Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks PDF Download

Author: Reuven Y. Rubinstein
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 282

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Book Description
A theoretical treatment of Monte Carlo optimization--simulation using perturbation analysis, adaptive methods, and variance reduction techniques. Emphasizes concepts rather than mathematical completeness. Shows how to use simulation and Monte Carlo methods efficiently for estimating performance measures, sensitivities and optimization of stochastic systems.

Author: Reuven Y. Rubinstein
Publisher: John Wiley & Sons
ISBN: 0470317221
Category : Mathematics
Languages : en
Pages : 308

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Book Description
This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, regenerative simulation, and Monte Carlo optimization.

Author: Reuven Y. Rubinstein
Publisher: Wiley-Interscience
ISBN: 9780470139011
Category : Mathematics
Languages : en
Pages : 0

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Book Description
This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, regenerative simulation, and Monte Carlo optimization.

Author: Reuven Y. Rubinstein
Publisher: Wiley-Interscience
ISBN: 9780470345245
Category : Mathematics
Languages : en
Pages : 0

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Book Description
This set contains the text Simulation and the Monte Carlo Method, Second Edition 9780470177945 and the Student Solutions Manual to Accompany Simulation and the Monte Carlo Method, Second Edition 9780470258798.

Author: Reuven Y. Rubinstein
Publisher: John Wiley & Sons
ISBN: 1118210522
Category : Mathematics
Languages : en
Pages : 331

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Book Description
This accessible new edition explores the major topics in Monte Carlo simulation Simulation and the Monte Carlo Method, Second Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the major topics that have emerged in Monte Carlo simulation since the publication of the classic First Edition over twenty-five years ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo Variance reduction techniques such as the transform likelihood ratio method and the screening method The score function method for sensitivity analysis The stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization The cross-entropy method to rare events estimation and combinatorial optimization Application of Monte Carlo techniques for counting problems, with an emphasis on the parametric minimum cross-entropy method An extensive range of exercises is provided at the end of each chapter, with more difficult sections and exercises marked accordingly for advanced readers. A generous sampling of applied examples is positioned throughout the book, emphasizing various areas of application, and a detailed appendix presents an introduction to exponential families, a discussion of the computational complexity of stochastic programming problems, and sample MATLAB programs. Requiring only a basic, introductory knowledge of probability and statistics, Simulation and the Monte Carlo Method, Second Edition is an excellent text for upper-undergraduate and beginning graduate courses in simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method.

Author: Georg Pflug
Publisher: Springer Science & Business Media
ISBN: 3642489141
Category : Business & Economics
Languages : en
Pages : 175

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Book Description
This volume contains selected papers presented at the "International Workshop on Computationally Intensive Methods in Simulation and Op th th timization" held from 23 to 25 August 1990 at the International Institute for Applied Systems Analysis (nASA) in La~enburg, Austria. The purpose of this workshop was to evaluate and to compare recently developed methods dealing with optimization in uncertain environments. It is one of the nASA's activities to study optimal decisions for uncertain systems and to make the result usable in economic, financial, ecological and resource planning. Over 40 participants from 12 different countries contributed to the success of the workshop, 12 papers were selected for this volume. Prof. A. Kurzhanskii Chairman of the Systems and Decision Sciences Program nASA Preface Optimization in an random environment has become an important branch of Applied Mathematics and Operations Research. It deals with optimal de cisions when only incomplete information of t.he future is available. Consider the following example: you have to make the decision about the amount of production although the future demand is unknown. If the size of the de mand can be described by a probability distribution, the problem is called a stochastic optimization problem.

Author: Hong Chen
Publisher: Springer Science & Business Media
ISBN: 9780387951669
Category : Business & Economics
Languages : en
Pages : 512

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Book Description
"The selection of materials is well balanced in breadth and depth, making the book an ideal graduate-level text for students in engineering, business, applied mathematics, and probability and statistics.

Author: Reuven Y. Rubinstein
Publisher: Wiley-Interscience
ISBN:
Category : Mathematics
Languages : en
Pages : 392

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Book Description
A step-by-step guide for today's modeling and simulation practices This new guide for modeling and simulation of discrete-event systems (DES) demonstrates why simulation is fast becoming the method of choice for the evaluation of system performance in science, engineering, and management. The book begins with the basics of conventional simulation, then proceeds to modern simulation-treating sensitivity analysis and optimization in a wide range of systems that exhibit complex interaction of discrete events. These include communications networks, flexible manufacturing systems, PERT (project evaluation and review techniques) networks, queueing systems, and more. Less focused on theory than on presenting a clear approach to practical applications, Modern Simulation and Modeling: * Emphasizes concepts rather than mathematical completeness * Integrates references and explanations of complex topics into the body of the text * Provides an innovative chapter on rare-event probability estimation * Describes the implementation of the score function (SF) method using the NSO simulation package * Features 40 illustrations and numerous algorithms * Offers extensive, end-of-chapter exercise sets * Includes chapter bibliographies for further reading Modern Simulation and Modeling is an essential text for graduate students of DES and stochastic processes and for undergraduate students in simulation. It is also an excellent reference for professionals in statistics and probability, mathematics, and management science.

Author: Jewgeni H. Dshalalow
Publisher: CRC Press
ISBN: 9780849380761
Category : Business & Economics
Languages : en
Pages : 482

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Book Description
Queueing systems and networks are being applied to many areas of technology today, including telecommunications, computers, satellite systems, and traffic processes. This timely book, written by 26 of the most respected and influential researchers in the field, provides an overview of fundamental queueing systems and networks as applied to these technologies. Frontiers in Queueing: Models and Applications in Science and Engineering was written with more of an engineering slant than its predecessor, Advances in Queueing: Theory, Methods, and Open Problems. The earlier book was primarily concerned with methods, and was more theoretically oriented. This new volume, meant to be a sequel to the first book, was written by scientists and queueing theorists whose expertise is in technology and engineering, allowing readers to answer questions regarding the technicalities of related methods from the earlier book. Each chapter in the book surveys the classes of queueing models and networks, or the applied methods in queueing, and is followed by a discussion of open problems and future research directions. The discussion of these future trends is especially important to novice researchers, students, and even their advisors, as it provides the perspectives of eminent scientists in each area, thus showing where research efforts should be focused. Frontiers in Queueing: Models and Applications in Science and Engineering also includes applications to vital areas of engineering and technology, specifically, telecommunications, computers and computer networks, satellite systems, traffic processes, and more applied methods such as simulation, statistics, and numerical methods. All researchers, from students to advanced professionals, can benefit from the sound advice and perspective of the contributors represented in this book.

Author: Neal Noah Madras
Publisher: American Mathematical Soc.
ISBN: 0821829785
Category : Mathematics
Languages : en
Pages : 113

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Book Description
Monte Carlo methods form an experimental branch of mathematics that employs simulations driven by random number generators. These methods are often used when others fail, since they are much less sensitive to the ``curse of dimensionality'', which plagues deterministic methods in problems with a large number of variables. Monte Carlo methods are used in many fields: mathematics, statistics, physics, chemistry, finance, computer science, and biology, for instance. This book is an introduction to Monte Carlo methods for anyone who would like to use these methods to study various kinds of mathematical models that arise in diverse areas of application. The book is based on lectures in a graduate course given by the author. It examines theoretical properties of Monte Carlo methods as well as practical issues concerning their computer implementation and statistical analysis. The only formal prerequisite is an undergraduate course in probability. The book is intended to be accessible to students from a wide range of scientific backgrounds. Rather than being a detailed treatise, it covers the key topics of Monte Carlo methods to the depth necessary for a researcher to design, implement, and analyze a full Monte Carlo study of a mathematical or scientific problem. The ideas are illustrated with diverse running examples. There are exercises sprinkled throughout the text. The topics covered include computer generation of random variables, techniques and examples for variance reduction of Monte Carlo estimates, Markov chain Monte Carlo, and statistical analysis of Monte Carlo output.