Stochastic Simulation Optimization for Discrete Event Systems: Perturbation Analysis, Ordinal Optimization, and Beyond
Saved in:
| Other Authors: | , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
[Hackensack] New Jersey
World Scientific
2013
|
| Subjects: | |
| Item Description: | Print version record |
| Physical Description: | 1 online resource |
| ISBN: | 9789814513012 9814513016 |
Staff View
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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), Qing-Shan Jia (Tsinghua University, China) & Loo Hay Lee (National University of Singapore, Singapore) |
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| 505 | 8 | |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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Record in the Search Index
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| author2 | Chen, Chun-Hung 1964- Jia, Qing-Shan 1980- Lee, Loo Hay |
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| contents | "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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| discipline | Informatik Mathematik Wirtschaftswissenschaften |
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| indexdate | 2024-07-10T08:15:32Z |
| institution | BVB |
| isbn | 9789814513012 9814513016 |
| language | English |
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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), Qing-Shan Jia (Tsinghua University, China) & Loo Hay Lee (National University of Singapore, Singapore) [Hackensack] New Jersey World Scientific 2013 1 online resource txt rdacontent c rdamedia cr rdacarrier Print version record "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."-- SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Discrete-time systems / Mathematical models fast Perturbation (Mathematics) fast Discrete-time systems Mathematical models Perturbation (Mathematics) Systems engineering Computer simulaton Diskretes Ereignissystem (DE-588)4196828-1 gnd rswk-swf Stochastischer Prozess (DE-588)4057630-9 gnd rswk-swf Simulation (DE-588)4055072-2 gnd rswk-swf Stochastischer Prozess (DE-588)4057630-9 s Simulation (DE-588)4055072-2 s Diskretes Ereignissystem (DE-588)4196828-1 s 1\p DE-604 Chen, Chun-Hung 1964- edt Jia, Qing-Shan 1980- edt Lee, Loo Hay edt Erscheint auch als Druck-Ausgabe Stochastic simulation optimization for discrete event systems [Hackensack] New Jersey : World Scientific, 2013 9789814513005 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | 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 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."-- SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Discrete-time systems / Mathematical models fast Perturbation (Mathematics) fast Discrete-time systems Mathematical models Perturbation (Mathematics) Systems engineering Computer simulaton Diskretes Ereignissystem (DE-588)4196828-1 gnd Stochastischer Prozess (DE-588)4057630-9 gnd Simulation (DE-588)4055072-2 gnd |
| subject_GND | (DE-588)4196828-1 (DE-588)4057630-9 (DE-588)4055072-2 |
| 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), 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), 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), 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 | SCIENCE / System Theory bisacsh TECHNOLOGY & ENGINEERING / Operations Research bisacsh Discrete-time systems / Mathematical models fast Perturbation (Mathematics) fast Discrete-time systems Mathematical models Perturbation (Mathematics) Systems engineering Computer simulaton Diskretes Ereignissystem (DE-588)4196828-1 gnd Stochastischer Prozess (DE-588)4057630-9 gnd Simulation (DE-588)4055072-2 gnd |
| topic_facet | SCIENCE / System Theory TECHNOLOGY & ENGINEERING / Operations Research Discrete-time systems / Mathematical models Perturbation (Mathematics) Discrete-time systems Mathematical models Perturbation (Mathematics) Systems engineering Computer simulaton Diskretes Ereignissystem Stochastischer Prozess Simulation |
| work_keys_str_mv | AT chenchunhung stochasticsimulationoptimizationfordiscreteeventsystemsperturbationanalysisordinaloptimizationandbeyond AT jiaqingshan stochasticsimulationoptimizationfordiscreteeventsystemsperturbationanalysisordinaloptimizationandbeyond AT leeloohay stochasticsimulationoptimizationfordiscreteeventsystemsperturbationanalysisordinaloptimizationandbeyond |