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...
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| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[Hackensack] New Jersey :
World Scientific,
2013.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| 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: | 1 online resource |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9789814513012 9814513016 |
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| 245 | 0 | 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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| 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."-- |c Provided by publisher | ||
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Chapter 1. The IPA calculus for hybrid systems -- chapter 2. Smoothed perturbation analysis : a retrospective and prospective look -- chapter 3. Perturbation analysis and variance reduction in Monte Carlo simulation -- chapter 4. Adjoints and averaging -- chapter 5. Infinitesimal perturbation analysis and optimization algorithms -- chapter 6. Simulation-based optimization of failure-prone continuous flow lines -- chapter 7. Perturbation analysis, dynamic programming, and beyond -- chapter 8. Fundamentals of ordinal optimization -- chapter 9. Optimal computing budget allocation framework -- chapter 10. Nested partitions -- chapter 11. Applications of ordinal optimization. | |
| 546 | |a English. | ||
| 650 | 0 | |a Discrete-time systems |x Mathematical models. | |
| 650 | 0 | |a Perturbation (Mathematics) |0 http://id.loc.gov/authorities/subjects/sh85100181 | |
| 650 | 0 | |a Systems engineering |x Computer simulaton. | |
| 650 | 6 | |a Systèmes échantillonnés |x Modèles mathématiques. | |
| 650 | 6 | |a Perturbation (Mathématiques) | |
| 650 | 7 | |a SCIENCE |x System Theory. |2 bisacsh | |
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| 650 | 7 | |a Discrete-time systems |x Mathematical models |2 fast | |
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| 700 | 1 | |a Chen, Chun-Hung, |d 1964- |e editor. | |
| 700 | 1 | |a Jia, Qing-Shan, |d 1980- |e editor. | |
| 700 | 1 | |a Lee, Loo Hay, |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Stochastic simulation optimization for discrete event systems. |d [Hackensack] New Jersey : World Scientific, 2013 |z 9789814513005 |w (DLC) 2013012700 |w (OCoLC)842879862 |
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| contents | Chapter 1. The IPA calculus for hybrid systems -- chapter 2. Smoothed perturbation analysis : a retrospective and prospective look -- chapter 3. Perturbation analysis and variance reduction in Monte Carlo simulation -- chapter 4. Adjoints and averaging -- chapter 5. Infinitesimal perturbation analysis and optimization algorithms -- chapter 6. Simulation-based optimization of failure-prone continuous flow lines -- chapter 7. Perturbation analysis, dynamic programming, and beyond -- chapter 8. Fundamentals of ordinal optimization -- chapter 9. Optimal computing budget allocation framework -- chapter 10. Nested partitions -- chapter 11. Applications of ordinal optimization. |
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| publisher | World Scientific, |
| record_format | marc |
| 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 text txt rdacontent computer c rdamedia online resource cr rdacarrier "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."-- Provided by publisher Print version record. Includes bibliographical references. Chapter 1. The IPA calculus for hybrid systems -- chapter 2. Smoothed perturbation analysis : a retrospective and prospective look -- chapter 3. Perturbation analysis and variance reduction in Monte Carlo simulation -- chapter 4. Adjoints and averaging -- chapter 5. Infinitesimal perturbation analysis and optimization algorithms -- chapter 6. Simulation-based optimization of failure-prone continuous flow lines -- chapter 7. Perturbation analysis, dynamic programming, and beyond -- chapter 8. Fundamentals of ordinal optimization -- chapter 9. Optimal computing budget allocation framework -- chapter 10. Nested partitions -- chapter 11. Applications of ordinal optimization. English. Discrete-time systems Mathematical models. Perturbation (Mathematics) http://id.loc.gov/authorities/subjects/sh85100181 Systems engineering Computer simulaton. Systèmes échantillonnés Modèles mathématiques. Perturbation (Mathématiques) SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh Discrete-time systems Mathematical models fast Perturbation (Mathematics) fast Chen, Chun-Hung, 1964- editor. Jia, Qing-Shan, 1980- editor. Lee, Loo Hay, editor. Print version: Stochastic simulation optimization for discrete event systems. [Hackensack] New Jersey : World Scientific, 2013 9789814513005 (DLC) 2013012700 (OCoLC)842879862 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=605588 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=605588 Volltext |
| spellingShingle | Stochastic Simulation Optimization for Discrete Event Systems : Perturbation Analysis, Ordinal Optimization, and Beyond / Chapter 1. The IPA calculus for hybrid systems -- chapter 2. Smoothed perturbation analysis : a retrospective and prospective look -- chapter 3. Perturbation analysis and variance reduction in Monte Carlo simulation -- chapter 4. Adjoints and averaging -- chapter 5. Infinitesimal perturbation analysis and optimization algorithms -- chapter 6. Simulation-based optimization of failure-prone continuous flow lines -- chapter 7. Perturbation analysis, dynamic programming, and beyond -- chapter 8. Fundamentals of ordinal optimization -- chapter 9. Optimal computing budget allocation framework -- chapter 10. Nested partitions -- chapter 11. Applications of ordinal optimization. Discrete-time systems Mathematical models. Perturbation (Mathematics) http://id.loc.gov/authorities/subjects/sh85100181 Systems engineering Computer simulaton. Systèmes échantillonnés Modèles mathématiques. Perturbation (Mathématiques) SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh Discrete-time systems Mathematical models fast Perturbation (Mathematics) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85100181 |
| 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 | Discrete-time systems Mathematical models. Perturbation (Mathematics) http://id.loc.gov/authorities/subjects/sh85100181 Systems engineering Computer simulaton. Systèmes échantillonnés Modèles mathématiques. Perturbation (Mathématiques) SCIENCE System Theory. bisacsh TECHNOLOGY & ENGINEERING Operations Research. bisacsh Discrete-time systems Mathematical models fast Perturbation (Mathematics) fast |
| topic_facet | Discrete-time systems Mathematical models. Perturbation (Mathematics) Systems engineering Computer simulaton. Systèmes échantillonnés Modèles mathématiques. Perturbation (Mathématiques) SCIENCE System Theory. TECHNOLOGY & ENGINEERING Operations Research. Discrete-time systems Mathematical models |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=605588 |
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