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| HANDBOOKS IN OPERATIONS RESEARCH AND MANAGEMENT SCIENCE: SIMULATION, 13
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Edited By
Shane Henderson, School of Operations and Industrial Engineering, Cornell University, Ithaca, NY, USA
Barry Nelson, Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA
Included in series
Handbooks in Operations Research and Management Science,
Description
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.
Audience
computer scientists, financial analysts, industrial engineers, management scientists, operations researchers
Contents
1. Stochastic computer simulation (S.G. Henderson, B.L. Nelson).
2. Mathematics for simulation (S.G. Henderson).
3. Uniform random number
generation (P. L'Ecuyer). 4. Non-Uniform random variate generation (L. Devroye).
5. Multivariate input processes (B. Biller, S. Ghosh).
6. Arrival processes, random lifetimes, and random objects (L. M. Leemis).
7. Implementing representations of uncertainty (W.D. Kelton).
8. Statistical estimation in computer simulation (C. Alexopoulos).
9. Subjective probability and Bayesian methodology (S.E. Chick).
10. A Hilbert space approach to variance reduction (R.Szechtman).
11. Rare-event simulation techniques(S.Juneja, P.Shahabuddin).
12.
Quasi-random number techniques (C. Lemieux). 13. Analysis for design (W. Whitt).
14. Resampling methods (R.C.H. Cheng).
15. Correlation-based
methods for output analysis (D. Goldsman, B.L. Nelson).
16. Simulation algorithms for regenerative processes (P.W. Glynn).
17. Selecting
the best system (S.-H. Kim, B. L. Nelson).
18. Metamodel-based simulation optimization (R.R. Barton, M. Meckesheimer).
19. Gradient
estimation (M.C. Fu).
20. An overview of simulation optimization via random search (S.Andratd ttir).
21. Metaheuristics (S. lafsson).
| Bibliographic details |
Hardbound, 692 pages, publication date: SEP-2006
ISBN-13: 978-0-444-51428-8
ISBN-10: 0-444-51428-7
Imprint: NORTH-HOLLAND
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080/851
Last update: 30 Nov 2009
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