Stochastic Simulation Optimization for Discrete Event Systems Perturbation Analysis, Ordinal Optimization and Beyond

ISBN-10: 9814513008

ISBN-13: 9789814513005

Authors: Chun Hung Chen, Jia Qing-shan, Loo Hay Lee

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Binding: Hardcover Publisher: World Scientific Publishing Co Pte Ltd Number of pages: 274 Dimensions: 6.00" wide x 9.00" long x 0.75" tall Weight: 1.144 lbs. Language: English



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



Perturbation Analysis



The IPA Calculus for Hybrid Systems



Introduction



Perturbation Analysis of Hybrid Systems



Infinitesimal Perturbation Analysis (IPA): The IPA calculus



IPA Properties



General Scheme for Abstracting DES to SFM



Conclusions and Future Work


References



Smoothed Perturbation Analysis: A Retrospective and Prospective Look



Introduction



Brief History of SPA



Another Example



Overview of a General SPA framework



Applications



Queueing



Inventory



Finance



Stochastic Activity Networks (SANs)



Others



Random Retrospective and Prospective Concluding Remarks


Acknowledgements


References



Perturbation Analysis and Variance Reduction in Monte Carlo Simulation



Introduction



Systematic and Generic Control Variate Selection



Control variate technique: a brief review



Parametrized estimation problems



Deterministic function approximation and generic CV selection



Control Variates for Sensitivity Estimation



A parameterized estimation formulation of sensitivity estimation



Finite difference based controls



Illustrating example



Database Monte Carlo (DBMC) Implementation



Conclusions


Acknowledgements


References



Adjoints and Averaging



Introduction



Adjoints: Classical Setting



Adjoints: Waiting Times



Adjoints: Vector Recursions



Averaging



Concluding Remarks


References



infinitesimal Perturbation Analysis and Optimization Algorithms



Preliminary Remarks



Motivation



Single-server Queues



Controlled single-server queue



Infinitesimal perturbation analysis



Optimization algorithm



Convergence



Stochastic approximation convergence theorem



Updating after every busy period



Updating after every service time



Example



Final Remarks


References



Simulation-based Optimization of Failure-prone Continuous Flow Lines



Introduction



Two-machine Continuous Flow Lines



Gradient Estimation of a Two-machine Line



Modeling Assembly/Disassembly Networks Subject to TDF Failures with Stochastic Fluid Event Graphs



Evolution Equations and Sample Path Gradients



Optimization of Stochastic Fluid Event Graphs



Conclusion


References



Perturbation Analysis, Dynamic Programming, and Beyond



Introduction



Perturbation Analysis of Queueing Systems Based on Perturbation Realization Factors



Performance gradient



Policy iteration



Performance Optimization of Markov Systems Based on Performance Potentials



Performance gradients and potentials



Policy iteration and HJB equation



Beyond Dynamic Programming



New results based on direct comparison



N-bias optimality in MDP



Optimization of sample-path variance in MDP



Event-based optimization



Financial engineering related


Acknowledgments


References



Ordinal Optimization



Fundamentals of Ordinal Optimization



Two Basic Ideas



The Exponential Convergence of Order and Goal Softening



Universal Alignment Probabilities



Extensions



Comparison of selection rules



Vector ordinal optimization



Constrained ordinal optimization



Deterministic complex optimization problem



00 ruler: quantification of heuristic designs



Conclusion


References



Optimal Computing Budget Allocation Framework



Introduction



History of OCBA



Basics of OCBA



Problem formulation



Common assumptions



Ideas for deriving the simulation budget allocation



Closed-form allocation rules



Intuitive explanations of the allocation rules



Sequential heuristic algorithm



Different Extensions of OCBA



Selection qualities other than PCS



Other extensions to OCBA with single objective



OCBA for multiple performance measures



Integration of OCBA and the searching algorithms



Generalized OCBA framework



Applications of OCBA



Future Research



Concluding Remarks


References



Nested Partitions



Overview



Nested Partitions for Deterministic Optimization



Nested partitions framework



Global convergence



Enhancements and Advanced Developments



LP solution-based sampling



Extreme value-based promising index



Hybrid algorithms



Product design



Local pickup and delivery



Nested Partitions for Stochastic Optimization



Nested partitions for stochastic optimization



Global convergence



Conclusions


Acknowledgements


References



Applications of Ordinal Optimization



Scheduling Problem for Apparel Manufacturing



The Turbine Blade Manufacturing Process Optimization Problem



Performance Optimization for a Remanufacturing System



Application of constrained ordinal optimization



Application of vector ordinal optimization



Witsenhausen Problem



Other Application Researches


Acknowledgments


References

Stochastic Simulation Optimization for Discrete Event Systems Perturbation Analysis, Ordinal Optimization and Beyond

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