Handbooks in Operations Research and Management Science: Simulation, Volume 13

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).
4. Multivariate input processes (B. Biller, S. Ghosh).
5. Arrival processes, random lifetimes, and random objects (L. M. Leemis).
6. Implementing representations of uncertainty (W.D. Kelton).
7. Statistical estimation in computer simulation (C. Alexopoulos).
8. Subjective probability and Bayesian methodology (S.E. Chick).
9. A Hilbert space approach to variance reduction (R.Szechtman).
10. Rare-event simulation techniques(S.Juneja, P.Shahabuddin).
11. Quasi-random number techniques (C. Lemieux). 13. Analysis for design (W. Whitt).
12. Resampling methods (R.C.H. Cheng).
13. Correlation-based methods for output analysis (D. Goldsman, B.L. Nelson).
14. Simulation algorithms for regenerative processes (P.W. Glynn).
15. Selecting the best system (S.-H. Kim, B. L. Nelson).
16. Metamodel-based simulation optimization (R.R. Barton, M. Meckesheimer).
17. Gradient estimation (M.C. Fu).
18. An overview of simulation optimization via random search (S.Andratdóttir).
19. Metaheuristics (S. Ólafsson).

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

computer scientists, financial analysts, industrial engineers, management scientists, operations researchers