Handbooks in Operations Research and Management Science: Simulation

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Shane G. Henderson, Barry L. Nelson
Elsevier, Sep 2, 2006 - Business & Economics - 692 pages
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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
 

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Contents

Chapter 1 Stochastic Computer Simulation
1
Chapter 2 Mathematics for Simulation
19
Chapter 3 Uniform Random Number Generation
55
Chapter 4 Nonuniform Random Variate Generation
83
Chapter 5 Multivariate Input Processes
123
Chapter 6 Arrival Processes Random Lifetimes and Random Objects
155
Chapter 7 Implementing Representations of Uncertainty
181
Chapter 8 Statistical Estimation in Computer Simulation
193
Chapter 13 Analysis for Design
381
Chapter 14 Resampling Methods
415
Chapter 15 CorrelationBased Methods for Output Analysis
455
Chapter 16 Simulation Algorithms for Regenerative Processes
477
Chapter 17 Selecting the Best System
501
Chapter 18 MetamodelBased Simulation Optimization
535
Chapter 19 Gradient Estimation
575
Chapter 20 An Overview of Simulation Optimization via Random Search
617

Chapter 9 Subjective Probability and Bayesian Methodology
225
Chapter 10 A Hilbert Space Approach to Variance Reduction
259
An Introduction and Recent Advances
291
Chapter 12 QuasiRandom Number Techniques
351
Chapter 21 Metaheuristics
633
Author Index
655
Subject Index
667
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