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Stochastic Simulation Optimization For Discrete Event Systems: Perturbation Analysis, Ordinal Optimization And Beyond [Kõva köide]

Edited by (Geroge Mason Univ, Usa & National Taiwan Univ, Taiwan), Edited by (Nus, S'pore), Edited by (Tsinghua Univ, China)
  • Formaat: Hardback, 276 pages
  • Ilmumisaeg: 28-Aug-2013
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814513008
  • ISBN-13: 9789814513005
Teised raamatud teemal:
Stochastic Simulation Optimization For Discrete Event Systems: Perturbation Analysis, Ordinal Optimization And BeyondSuurem pilt
  • Formaat: Hardback, 276 pages
  • Ilmumisaeg: 28-Aug-2013
  • Kirjastus: World Scientific Publishing Co Pte Ltd
  • ISBN-10: 9814513008
  • ISBN-13: 9789814513005
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9789814513005.html
Märksõnad:
"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."--

Preface v
Foreword: A Tribute to a Great Leader in Perturbation Analysis and Ordinal Optimization ix
Foreword: The Being and Becoming of Perturbation Analysis xv
Foreword: Remembrance of Things Past xxi
Part I: Perturbation Analysis 1(156)
Chapter 1 The IPA Calculus for Hybrid Systems
3(22)
1.1 Introduction
4(3)
1.2 Perturbation Analysis of Hybrid Systems
7(7)
1.2.1 Infinitesimal Perturbation Analysis (IPA): The IPA calculus
10(4)
1.3 IPA Properties
14(4)
1.4 General Scheme for Abstracting DES to SFM
18(3)
1.5 Conclusions and Future Work
21(1)
References
22(3)
Chapter 2 Smoothed Perturbation Analysis: A Retrospective and Prospective Look
25(20)
2.1 Introduction
25(3)
2.2 Brief History of SPA
28(1)
2.3 Another Example
29(1)
2.4 Overview of a General SPA Framework
30(3)
2.5 Applications
33(6)
2.5.1 Queueing
33(1)
2.5.2 Inventory
34(1)
2.5.3 Finance
34(2)
2.5.4 Stochastic Activity Networks (SANs)
36(2)
2.5.5 Others
38(1)
2.6 Random Retrospective and Prospective Concluding Remarks
39(2)
Acknowledgements
41(1)
References
41(4)
Chapter 3 Perturbation Analysis and Variance Reduction in Monte Carlo Simulation
45(18)
3.1 Introduction
45(2)
3.2 Systematic and Generic Control Variate Selection
47(7)
3.2.1 Control variate technique: a brief review
47(2)
3.2.2 Parametrized estimation problems
49(1)
3.2.3 Deterministic function approximation and generic CV selection
50(4)
3.3 Control Variates for Sensitivity Estimation
54(5)
3.3.1 A parameterized estimation formulation of sensitivity estimation
54(2)
3.3.2 Finite difference based controls
56(1)
3.3.3 Illustrating example
57(2)
3.4 Database Monte Carlo (DBMC) Implementation
59(1)
3.5 Conclusions
60(1)
Acknowledgements
61(1)
References
61(2)
Chapter 4 Adjoints and Averaging
63(12)
4.1 Introduction
63(1)
4.2 Adjoints: Classical Setting
64(1)
4.3 Adjoints: Waiting Times
64(3)
4.4 Adjoints: Vector Recursions
67(2)
4.5 Averaging
69(3)
4.6 Concluding Remarks
72(1)
References
73(2)
Chapter 5 Infinitesimal Perturbation Analysis and Optimization Algorithms
75(22)
5.1 Preliminary Remarks
75(1)
5.2 Motivation
76(1)
5.3 Single-server Queues
77(8)
5.3.1 Controlled single-server queue
77(2)
5.3.2 Infinitesimal perturbation analysis
79(4)
5.3.3 Optimization algorithm
83(2)
5.4 Convergence
85(7)
5.4.1 Stochastic approximation convergence theorem
85(1)
5.4.2 Updating after every busy period
86(2)
5.4.3 Updating after every service time
88(4)
5.4.4 Example
92(1)
5.5 Final Remarks
92(1)
References
93(4)
Chapter 6 Simulation-based Optimization of Failure-prone Continuous Flow Lines
97(30)
6.1 Introduction
97(3)
6.2 Two-machine Continuous Flow Lines
100(4)
6.3 Gradient Estimation of a Two-machine Line
104(4)
6.4 Modeling Assembly/Disassembly Networks Subject to TDF Failures with Stochastic Fluid Event Graphs
108(7)
6.5 Evolution Equations and Sample Path Gradients
115(4)
6.6 Optimization of Stochastic Fluid Event Graphs
119(3)
6.7 Conclusion
122(1)
References
123(4)
Chapter 7 Perturbation Analysis, Dynamic Programming, and Beyond
127(30)
7.1 Introduction
128(3)
7.2 Perturbation Analysis of Queueing Systems Based on Perturbation Realization Factors
131(6)
7.2.1 Performance gradient
131(4)
7.2.2 Policy iteration
135(2)
7.3 Performance Optimization of Markov Systems Based on Performance Potentials
137(5)
7.3.1 Performance gradients and potentials
137(4)
7.3.2 Policy iteration and HJB equation
141(1)
7.4 Beyond Dynamic Programming
142(11)
7.4.1 New results based on direct comparison
143(4)
7.4.1.1 N-bias optimality in MDP
143(2)
7.4.1.2 Optimization of sample-path variance in MDP
145(2)
7.4.2 Event-based optimization
147(4)
7.4.3 Financial engineering related
151(2)
Acknowledgments
153(1)
References
153(4)
Part II: Ordinal Optimization 157
Chapter 8 Fundamentals of Ordinal Optimization
159(16)
8.1 Two Basic Ideas
159(1)
8.2 The Exponential Convergence of Order and Goal Softening
160(3)
8.3 Universal Alignment Probabilities
163(1)
8.4 Extensions
164(8)
8.4.1 Comparison of selection rules
165(1)
8.4.2 Vector ordinal optimization
166(2)
8.4.3 Constrained ordinal optimization
168(1)
8.4.4 Deterministic complex optimization problem
169(1)
8.4.5 00 ruler: quantification of heuristic designs
170(2)
8.5 Conclusion
172(1)
References
173(2)
Chapter 9 Optimal Computing Budget Allocation Framework
175(28)
9.1 Introduction
176(1)
9.2 History of OCBA
177(3)
9.3 Basics of OCBA
180(8)
9.3.1 Problem formulation
180(2)
9.3.2 Common assumptions
182(1)
9.3.3 Ideas for deriving the simulation budget allocation
183(2)
9.3.4 Closed-form allocation rules
185(1)
9.3.5 Intuitive explanations of the allocation rules
185(1)
9.3.6 Sequential heuristic algorithm
186(2)
9.4 Different Extensions of OCBA
188(3)
9.4.1 Selection qualities other than PCS
188(1)
9.4.2 Other extensions to OCBA with single objective
188(1)
9.4.3 OCBA for multiple performance measures
189(1)
9.4.4 Integration of OCBA and the searching algorithms
190(1)
9.5 Generalized OCBA Framework
191(1)
9.6 Applications of OCBA
192(1)
9.7 Future Research
193(1)
9.8 Concluding Remarks
193(1)
References
194(9)
Chapter 10 Nested Partitions
203(24)
10.1 Overview
203(3)
10.2 Nested Partitions for Deterministic Optimization
206(6)
10.2.1 Nested partitions framework
207(2)
10.2.2 Global convergence
209(3)
10.3 Enhancements and Advanced Developments
212(6)
10.3.1 LP solution-based sampling
212(1)
10.3.2 Extreme value-based promising index
213(3)
10.3.3 Hybrid algorithms
216(2)
10.3.3.1 Product design
217(1)
10.3.3.2 Local pickup and delivery
218(1)
10.4 Nested Partitions for Stochastic Optimization
218(4)
10.4.1 Nested partitions for stochastic optimization
219(2)
10.4.2 Global convergence
221(1)
10.5 Conclusions
222(1)
Acknowledgements
223(1)
References
223(4)
Chapter 11 Applications of Ordinal Optimization
227
11.1 Scheduling Problem for Apparel Manufacturing
228(4)
11.2 The Turbine Blade Manufacturing Process Optimization Problem
232(3)
11.3 Performance Optimization for a Remanufacturing System
235(4)
11.3.1 Application of constrained ordinal optimization
235(3)
11.3.2 Application of vector ordinal optimization
238(1)
11.4 Witsenhausen Problem
239(4)
11.5 Other Application Researches
243(1)
Acknowledgments
243(1)
References
243