Contents -- Preface -- Foreword: A Tribute to a Great Leader in Perturbation Analysis and Ordinal Optimization -- Foreword: The Being and Becoming of Perturbation Analysis -- Foreword: Remembrance of Things Past -- 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): The IPA calculus -- 1.3. IPA Properties -- 1.4. General Scheme for Abstracting DES to SFM -- 1.5. Conclusions and FutureWork -- 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. Others -- 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 inMDP -- 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 -- References -- 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 -- Acknowledgements -- 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.