Stochastic Simulation Optimization: An Optimal Computing Budget Allocation

by Chen, Chun-hung; Lee, Loo Hay

• ISBN13: 9789814282642
• ISBN10: 9814282642
• Format: Hardcover
• Copyright: 2010-08-31
• Publisher: World Scientific Pub Co Inc

Summary

With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive. Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation.

Table of Contents

Foreword	p. v
Preface	p. vii
Acknowledgments	p. xi
Introduction to Stochastic Simulation Optimization	p. 1
Introduction	p. 1
Problem Definition	p. 7
Classification	p. 10
Summary	p. 13
Computing Budget Allocation	p. 15
Simulation Precision versus Computing Budget	p. 15
Computing Budget Allocation for Comparison of Multiple Designs	p. 17
Intuitive Explanations of Optimal Computing Budget Allocation	p. 19
Computing Budget Allocation for Large Simulation Optimization	p. 26
Roadmap	p. 28
Selecting the Best from a Set of Alternative Designs	p. 29
A Bayesian Framework for Simulation Output Modeling	p. 30
Probability of Correct, Selection	p. 36
Maximizing the Probability of Correct Selection	p. 40
Minimizing the Total Simulation Cost	p. 51
Non-Equal Simulation Costs	p. 55
Minimizing Opportunity Cost	p. 57
OCBA Derivation Based on Classical Model	p. 64
Numerical Implementation and Experiments	p. 69
Numerical Testing	p. 69
Parameter Setting and Implementation of the OCBA Procedure	p. 88
Selecting An Optimal Subset	p. 93
Introduction and Problem Statement	p. 94
Approximate Asymptotically Optimal Allocation Scheme	p. 96
Numerical Experiments	p. 106
Multi-objective Optimal Computing Budget Allocation	p. 117
Pareto Optimality	p. 118
Multi-objective Optimal Computing Budget Allocation Problem	p. 120
Asymptotic Allocation Rule	p. 128
A Sequential Allocation Procedure	p. 132
Numerical Results	p. 133
Large-Scale Simulation and Optimization	p. 141
A General Framework of Integration of OCBA with Metaheuristics	p. 144
Problems with Single Objective	p. 147
Numerical Experiments	p. 152
Multiple Objectives	p. 156
Concluding Remarks	p. 159
Generalized OCBA Framework and Other Related Methods	p. 161
Optimal Computing Budget Allocation for Selecting the Best by Utilizing Regression Analysis (OCBA-OSD)	p. 164
Optimal Computing Budget Allocation for Extended Cross-Entropy Method (OCBA-CE)	p. 167
Optimal Computing Budget Allocation for Variance Reduction in Rare-event Simulation	p. 169
Optimal Data Collection Budget Allocation (ODCBA) for Monte Carlo DEA	p. 171
Other Related Works	p. 173
Fundamentals of Simulation	p. 175
What is Simulation?	p. 175
Steps in Developing a Simulation Model	p. 176
Concepts in Simulation Model Building	p. 178
Input Data Modeling	p. 181
Random Number and Variables Generation	p. 183
Output Analysis	p. 188
Verification and Validation	p. 192
Basic Probability and Statistics	p. 195
Probability Distribution	p. 195
Some Important Statistical Laws	p. 199
Goodness of Fit Test	p. 199
Some Proofs in Chapter 6	p. 201
Proof of Lemma 6.1	p. 201
Proof of Lemma 6.2	p. 204
Proof of Lemma 6.3	p. 204
Proof of Lemma 6.5 (Asymptotic Allocation Rules)	p. 206
Some OCBA Source Codes	p. 213
References	p. 219
Index	p. 225
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