Stochastic simulation optimization for discrete event systems: perturbation analysis, ordinal optimization and beyond
"Discrete event systems (DES) have become pervasive in our daily life. 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 billio...
Gespeichert in:
| Weitere beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New Jersey
World Scientific
2013
|
| Schlagwörter: | |
| Zusammenfassung: | "Discrete event systems (DES) have become pervasive in our daily life. 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 of 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 for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y.C. Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions. Contents: Part I: Perturbation Analysis: IPA Calculus for Hybrid Systems; Smoothed Perturbation Analysis: A Retrospective and Prospective Look; Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; Adjoints and Averaging; Infinitesimal Perturbation Analysis in On-Line Optimization; Simulation-based Optimization of Failure-Prone Continuous Flow Lines; Perturbation Analysis, Dynamic Programming, and Beyond; Part II: Ordinal Optimization : Fundamentals of Ordinal Optimization; Optimal Computing Budget Allocation; Nested Partitions; Applications of Ordinal Optimization. Readership: Professionals in industrial and systems engineering, graduate reference for probability & statistics, stochastic analysis and general computer science, and research."... |
| Beschreibung: | Includes bibliographical references |
| Umfang: | xxviii, 245 pages 24 cm |
| ISBN: | 9789814513005 |
Internformat
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| 245 | 1 | 0 | |a Stochastic simulation optimization for discrete event systems |b perturbation analysis, ordinal optimization and beyond |c edited by Chun-Hung Chen, George Mason University, USA and National Taiwan University, Taiwan ; Qing-Shan Jia, Tsinghua University, China ; Loo Hay Lee, National University of Singapore, Singapore |
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| 520 | |a "Discrete event systems (DES) have become pervasive in our daily life. 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 of 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 for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y.C. Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions. Contents: Part I: Perturbation Analysis: IPA Calculus for Hybrid Systems; Smoothed Perturbation Analysis: A Retrospective and Prospective Look; Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; Adjoints and Averaging; Infinitesimal Perturbation Analysis in On-Line Optimization; Simulation-based Optimization of Failure-Prone Continuous Flow Lines; Perturbation Analysis, Dynamic Programming, and Beyond; Part II: Ordinal Optimization : Fundamentals of Ordinal Optimization; Optimal Computing Budget Allocation; Nested Partitions; Applications of Ordinal Optimization. Readership: Professionals in industrial and systems engineering, graduate reference for probability & statistics, stochastic analysis and general computer science, and research."... | ||
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| id | DE-604.BV044045793 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:52:12Z |
| institution | BVB |
| isbn | 9789814513005 |
| language | English |
| lccn | 013012700 |
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| physical | xxviii, 245 pages 24 cm |
| publishDate | 2013 |
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| publisher | World Scientific |
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| spelling | Stochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond edited by Chun-Hung Chen, George Mason University, USA and National Taiwan University, Taiwan ; Qing-Shan Jia, Tsinghua University, China ; Loo Hay Lee, National University of Singapore, Singapore New Jersey World Scientific 2013 xxviii, 245 pages 24 cm txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references "Discrete event systems (DES) have become pervasive in our daily life. 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 of 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 for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y.C. Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions. Contents: Part I: Perturbation Analysis: IPA Calculus for Hybrid Systems; Smoothed Perturbation Analysis: A Retrospective and Prospective Look; Perturbation Analysis and Variance Reduction in Monte Carlo Simulation; Adjoints and Averaging; Infinitesimal Perturbation Analysis in On-Line Optimization; Simulation-based Optimization of Failure-Prone Continuous Flow Lines; Perturbation Analysis, Dynamic Programming, and Beyond; Part II: Ordinal Optimization : Fundamentals of Ordinal Optimization; Optimal Computing Budget Allocation; Nested Partitions; Applications of Ordinal Optimization. Readership: Professionals in industrial and systems engineering, graduate reference for probability & statistics, stochastic analysis and general computer science, and research."... Mathematisches Modell Discrete-time systems Mathematical models Perturbation (Mathematics) Systems engineering Computer simulaton Stochastischer Prozess (DE-588)4057630-9 gnd rswk-swf Simulation (DE-588)4055072-2 gnd rswk-swf Diskretes Ereignissystem (DE-588)4196828-1 gnd rswk-swf Stochastischer Prozess (DE-588)4057630-9 s Simulation (DE-588)4055072-2 s Diskretes Ereignissystem (DE-588)4196828-1 s DE-604 Chen, Chun-Hung edt Jia, Qing-Shan edt Lee, Loo Hay 1969-2022 (DE-588)138030529 edt Erscheint auch als Online-Ausgabe 978-981-4513-01-2 |
| spellingShingle | Stochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond Mathematisches Modell Discrete-time systems Mathematical models Perturbation (Mathematics) Systems engineering Computer simulaton Stochastischer Prozess (DE-588)4057630-9 gnd Simulation (DE-588)4055072-2 gnd Diskretes Ereignissystem (DE-588)4196828-1 gnd |
| subject_GND | (DE-588)4057630-9 (DE-588)4055072-2 (DE-588)4196828-1 |
| title | Stochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond |
| title_auth | Stochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond |
| title_exact_search | Stochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond |
| title_full | Stochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond edited by Chun-Hung Chen, George Mason University, USA and National Taiwan University, Taiwan ; Qing-Shan Jia, Tsinghua University, China ; Loo Hay Lee, National University of Singapore, Singapore |
| title_fullStr | Stochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond edited by Chun-Hung Chen, George Mason University, USA and National Taiwan University, Taiwan ; Qing-Shan Jia, Tsinghua University, China ; Loo Hay Lee, National University of Singapore, Singapore |
| title_full_unstemmed | Stochastic simulation optimization for discrete event systems perturbation analysis, ordinal optimization and beyond edited by Chun-Hung Chen, George Mason University, USA and National Taiwan University, Taiwan ; Qing-Shan Jia, Tsinghua University, China ; Loo Hay Lee, National University of Singapore, Singapore |
| title_short | Stochastic simulation optimization for discrete event systems |
| title_sort | stochastic simulation optimization for discrete event systems perturbation analysis ordinal optimization and beyond |
| title_sub | perturbation analysis, ordinal optimization and beyond |
| topic | Mathematisches Modell Discrete-time systems Mathematical models Perturbation (Mathematics) Systems engineering Computer simulaton Stochastischer Prozess (DE-588)4057630-9 gnd Simulation (DE-588)4055072-2 gnd Diskretes Ereignissystem (DE-588)4196828-1 gnd |
| topic_facet | Mathematisches Modell Discrete-time systems Mathematical models Perturbation (Mathematics) Systems engineering Computer simulaton Stochastischer Prozess Simulation Diskretes Ereignissystem |
| work_keys_str_mv | AT chenchunhung stochasticsimulationoptimizationfordiscreteeventsystemsperturbationanalysisordinaloptimizationandbeyond AT jiaqingshan stochasticsimulationoptimizationfordiscreteeventsystemsperturbationanalysisordinaloptimizationandbeyond AT leeloohay stochasticsimulationoptimizationfordiscreteeventsystemsperturbationanalysisordinaloptimizationandbeyond |