Stochastic Simulation Optimization An Optimal Computing Budget Allocation
DOWNLOAD
Download Stochastic Simulation Optimization An Optimal Computing Budget Allocation PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Stochastic Simulation Optimization An Optimal Computing Budget Allocation book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Stochastic Simulation Optimization
DOWNLOAD
Author : Chun-hung Chen
language : en
Publisher: World Scientific
Release Date : 2011
Stochastic Simulation Optimization written by Chun-hung Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.
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 Perturbation Analysis Ordinal Optimization And Beyond
DOWNLOAD
Author : Chun-hung Chen
language : en
Publisher: World Scientific
Release Date : 2013-07-03
Stochastic Simulation Optimization For Discrete Event Systems Perturbation Analysis Ordinal Optimization And Beyond written by Chun-hung Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-03 with Technology & Engineering categories.
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.
Efficient Computing Budget Allocation For Stochastic Simulation Optimization
DOWNLOAD
Author : Donghai He
language : en
Publisher:
Release Date : 2008
Efficient Computing Budget Allocation For Stochastic Simulation Optimization written by Donghai He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computer simulation categories.
Handbook Of Simulation Optimization
DOWNLOAD
Author : Michael C Fu
language : en
Publisher: Springer
Release Date : 2014-11-13
Handbook Of Simulation Optimization written by Michael C Fu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-13 with Business & Economics categories.
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.
Optimal Computing Budget Allocation In Selecting The Best Design Via Discrete Event Simulation
DOWNLOAD
Author : Hsiao Chang Chen
language : en
Publisher:
Release Date : 1998
Optimal Computing Budget Allocation In Selecting The Best Design Via Discrete Event Simulation written by Hsiao Chang Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Stochastic Recursive Algorithms For Optimization
DOWNLOAD
Author : S. Bhatnagar
language : en
Publisher: Springer
Release Date : 2012-08-11
Stochastic Recursive Algorithms For Optimization written by S. Bhatnagar and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-11 with Technology & Engineering categories.
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from similarly diverse backgrounds: workers in relevant areas of computer science, control engineering, management science, applied mathematics, industrial engineering and operations research will find the content of value.
Optimal Allocation And Splitting Among Designs In Rare Event Simulation
DOWNLOAD
Author : Ben W. Crain
language : en
Publisher:
Release Date : 2013
Optimal Allocation And Splitting Among Designs In Rare Event Simulation written by Ben W. Crain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computer algorithms categories.
This dissertation develops efficient algorithms, in theory and in implementation, for selecting, via simulation, the best design, or system, from a set of designs, where "best" is the design with the smallest probability of some (generally undesirable) outcome. Compared to standard techniques these algorithms improve the efficiency of simulation when the (undesirable) outcomes have very small probabilities, on the order of 1.0E-6, or smaller. Such outcomes are "rare events". The algorithms could also be used to estimate non-rare probabilities, although, in that case, their advantages over techniques not geared toward rare events diminish. The designs in question differ in construction, or in the values of their parameters, but are such that their operations can be simulated as stochastic processes which terminate in a non-rare event (a set of non-rare outcomes), or a rare event (a set of rare outcomes). The task is to estimate, efficiently, the probabilities of the rare events, in order to select the design with the smallest one. Efficient algorithms are those which produce estimators of the rare event probabilities with acceptable variances, within acceptable computational times. I do not define "acceptable variances". The focus is on algorithms which achieve better results, compared to standard methods, for a given amount of computational time (a given computing budget constraint). Better results are achieved by (approximately) maximizing the Probability of Correct Selection (PCS) of an algorithm, subject to a computing budget constraint. PCS is the probability that the algorithm will correctly identify the best design. Simulation algorithms against which the new algorithms are compared include simple Monte Carlo (MC), Optimal Computing Budget Allocation (OCBA), fixed-effort Splitting, and the Optimal Splitting Technique for Rare-Event Simulation (OSTRE). These algorithms are reviewed, prior to the development of the two new algorithms: Single Optimization and OCBA+OSTRE. The major contribution of this dissertation is the theoretical development and practical implementation of these new algorithms. The mathematical equivalence of these two new methods (in the sense that they attain, in theory, the same maximum PCS) is proven, and their computational complexities are compared. Numerical testing illustrates that they can out-perform standard techniques, and suggests that OCBA+OSTRE is better, in practice, than Single Optimization.
Stochastic Simulation Optimization For Discrete Event Systems
DOWNLOAD
Author : Chun-Hung Chen
language : en
Publisher: World Scientific
Release Date : 2013
Stochastic Simulation Optimization For Discrete Event Systems written by Chun-Hung Chen and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Mathematics categories.
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.
Adversarial Risk Analysis
DOWNLOAD
Author : David L. Banks
language : en
Publisher: CRC Press
Release Date : 2015-06-30
Adversarial Risk Analysis written by David L. Banks and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-30 with Business & Economics categories.
Winner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against
Conditional Monte Carlo
DOWNLOAD
Author : Michael C. Fu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Conditional Monte Carlo written by Michael C. Fu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
Conditional Monte Carlo: Gradient Estimation and Optimization Applications deals with various gradient estimation techniques of perturbation analysis based on the use of conditional expectation. The primary setting is discrete-event stochastic simulation. This book presents applications to queueing and inventory, and to other diverse areas such as financial derivatives, pricing and statistical quality control. To researchers already in the area, this book offers a unified perspective and adequately summarizes the state of the art. To researchers new to the area, this book offers a more systematic and accessible means of understanding the techniques without having to scour through the immense literature and learn a new set of notation with each paper. To practitioners, this book provides a number of diverse application areas that makes the intuition accessible without having to fully commit to understanding all the theoretical niceties. In sum, the objectives of this monograph are two-fold: to bring together many of the interesting developments in perturbation analysis based on conditioning under a more unified framework, and to illustrate the diversity of applications to which these techniques can be applied. Conditional Monte Carlo: Gradient Estimation and Optimization Applications is suitable as a secondary text for graduate level courses on stochastic simulations, and as a reference for researchers and practitioners in industry.