Convergence Properties Of The Nested Partitions Method For Stochastic Optimization
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Convergence Properties Of The Nested Partitions Method For Stochastic Optimization
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Author : Sigurdur Ólafsson
language : en
Publisher:
Release Date : 1998
Convergence Properties Of The Nested Partitions Method For Stochastic Optimization written by Sigurdur Ólafsson 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.
Nested Partitions Method Theory And Applications
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Author : Leyuan Shi
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-30
Nested Partitions Method Theory And Applications written by Leyuan Shi 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 2008-10-30 with Mathematics categories.
Thesubjectofthisbookisthenested partitions method(NP),arelativelynew optimization method that has been found to be very e?ective solving discrete optimization problems. Such discrete problems are common in many practical applications and the NP method is thus useful in diverse application areas. It can be applied to both operational and planning problems and has been demonstrated to e?ectively solve complex problems in both manufacturing and service industries. To illustrate its broad applicability and e?ectiveness, in this book we will show how the NP method has been successful in solving complex problems in planning and scheduling, logistics and transportation, supply chain design, data mining, and health care. All of these diverse app- cationshaveonecharacteristicincommon:theyallleadtocomplexlarge-scale discreteoptimizationproblemsthatareintractableusingtraditionaloptimi- tion methods. 1.1 Large-Scale Optimization IndevelopingtheNPmethodwewillconsideroptimization problemsthatcan be stated mathematically in the following generic form: minf(x), (1.1) x?X where the solution space or feasible region X is either a discrete or bounded ? set of feasible solutions. We denote a solution to this problem x and the ? ? objective function value f = f (x ).
Proceedings Of The 1998 Winter Simulation Conference
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Author : D. J. Medeiros
language : en
Publisher:
Release Date : 1998
Proceedings Of The 1998 Winter Simulation Conference written by D. J. Medeiros and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Digital computer simulation categories.
Stochastic Simulation Optimization For Discrete Event Systems
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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.
Proceedings Of The Winter Simulation Conference
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Author :
language : en
Publisher:
Release Date : 1997
Proceedings Of The Winter Simulation Conference written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Digital computer simulation categories.
Simulation Based Algorithms For Markov Decision Processes
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Author : Hyeong Soo Chang
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-02-26
Simulation Based Algorithms For Markov Decision Processes written by Hyeong Soo Chang 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 2013-02-26 with Technology & Engineering categories.
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical solution of the resulting models intractable. In other cases, the system of interest is too complex to allow explicit specification of some of the MDP model parameters, but simulation samples are readily available (e.g., for random transitions and costs). For these settings, various sampling and population-based algorithms have been developed to overcome the difficulties of computing an optimal solution in terms of a policy and/or value function. Specific approaches include adaptive sampling, evolutionary policy iteration, evolutionary random policy search, and model reference adaptive search. This substantially enlarged new edition reflects the latest developments in novel algorithms and their underpinning theories, and presents an updated account of the topics that have emerged since the publication of the first edition. Includes: innovative material on MDPs, both in constrained settings and with uncertain transition properties; game-theoretic method for solving MDPs; theories for developing roll-out based algorithms; and details of approximation stochastic annealing, a population-based on-line simulation-based algorithm. The self-contained approach of this book will appeal not only to researchers in MDPs, stochastic modeling, and control, and simulation but will be a valuable source of tuition and reference for students of control and operations research.
Simulation Based Optimization
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Author : Abhijit Gosavi
language : en
Publisher: Springer
Release Date : 2014-10-30
Simulation Based Optimization written by Abhijit Gosavi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-30 with Business & Economics categories.
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.
Handbooks In Operations Research And Management Science Simulation
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Author : Shane G. Henderson
language : en
Publisher: Elsevier
Release Date : 2006-09-02
Handbooks In Operations Research And Management Science Simulation written by Shane G. Henderson and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-02 with Business & Economics categories.
This Handbook is a collection of chapters on key issues in the design and analysis of computer simulation experiments on models of stochastic systems. The chapters are tightly focused and written by experts in each area. For the purpose of this volume "simulation refers to the analysis of stochastic processes through the generation of sample paths (realization) of the processes. Attention focuses on design and analysis issues and the goal of this volume is to survey the concepts, principles, tools and techniques that underlie the theory and practice of stochastic simulation design and analysis. Emphasis is placed on the ideas and methods that are likely to remain an intrinsic part of the foundation of the field for the foreseeable future. The chapters provide up-to-date references for both the simulation researcher and the advanced simulation user, but they do not constitute an introductory level 'how to' guide. Computer scientists, financial analysts, industrial engineers, management scientists, operations researchers and many other professionals use stochastic simulation to design, understand and improve communications, financial, manufacturing, logistics, and service systems. A theme that runs throughout these diverse applications is the need to evaluate system performance in the face of uncertainty, including uncertainty in user load, interest rates, demand for product, availability of goods, cost of transportation and equipment failures.* Tightly focused chapters written by experts* Surveys concepts, principles, tools, and techniques that underlie the theory and practice of stochastic simulation design and analysis* Provides an up-to-date reference for both simulation researchers and advanced simulation users
American Doctoral Dissertations
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Author :
language : en
Publisher:
Release Date : 1998
American Doctoral Dissertations written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Dissertation abstracts categories.
Statistical Theory And Method Abstracts
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Author :
language : en
Publisher:
Release Date : 2001
Statistical Theory And Method Abstracts written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Statistics categories.