LEADER 05704nam 2200697Ia 450 001 9910452310203321 005 20200520144314.0 010 $a981-4513-01-6 035 $a(CKB)2550000001096053 035 $a(EBL)1275568 035 $a(OCoLC)853362791 035 $a(SSID)ssj0000917130 035 $a(PQKBManifestationID)12373706 035 $a(PQKBTitleCode)TC0000917130 035 $a(PQKBWorkID)10891742 035 $a(PQKB)10755741 035 $a(MiAaPQ)EBC1275568 035 $a(WSP)00008827 035 $a(Au-PeEL)EBL1275568 035 $a(CaPaEBR)ebr10731547 035 $a(CaONFJC)MIL502628 035 $a(OCoLC)860387930 035 $a(EXLCZ)992550000001096053 100 $a20130514d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aStochastic simulation optimization for discrete event systems$b[electronic resource] $eperturbation analysis, ordinal optimization, and beyond /$feditors, Chun-Hung Chen, Qing-Shan Jia, Loo Hay Lee 210 $aHackensack, NJ $cWorld Scientific$dc2013 215 $a1 online resource (274 p.) 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 simulation 608 $aElectronic books. 615 0$aDiscrete-time systems$xMathematical models. 615 0$aPerturbation (Mathematics) 615 0$aSystems engineering$xComputer simulation. 676 $a003/.83 701 $aChen$b Chun-Hung$f1964-$0970958 701 $aJia$b Qing-Shan$f1980-$0770921 701 $aLee$b Loo Hay$0889759 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910452310203321 996 $aStochastic simulation optimization for discrete event systems$92206883 997 $aUNINA