LEADER 05401oam 2200565 450 001 9910779883803321 005 20190911112729.0 010 $a981-4513-01-6 035 $a(OCoLC)852150965 035 $a(MiFhGG)GVRL8RHC 035 $a(EXLCZ)992550000001096053 100 $a20130514h20132013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 00$aStochastic simulation optimization for discrete event systems $eperturbation analysis, ordinal optimization and beyond /$feditors, Chun-Hung Chen, George Mason University, USA, Qing-Shan Jia, Tsinghua University, China, Loo Hay Lee, National University of Singapore, Singapore 210 $aHackensack, NJ $cWorld Scientific$dc2013 210 1$aNew Jersey :$cWorld Scientific,$d[2013] 210 4$d?2013 215 $a1 online resource (xxviii, 245 pages) $cillustrations 225 0 $aGale eBooks 300 $aDescription based upon print version of record. 311 $a981-4513-00-8 311 $a1-299-71377-7 320 $aIncludes bibliographical references. 327 $aPreface; 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; References 327 $aChapter 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 Selection 327 $a3.2.1. 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 Recursions 327 $a4.5. 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 Lines 327 $a6.1. 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 iteration 327 $a7.3. Performance Optimization of Markov Systems Based on Performance Potentials 330 $aDiscrete 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 f 606 $aDiscrete-time systems$xMathematical models 606 $aPerturbation (Mathematics) 606 $aSystems engineering$xComputer simulaton 615 0$aDiscrete-time systems$xMathematical models. 615 0$aPerturbation (Mathematics) 615 0$aSystems engineering$xComputer simulaton. 676 $a003/.83 702 $aChen$b Chun-Hung$f1964- 702 $aJia$b Qing-Shan$f1980- 702 $aLee$b Loo Hay 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910779883803321 996 $aStochastic simulation optimization for discrete event systems$93870987 997 $aUNINA