内容简介：
Discrete event systems (DES) have become pervasive in our daily lives. 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 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.
英文目录：
Part I： Perturbation Analysis
Chapter 1. The IPA Calculus for Hybrid Systems
1.1. Introduction
1.2. Perturbation Analysis of Hybrid Systems
1.2.1. Infinitesimal Perturbation Analysis (IPA):
TheIPAcalculu
1.3. IPA Properties
1.4. General Scheme for Abstracting DES to SFM
1.5. Conclusions and Future Work
References
Chapter 2. Smoothed Perturbation Analysis: A Retrospective and Prospective Look
2.1. Introduction
2.2. Brief History of SPA
2.3. Another Example
2.4. Overview of a General SPA Framework
2.5. Applications
2.5.1. Queueing
2.5.2. Inventory
2.5.3. Finance
2.5.4. Stochastic Activity Networks (SANs)
2.5.5. Other
2.6. Random Retrospective and Prospective Concluding Remarks
Acknowledgements
References
Chapter 3. Perturbation Analysis and Variance Reduction in Monte Carlo Simulation
3.1. Introduction
3.2. Systematic and Generic Control Variate Selection
3.2.1. Control variate technique: a brief review
3.2.2. Parametrized estimation problems
3.2.3. Deterministic function approximation and generic CV selection
3.3. Control Variates for Sensitivity Estimation
3.3.1. A parameterized estimation formulation of sensitivity estimation
3.3.2. Finite difference based controls
3.3.3. Illustrating example
3.4. Database Monte Carlo (DBMC) Implementation
3.5. Conclusions
Acknowledgements
References
Chapter 4. Adjoints and Averaging
4.1. Introduction
4.2. Adjoints: Classical Setting
4.3. Adjoints: Waiting Times
4.4. Adjoints: Vector Recursions
4.5. Averaging
4.6. Concluding Remarks
References
Chapter 5. Infinitesimal Perturbation Analysis and Optimization Algorithms
5.1. Preliminary Remarks
5.2. Motivation
5.3. Single-server Queues
5.3.1. Controlled single-server queue
5.3.2. Infinitesimal perturbation analysis
5.3.3. Optimization algorithm
5.4. Convergence
5.4.1. Stochastic approximation convergence theorem
5.4.2. Updating after every busy period
5.4.3. Updating after every service time
5.4.4. Example
5.5. Final Remarks
References
Chapter 6. Simulation-based Optimization of Failure-prone Continuous Flow Lines
6.1. Introduction
6.2. Two-machine Continuous Flow Lines
6.3. Gradient Estimation of a Two-machine Line
6.4. Modeling Assembly/Disassembly Networks Subject to TDF Failures with Stochastic Fluid Event Graphs
6.5. Evolution Equations and Sample Path Gradients
6.6. Optimization of Stochastic Fluid Event Graphs
6.7. Conclusion
References
Chapter 7. Perturbation Analysis, Dynamic Programming, and Beyond
7.1. Introduction
7.2. Perturbation Analysis of Queueing Systems Based on Perturbation Realization Factors
7.2.1. Performance gradient
7.2.2. Policy iteration
7.3. Performance Optimization of Markov Systems Based on Performance Potentials
7.3.1. Performance gradients and potentials
7.3.2. Policy iteration and HJB equation
7.4. Beyond Dynamic Programming
7.4.1. New results based on direct comparison
7.4.1.1. N-bias optimality in MDP
7.4.1.2. Optimization of sample-path Variance in MDP
7.4.2. Event-based optimization
7.4.3. Financial engineering related
Acknowledgments
References
Part II： Ordinal Optimization
Chapter 8. Fundamentals of Ordinal Optimization
8.1. Two Basic Ideas
8.2. The Exponential Convergence of Order and Goal Softening
8.3. Universal Alignment Probabilities
8.4. Extensions
8.4.1. Comparison of selection rules
8.4.2. Vector ordinal optimization
8.4.3. Constrained ordinal optimization
8.4.4. Deterministic complex optimization problem
8.4.5. OO ruler: quantification of heuristic designs
8.5. Conclusion
Reference
Chapter 9. Optimal Computing Budget Allocation Framework
9.1. Introduction
9.2. History of OCBA
9.3. Basics of OCBA
9.3.1. Problem formulation
9.3.2. Common assumptions
9.3.3. Ideas for deriving the simulation budget allocation
9.3.4. Closed-form allocation rules
9.3.5. Intuitive explanations of the allocation rules
9.3.6. Sequential heuristic algorithm
9.4. Different Extensions of OCBA
9.4.1. Selection qualities other than PCS
9.4.2. Other extensions to OCBA with single objective
9.4.3. OCBA for multiple performance measures
9.4.4. Integration of OCBA and the searching algorithms
9.5. Generalized OCBA Framework
9.6. Applications of OCBA
9.7. Future Research
9.8. Concluding Remarks
References
Chapter 10. Nested Partitions
10.1. Overview
10.2. Nested Partitions for Deterministic Optimization
10.2.1. Nested partitions framework
10.2.2. Global convergence
10.3. Enhancements and Advanced Developments
10.3.1. LP solution-based sampling
10.3.2. Extreme value-based promising index
10.3.3. Hybrid algorithms
10.3.3.1. Product design
10.3.3.2. Local pickup and delivery
10.4. Nested Partitions for Stochastic Optimization
10.4.1. Nested partitions for stochastic optimization
10.4.2. Global convergence
10.5. Conclusions
Acknowledgement
References
Chapter 11. Applications of Ordinal Optimization
11.1. Scheduling Problem for Apparel Manufacturing
11.2. The Turbine Blade Manufacturing Process Optimization Problem
11.3. Performance Optimization for a Remanufacturing System
11.3.1. Application of constrained ordinal optimization
11.3.2. Application of vector ordinal optimization
11.4. Witsenhausen Problem
11.5. Other Application Researches
Acknowledgments
References
