Handbooks in Operations Research and Management Science: Simulation

Author:   Shane G. Henderson (School of Operations and Industrial Engineering, Cornell University, Ithaca, NY, USA) ,  Barry L. Nelson (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA)
Publisher:   Elsevier Science & Technology
Edition:   13th edition
Volume:   v. 13
ISBN:  

9780444514288


Pages:   692
Publication Date:   02 September 2006
Format:   Hardback
Availability:   In Print   Availability explained
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

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Handbooks in Operations Research and Management Science: Simulation


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Overview

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

Full Product Details

Author:   Shane G. Henderson (School of Operations and Industrial Engineering, Cornell University, Ithaca, NY, USA) ,  Barry L. Nelson (Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL, USA)
Publisher:   Elsevier Science & Technology
Imprint:   North-Holland
Edition:   13th edition
Volume:   v. 13
Dimensions:   Width: 16.50cm , Height: 4.60cm , Length: 24.00cm
Weight:   1.410kg
ISBN:  

9780444514288


ISBN 10:   0444514287
Pages:   692
Publication Date:   02 September 2006
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Out of Print
Availability:   In Print   Availability explained
Limited stock is available. It will be ordered for you and shipped pending supplier's limited stock.

Table of 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.Andratdóttir). 21. Metaheuristics (S. Ólafsson).

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