulation Optimization Book PDF

Author : Michael C Fu
Publisher : Springer
Page : 400 pages
File Size : 55,8 Mb
Release : 2014-11-13
Category : Business & Economics
ISBN : 9781493913848

Get Book

Handbook of Simulation Optimization by Michael C Fu Pdf

The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of 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, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, variance reduction techniques, model-based stochastic search methods and Markov decision processes. This single volume should serve as a reference for those already in the field and as a means for those new to the field for understanding and applying the main approaches. The intended audience includes researchers, practitioners and graduate students in the business/engineering fields of operations research, management science, operations management and stochastic control, as well as in economics/finance and computer science.

Introduction to Stochastic Search and Optimization

James C. Spall
Introduction to Stochastic Search and Optimization Book PDF

Author : James C. Spall
Publisher : John Wiley & Sons
Page : 620 pages
File Size : 49,6 Mb
Release : 2005-03-11
Category : Mathematics
ISBN : 9780471441908

Get Book

Introduction to Stochastic Search and Optimization by James C. Spall Pdf

* Unique in its survey of the range of topics. * Contains a strong, interdisciplinary format that will appeal to both students and researchers. * Features exercises and web links to software and data sets.

Foundations and Methods of Stochastic Simulation

Barry L. Nelson,Linda Pei
Foundations and Methods of Stochastic Simulation Book PDF

Author : Barry L. Nelson,Linda Pei
Publisher : Springer Nature
Page : 323 pages
File Size : 43,7 Mb
Release : 2021-11-10
Category : Business & Economics
ISBN : 9783030861940

Get Book

Foundations and Methods of Stochastic Simulation by Barry L. Nelson,Linda Pei Pdf

This graduate-level textbook covers modelling, programming and analysis of stochastic computer simulation experiments, including the mathematical and statistical foundations of simulation and why it works. The book is rigorous and complete, but concise and accessible, providing all necessary background material. Object-oriented programming of simulations is illustrated in Python, while the majority of the book is programming language independent. In addition to covering the foundations of simulation and simulation programming for applications, the text prepares readers to use simulation in their research. A solutions manual for end-of-chapter exercises is available for instructors.

Modeling, Stochastic Control, Optimization, and Applications

George Yin,Qing Zhang
Modeling Stochastic Control Optimization and Applications Book PDF

Author : George Yin,Qing Zhang
Publisher : Springer
Page : 599 pages
File Size : 46,6 Mb
Release : 2019-07-16
Category : Mathematics
ISBN : 9783030254988

Get Book

Modeling, Stochastic Control, Optimization, and Applications by George Yin,Qing Zhang Pdf

This volume collects papers, based on invited talks given at the IMA workshop in Modeling, Stochastic Control, Optimization, and Related Applications, held at the Institute for Mathematics and Its Applications, University of Minnesota, during May and June, 2018. There were four week-long workshops during the conference. They are (1) stochastic control, computation methods, and applications, (2) queueing theory and networked systems, (3) ecological and biological applications, and (4) finance and economics applications. For broader impacts, researchers from different fields covering both theoretically oriented and application intensive areas were invited to participate in the conference. It brought together researchers from multi-disciplinary communities in applied mathematics, applied probability, engineering, biology, ecology, and networked science, to review, and substantially update most recent progress. As an archive, this volume presents some of the highlights of the workshops, and collect papers covering a broad range of topics.

Stochastic Multi-Stage Optimization

Pierre Carpentier,Jean-Philippe Chancelier,Guy Cohen,Michel De Lara
Stochastic Multi Stage Optimization Book PDF

Author : Pierre Carpentier,Jean-Philippe Chancelier,Guy Cohen,Michel De Lara
Publisher : Springer
Page : 362 pages
File Size : 45,8 Mb
Release : 2015-05-05
Category : Mathematics
ISBN : 9783319181387

Get Book

Stochastic Multi-Stage Optimization by Pierre Carpentier,Jean-Philippe Chancelier,Guy Cohen,Michel De Lara Pdf

The focus of the present volume is stochastic optimization of dynamical systems in discrete time where - by concentrating on the role of information regarding optimization problems - it discusses the related discretization issues. There is a growing need to tackle uncertainty in applications of optimization. For example the massive introduction of renewable energies in power systems challenges traditional ways to manage them. This book lays out basic and advanced tools to handle and numerically solve such problems and thereby is building a bridge between Stochastic Programming and Stochastic Control. It is intended for graduates readers and scholars in optimization or stochastic control, as well as engineers with a background in applied mathematics.

Simulation-Based Optimization

Abhijit Gosavi
Simulation Based Optimization Book PDF

Author : Abhijit Gosavi
Publisher : Springer
Page : 530 pages
File Size : 55,9 Mb
Release : 2014-10-30
Category : Business & Economics
ISBN : 9781489974914

Get Book

Simulation-Based Optimization by Abhijit Gosavi Pdf

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.

Reinforcement Learning and Stochastic Optimization

Warren B. Powell
Reinforcement Learning and Stochastic Optimization Book PDF

Author : Warren B. Powell
Publisher : John Wiley & Sons
Page : 1090 pages
File Size : 49,8 Mb
Release : 2022-03-15
Category : Mathematics
ISBN : 9781119815037

Get Book

Reinforcement Learning and Stochastic Optimization by Warren B. Powell Pdf

REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

Stochastic Optimization

Johannes Schneider,Scott Kirkpatrick
Stochastic Optimization Book PDF

Author : Johannes Schneider,Scott Kirkpatrick
Publisher : Springer Science & Business Media
Page : 568 pages
File Size : 53,7 Mb
Release : 2007-08-06
Category : Computers
ISBN : 9783540345602

Get Book

Stochastic Optimization by Johannes Schneider,Scott Kirkpatrick Pdf

This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.

Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization

J.B. Marco,R. Harboe,J.D. Salas
Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization Book PDF

Author : J.B. Marco,R. Harboe,J.D. Salas
Publisher : Springer Science & Business Media
Page : 470 pages
File Size : 41,6 Mb
Release : 2012-12-06
Category : Science
ISBN : 9789401116978

Get Book

Stochastic Hydrology and its Use in Water Resources Systems Simulation and Optimization by J.B. Marco,R. Harboe,J.D. Salas Pdf

Stochastic hydrology is an essential base of water resources systems analysis, due to the inherent randomness of the input, and consequently of the results. These results have to be incorporated in a decision-making process regarding the planning and management of water systems. It is through this application that stochastic hydrology finds its true meaning, otherwise it becomes merely an academic exercise. A set of well known specialists from both stochastic hydrology and water resources systems present a synthesis of the actual knowledge currently used in real-world planning and management. The book is intended for both practitioners and researchers who are willing to apply advanced approaches for incorporating hydrological randomness and uncertainty into the simulation and optimization of water resources systems. (abstract) Stochastic hydrology is a basic tool for water resources systems analysis, due to inherent randomness of the hydrologic cycle. 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.

Stochastic Simulation: Algorithms and Analysis

Søren Asmussen,Peter W. Glynn
Stochastic Simulation Algorithms and Analysis Book PDF

Author : Søren Asmussen,Peter W. Glynn
Publisher : Springer Science & Business Media
Page : 490 pages
File Size : 45,8 Mb
Release : 2007-07-14
Category : Mathematics
ISBN : 9780387690339

Get Book

Stochastic Simulation: Algorithms and Analysis by Søren Asmussen,Peter W. Glynn Pdf

Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods; the second half discusses model-specific algorithms. Exercises and illustrations are included.

Optimization of Stochastic Models

Georg Ch. Pflug
Optimization of Stochastic Models Book PDF

Author : Georg Ch. Pflug
Publisher : Springer
Page : 382 pages
File Size : 54,5 Mb
Release : 1997-10-14
Category : Business & Economics
ISBN : 146128631X

Get Book

Optimization of Stochastic Models by Georg Ch. Pflug Pdf

Stochastic models are everywhere. In manufacturing, queuing models are used for modeling production processes, realistic inventory models are stochastic in nature. Stochastic models are considered in transportation and communication. Marketing models use stochastic descriptions of the demands and buyer's behaviors. In finance, market prices and exchange rates are assumed to be certain stochastic processes, and insurance claims appear at random times with random amounts. To each decision problem, a cost function is associated. Costs may be direct or indirect, like loss of time, quality deterioration, loss in production or dissatisfaction of customers. In decision making under uncertainty, the goal is to minimize the expected costs. However, in practically all realistic models, the calculation of the expected costs is impossible due to the model complexity. 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 does not even try to touch all work done in this area. Instead, many concepts are presented and treated with mathematical rigor and necessary conditions for the correctness of various approaches are stated. 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.