Stochastic Simulation Optimization
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Author |
: Chun-hung Chen |
Publisher |
: World Scientific |
Total Pages |
: 246 |
Release |
: 2011 |
ISBN-10 |
: 9789814282642 |
ISBN-13 |
: 9814282642 |
Rating |
: 4/5 (42 Downloads) |
Synopsis Stochastic Simulation Optimization by : Chun-hung Chen
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.
Author |
: Chun-hung Chen |
Publisher |
: World Scientific |
Total Pages |
: 274 |
Release |
: 2013-07-03 |
ISBN-10 |
: 9789814513029 |
ISBN-13 |
: 9814513024 |
Rating |
: 4/5 (29 Downloads) |
Synopsis Stochastic Simulation Optimization For Discrete Event Systems: Perturbation Analysis, Ordinal Optimization And Beyond by : Chun-hung Chen
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.
Author |
: Barry Nelson |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 285 |
Release |
: 2013-01-31 |
ISBN-10 |
: 9781461461609 |
ISBN-13 |
: 146146160X |
Rating |
: 4/5 (09 Downloads) |
Synopsis Foundations and Methods of Stochastic Simulation by : Barry Nelson
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.
Author |
: Michael C Fu |
Publisher |
: Springer |
Total Pages |
: 400 |
Release |
: 2014-11-13 |
ISBN-10 |
: 9781493913848 |
ISBN-13 |
: 1493913840 |
Rating |
: 4/5 (48 Downloads) |
Synopsis Handbook of Simulation Optimization by : Michael C Fu
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.
Author |
: James C. Spall |
Publisher |
: John Wiley & Sons |
Total Pages |
: 620 |
Release |
: 2005-03-11 |
ISBN-10 |
: 9780471441908 |
ISBN-13 |
: 0471441902 |
Rating |
: 4/5 (08 Downloads) |
Synopsis Introduction to Stochastic Search and Optimization by : James C. Spall
* 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.
Author |
: Abhijit Gosavi |
Publisher |
: Springer |
Total Pages |
: 530 |
Release |
: 2014-10-30 |
ISBN-10 |
: 9781489974914 |
ISBN-13 |
: 1489974911 |
Rating |
: 4/5 (14 Downloads) |
Synopsis Simulation-Based Optimization by : Abhijit Gosavi
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.
Author |
: Barry L. Nelson |
Publisher |
: Courier Corporation |
Total Pages |
: 338 |
Release |
: 2012-10-11 |
ISBN-10 |
: 9780486139944 |
ISBN-13 |
: 0486139948 |
Rating |
: 4/5 (44 Downloads) |
Synopsis Stochastic Modeling by : Barry L. Nelson
Coherent introduction to techniques also offers a guide to the mathematical, numerical, and simulation tools of systems analysis. Includes formulation of models, analysis, and interpretation of results. 1995 edition.
Author |
: Pierre Carpentier |
Publisher |
: |
Total Pages |
: |
Release |
: 2015 |
ISBN-10 |
: 3319181394 |
ISBN-13 |
: 9783319181394 |
Rating |
: 4/5 (94 Downloads) |
Synopsis Stochastic Multi-Stage Optimization by : Pierre Carpentier
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.
Author |
: Georg Ch. Pflug |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 384 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461314493 |
ISBN-13 |
: 1461314496 |
Rating |
: 4/5 (93 Downloads) |
Synopsis Optimization of Stochastic Models by : Georg Ch. Pflug
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.
Author |
: Johannes Schneider |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 551 |
Release |
: 2007-08-06 |
ISBN-10 |
: 9783540345602 |
ISBN-13 |
: 3540345604 |
Rating |
: 4/5 (02 Downloads) |
Synopsis Stochastic Optimization by : Johannes Schneider
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.