Stochastic simulation optimization for discrete event systems : perturbation analysis, ordinal optimization and beyond

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Title:

Publication Information:

New Jersey : World Scientific, 2013.

Physical Description:

xxviii, 245 pages ; 24 cm

ISBN:

9789814513005

Abstract:

"Discrete event systems (DES) have become pervasive in our daily life. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling of these stochastic simulations has long been a "hard nut to crack". The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y.C. Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions. Contents: Part I: Perturbation Analysis: IPA Calculus for Hybrid Systems; Smoothed Perturbation Analysis: A Retrospective and Prospective Look; Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; Adjoints and Averaging; Infinitesimal Perturbation Analysis in On-Line Optimization; Simulation-based Optimization of Failure-Prone Continuous Flow Lines; Perturbation Analysis, Dynamic Programming, and Beyond; Part II: Ordinal Optimization : Fundamentals of Ordinal Optimization; Optimal Computing Budget Allocation; Nested Partitions; Applications of Ordinal Optimization. Readership: Professionals in industrial and systems engineering, graduate reference for probability & statistics, stochastic analysis and general computer science, and research."-- Provided by publisher.

Subject Term:

Discrete-time systems -- Mathematical models.

Perturbation (Mathematics)

Systems engineering -- Computer simulation

Added Author:

Chen, Chun-Hung

Jia, Qing-Shan

Lee, Loo Hay

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Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010321111	TA343 S76 2013	Open Access Book	Book	Searching... Unknown

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Summary

REVIEW VOLUME

Preface	p. v
Foreword: A Tribute to a Great Leader in Perturbation Analysis and Ordinal Optimization	p. ix
Foreword: The Being and Becoming of Perturbation Analysis	p. xv
Foreword: Remembrance of Things Past	p. xxi
Part I Perturbation Analysis	p. 1
Chapter 1 The IPA Calculus for Hybrid Systems	p. 3
1.1 Introduction	p. 4
1.2 Perturbation Analysis of Hybrid Systems	p. 7
1.2.1 Infinitesimal Perturbation Analysis (IPA): The IPA calculus	p. 10
1.3 IPA Properties	p. 14
1.4 General Scheme for Abstracting DES to SFM	p. 18
1.5 Conclusions and Future Work	p. 21
References	p. 22
Chapter 2 Smoothed Perturbation Analysis: A Retrospective and Prospective Look	p. 25
2.1 Introduction	p. 25
2.2 Brief History of SPA	p. 28
2.3 Another Example	p. 29
2.4 Overview of a General SPA framework	p. 30
2.5 Applications	p. 33
2.5.1 Queueing	p. 33
2.5.2 Inventory	p. 34
2.5.3 Finance	p. 34
2.5.4 Stochastic Activity Networks (SANs)	p. 36
2.5.5 Others	p. 38
2.6 Random Retrospective and Prospective Concluding Remarks	p. 39
Acknowledgements	p. 41
References	p. 41
Chapter 3 Perturbation Analysis and Variance Reduction in Monte Carlo Simulation	p. 45
3.1 Introduction	p. 45
3.2 Systematic and Generic Control Variate Selection	p. 47
3.2.1 Control variate technique: a brief review	p. 47
3.2.2 Parametrized estimation problems	p. 49
3.2.3 Deterministic function approximation and generic CV selection	p. 50
3.3 Control Variates for Sensitivity Estimation	p. 54
3.3.1 A parameterized estimation formulation of sensitivity estimation	p. 54
3.3.2 Finite difference based controls	p. 56
3.3.3 Illustrating example	p. 57
3.4 Database Monte Carlo (DBMC) Implementation	p. 59
3.5 Conclusions	p. 60
Acknowledgements	p. 61
References	p. 61
Chapter 4 Adjoints and Averaging	p. 63
4.1 Introduction	p. 63
4.2 Adjoints: Classical Setting	p. 64
4.3 Adjoints: Waiting Times	p. 64
4.4 Adjoints: Vector Recursions	p. 67
4.5 Averaging	p. 69
4.6 Concluding Remarks	p. 72
References	p. 73
Chapter 5 infinitesimal Perturbation Analysis and Optimization Algorithms	p. 75
5.1 Preliminary Remarks	p. 75
5.2 Motivation	p. 76
5.3 Single-server Queues	p. 77
5.3.1 Controlled single-server queue	p. 77
5.3.2 Infinitesimal perturbation analysis	p. 79
5.3.3 Optimization algorithm	p. 83
5.4 Convergence	p. 85
5.4.1 Stochastic approximation convergence theorem	p. 85
5.4.2 Updating after every busy period	p. 86
5.4.3 Updating after every service time	p. 88
5.4.4 Example	p. 92
5.5 Final Remarks	p. 92
References	p. 93
Chapter 6 Simulation-based Optimization of Failure-prone Continuous Flow Lines	p. 97
6.1 Introduction	p. 97
6.2 Two-machine Continuous Flow Lines	p. 100
6.3 Gradient Estimation of a Two-machine Line	p. 104
6.4 Modeling Assembly/Disassembly Networks Subject to TDF Failures with Stochastic Fluid Event Graphs	p. 108
6.5 Evolution Equations and Sample Path Gradients	p. 115
6.6 Optimization of Stochastic Fluid Event Graphs	p. 119
6.7 Conclusion	p. 122
References	p. 123
Chapter 7 Perturbation Analysis, Dynamic Programming, and Beyond	p. 127
7.1 Introduction	p. 128
7.2 Perturbation Analysis of Queueing Systems Based on Perturbation Realization Factors	p. 131
7.2.1 Performance gradient	p. 131
7.2.2 Policy iteration	p. 135
7.3 Performance Optimization of Markov Systems Based on Performance Potentials	p. 137
7.3.1 Performance gradients and potentials	p. 137
7.3.2 Policy iteration and HJB equation	p. 141
7.4 Beyond Dynamic Programming	p. 142
7.4.1 New results based on direct comparison	p. 143
7.4.1.1 N-bias optimality in MDP	p. 143
7.4.1.2 Optimization of sample-path variance in MDP	p. 145
7.4.2 Event-based optimization	p. 147
7.4.3 Financial engineering related	p. 151
Acknowledgments	p. 153
References	p. 153
Part II Ordinal Optimization	p. 157
Chapter 8 Fundamentals of Ordinal Optimization	p. 159
8.1 Two Basic Ideas	p. 159
8.2 The Exponential Convergence of Order and Goal Softening	p. 160
8.3 Universal Alignment Probabilities	p. 163
8.4 Extensions	p. 164
8.4.1 Comparison of selection rules	p. 165
8.4.2 Vector ordinal optimization	p. 166
8.4.3 Constrained ordinal optimization	p. 168
8.4.4 Deterministic complex optimization problem	p. 169
8.4.5 00 ruler: quantification of heuristic designs	p. 170
8.5 Conclusion	p. 172
References	p. 173
Chapter 9 Optimal Computing Budget Allocation Framework	p. 175
9.1 Introduction	p. 176
9.2 History of OCBA	p. 177
9.3 Basics of OCBA	p. 180
9.3.1 Problem formulation	p. 180
9.3.2 Common assumptions	p. 182
9.3.3 Ideas for deriving the simulation budget allocation	p. 183
9.3.4 Closed-form allocation rules	p. 185
9.3.5 Intuitive explanations of the allocation rules	p. 185
9.3.6 Sequential heuristic algorithm	p. 186
9.4 Different Extensions of OCBA	p. 188
9.4.1 Selection qualities other than PCS	p. 188
9.4.2 Other extensions to OCBA with single objective	p. 188
9.4.3 OCBA for multiple performance measures	p. 189
9.4.4 Integration of OCBA and the searching algorithms	p. 190
9.5 Generalized OCBA framework	p. 191
9.6 Applications of OCBA	p. 192
9.7 Future Research	p. 193
9.8 Concluding Remarks	p. 193
References	p. 194
Chapter 10 Nested Partitions	p. 203
10.1 Overview	p. 203
10.2 Nested Partitions for Deterministic Optimization	p. 206
10.2.1 Nested partitions framework	p. 207
10.2.2 Global convergence	p. 209
10.3 Enhancements and Advanced Developments	p. 212
10.3.1 LP solution-based sampling	p. 212
10.3.2 Extreme value-based promising index	p. 213
10.3.3 Hybrid algorithms	p. 216
10.3.3.1 Product design	p. 217
10.3.3.2 Local pickup and delivery	p. 218
10.4 Nested Partitions for Stochastic Optimization	p. 218
10.4.1 Nested partitions for stochastic optimization	p. 219
10.4.2 Global convergence	p. 221
10.5 Conclusions	p. 222
Acknowledgements	p. 223
References	p. 223
Chapter 11 Applications of Ordinal Optimization	p. 227
11.1 Scheduling Problem for Apparel Manufacturing	p. 228
11.2 The Turbine Blade Manufacturing Process Optimization Problem	p. 232
11.3 Performance Optimization for a Remanufacturing System	p. 235
11.3.1 Application of constrained ordinal optimization	p. 235
11.3.2 Application of vector ordinal optimization	p. 238
11.4 Witsenhausen Problem	p. 239
11.5 Other Application Researches	p. 243
Acknowledgments	p. 243
References	p. 243

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