Handbooks in Operations Research and Management Science: Simulation
Shane G. Henderson, Barry L. Nelson
Elsevier, Sep 2, 2006 - Business & Economics - 692 pages
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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Chapter 4 Nonuniform Random Variate Generation
Chapter 5 Multivariate Input Processes
Chapter 6 Arrival Processes Random Lifetimes and Random Objects
Chapter 7 Implementing Representations of Uncertainty
Chapter 8 Statistical Estimation in Computer Simulation
Chapter 13 Analysis for Design
Chapter 14 Resampling Methods
Chapter 15 CorrelationBased Methods for Output Analysis
Chapter 16 Simulation Algorithms for Regenerative Processes
Chapter 17 Selecting the Best System
Chapter 18 MetamodelBased Simulation Optimization
Chapter 19 Gradient Estimation
Chapter 20 An Overview of Simulation Optimization via Random Search
ambulance analysis applied approach approximation asymptotic variance asymptotically optimal batch Bayesian bias bootstrap change of measure Chapter coefficients computational confidence interval consider convergence correlation defined denote density dependence deterministic discrete-event discussion distribution Equation event example exponential finite function genetic algorithms given Glasserman Glynn Goldsman gradient estimation IEEE Press importance sampling independent input integration L’Ecuyer lattice linear Markov chain Mathematical matrix metaheuristics metamodel Monte Carlo multivariate normal obtained Operations Research parameter performance measures Piscataway point sets Poisson process polynomial probability problem procedure Quasi-Monte Carlo Methods queue random numbers random search methods random variables random vector regenerative replications response surface RNGs Section selection sequence simulation model simulation optimization simulation run solution Springer-Verlag stationary statistical steady-state simulation stochastic process tabu search techniques Theorem tion variance reduction variates Whitt Winter Simulation Conference