05401oam 2200565 450 991077988380332120190911112729.0981-4513-01-6(OCoLC)852150965(MiFhGG)GVRL8RHC(EXLCZ)99255000000109605320130514h20132013 uy 0engurun|---uuuuatxtccrStochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond /editors, Chun-Hung Chen, George Mason University, USA, Qing-Shan Jia, Tsinghua University, China, Loo Hay Lee, National University of Singapore, SingaporeHackensack, NJ World Scientificc2013New Jersey :World Scientific,[2013]�20131 online resource (xxviii, 245 pages) illustrationsGale eBooksDescription based upon print version of record.981-4513-00-8 1-299-71377-7 Includes bibliographical references.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; Contents; 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; ReferencesChapter 2. Smoothed Perturbation Analysis: A Retrospective and Prospective Look2.1. 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 Selection3.2.1. Control variate technique: a brief review3.2.2. 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 Recursions4.5. Averaging4.6. 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 Lines6.1. Introduction6.2. 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 iteration7.3. Performance Optimization of Markov Systems Based on Performance PotentialsDiscrete 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 fDiscrete-time systemsMathematical modelsPerturbation (Mathematics)Systems engineeringComputer simulatonDiscrete-time systemsMathematical models.Perturbation (Mathematics)Systems engineeringComputer simulaton.003/.83Chen Chun-Hung1964-Jia Qing-Shan1980-Lee Loo HayMiFhGGMiFhGGBOOK9910779883803321Stochastic simulation optimization for discrete event systems3870987UNINA