Stochastic simulation optimization: an optimal computing budget allocation
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Singapore
World Scientific
c2011
|
| Schriftenreihe: | System engineering and operations research
vol. 1 |
| Schlagworte: | |
| Online-Zugang: | FAW01 FAW02 Volltext |
| Beschreibung: | Includes bibliographical references (p. 219-224) and index 1. Introduction to stochastic simulation optimization. 1.1. Introduction. 1.2. Problem definition. 1.3. Classification. 1.4. Summary -- 2. Computing budget allocation. 2.1. Simulation precision versus computing budget. 2.2. Computing budget allocation for comparison of multiple designs. 2.3. Intuitive explanations of optimal computing budget allocation. 2.4. Computing budget allocation for large simulation optimization. 2.5. Roadmap -- 3. Selecting the best from a set of alternative designs. 3.1. A Bayesian framework for simulation output modeling. 3.2. Probability of correct selection. 3.3. Maximizing the probability of correct selection. 3.4. Minimizing the total simulation cost. 3.5. Non-equal simulation costs. 3.6. Minimizing opportunity cost. 3.7. OCBA derivation based on classical model -- 4. Numerical implementation and experiments. 4.1. Numerical testing. 4.2. Parameter setting and implementation of the OCBA procedure -- - 5. Selecting an optimal subset. 5.1. Introduction and problem statement. 5.2. Approximate asymptotically optimal allocation scheme. 5.3. Numerical experiments -- 6. Multi-objective optimal computing budget allocation. 6.1. Pareto optimality. 6.2. Multi-objective optimal computing budget allocation problem. 6.3. Asymptotic allocation rule. 6.4. A sequential allocation procedure. 6.5. Numerical results -- 7. Large-scale simulation and optimization. 7.1. A general framework of integration of OCBA with metaheuristics. 7.2. Problems with single objective. 7.3. Numerical experiments. 7.4. Multiple objectives. 7.5. Concluding remarks -- - 8. Generalized OCBA framework and other related methods. 8.1. Optimal computing budget allocation for selecting the best by utilizing regression analysis (OCBA-OSD). 8.2. Optimal computing budget allocation for extended cross-entropy method (OCBA-CE). 8.3. Optimal computing budget allocation for variance reduction in rare-event simulation. 8.4. Optimal data collection budget allocation (ODCBA) for Monte Carlo DEA. 8.5. Other related works With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive. Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation |
| Beschreibung: | 1 Online-Ressource (xviii, 227 p.) |
| ISBN: | 1628702303 9781628702309 9789814282642 9789814282659 9814282642 9814282650 |
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| 500 | |a - 5. Selecting an optimal subset. 5.1. Introduction and problem statement. 5.2. Approximate asymptotically optimal allocation scheme. 5.3. Numerical experiments -- 6. Multi-objective optimal computing budget allocation. 6.1. Pareto optimality. 6.2. Multi-objective optimal computing budget allocation problem. 6.3. Asymptotic allocation rule. 6.4. A sequential allocation procedure. 6.5. Numerical results -- 7. Large-scale simulation and optimization. 7.1. A general framework of integration of OCBA with metaheuristics. 7.2. Problems with single objective. 7.3. Numerical experiments. 7.4. Multiple objectives. 7.5. Concluding remarks -- | ||
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| 500 | |a With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive. Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation | ||
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| 650 | 4 | |a Stochastic processes | |
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Datensatz im Suchindex
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| author | Chen, Chun-hung |
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| id | DE-604.BV043077685 |
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| indexdate | 2024-07-10T07:16:44Z |
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| isbn | 1628702303 9781628702309 9789814282642 9789814282659 9814282642 9814282650 |
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| series2 | System engineering and operations research |
| spelling | Chen, Chun-hung Verfasser aut Stochastic simulation optimization an optimal computing budget allocation Chun-Hung Chen, Loo Hay Lee Singapore World Scientific c2011 1 Online-Ressource (xviii, 227 p.) txt rdacontent c rdamedia cr rdacarrier System engineering and operations research vol. 1 Includes bibliographical references (p. 219-224) and index 1. Introduction to stochastic simulation optimization. 1.1. Introduction. 1.2. Problem definition. 1.3. Classification. 1.4. Summary -- 2. Computing budget allocation. 2.1. Simulation precision versus computing budget. 2.2. Computing budget allocation for comparison of multiple designs. 2.3. Intuitive explanations of optimal computing budget allocation. 2.4. Computing budget allocation for large simulation optimization. 2.5. Roadmap -- 3. Selecting the best from a set of alternative designs. 3.1. A Bayesian framework for simulation output modeling. 3.2. Probability of correct selection. 3.3. Maximizing the probability of correct selection. 3.4. Minimizing the total simulation cost. 3.5. Non-equal simulation costs. 3.6. Minimizing opportunity cost. 3.7. OCBA derivation based on classical model -- 4. Numerical implementation and experiments. 4.1. Numerical testing. 4.2. Parameter setting and implementation of the OCBA procedure -- - 5. Selecting an optimal subset. 5.1. Introduction and problem statement. 5.2. Approximate asymptotically optimal allocation scheme. 5.3. Numerical experiments -- 6. Multi-objective optimal computing budget allocation. 6.1. Pareto optimality. 6.2. Multi-objective optimal computing budget allocation problem. 6.3. Asymptotic allocation rule. 6.4. A sequential allocation procedure. 6.5. Numerical results -- 7. Large-scale simulation and optimization. 7.1. A general framework of integration of OCBA with metaheuristics. 7.2. Problems with single objective. 7.3. Numerical experiments. 7.4. Multiple objectives. 7.5. Concluding remarks -- - 8. Generalized OCBA framework and other related methods. 8.1. Optimal computing budget allocation for selecting the best by utilizing regression analysis (OCBA-OSD). 8.2. Optimal computing budget allocation for extended cross-entropy method (OCBA-CE). 8.3. Optimal computing budget allocation for variance reduction in rare-event simulation. 8.4. Optimal data collection budget allocation (ODCBA) for Monte Carlo DEA. 8.5. Other related works With the advance of new computing technology, simulation is becoming very popular for designing large, complex and stochastic engineering systems, since closed-form analytical solutions generally do not exist for such problems. However, the added flexibility of simulation often creates models that are computationally intractable. Moreover, to obtain a sound statistical estimate at a specified level of confidence, a large number of simulation runs (or replications) is usually required for each design alternative. If the number of design alternatives is large, the total simulation cost can be very expensive. Stochastic Simulation Optimization addresses the pertinent efficiency issue via smart allocation of computing resource in the simulation experiments for optimization, and aims to provide academic researchers and industrial practitioners with a comprehensive coverage of OCBA approach for stochastic simulation optimization. Starting with an intuitive explanation of computing budget allocation and a discussion of its impact on optimization performance, a series of OCBA approaches developed for various problems are then presented, from the selection of the best design to optimization with multiple objectives. Finally, this book discusses the potential extension of OCBA notion to different applications such as data envelopment analysis, experiments of design and rare-event simulation TECHNOLOGY & ENGINEERING / Engineering (General) bisacsh TECHNOLOGY & ENGINEERING / Reference bisacsh Stochastische Optimierung swd Stochastische optimale Kontrolle swd Systems engineering Simulation methods Stochastic processes Mathematical optimization Stochastische optimale Kontrolle (DE-588)4207850-7 gnd rswk-swf Stochastische Optimierung (DE-588)4057625-5 gnd rswk-swf Stochastische Optimierung (DE-588)4057625-5 s 1\p DE-604 Stochastische optimale Kontrolle (DE-588)4207850-7 s 2\p DE-604 Lee, Loo Hay Sonstige oth http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=374808 Aggregator Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Chen, Chun-hung Stochastic simulation optimization an optimal computing budget allocation TECHNOLOGY & ENGINEERING / Engineering (General) bisacsh TECHNOLOGY & ENGINEERING / Reference bisacsh Stochastische Optimierung swd Stochastische optimale Kontrolle swd Systems engineering Simulation methods Stochastic processes Mathematical optimization Stochastische optimale Kontrolle (DE-588)4207850-7 gnd Stochastische Optimierung (DE-588)4057625-5 gnd |
| subject_GND | (DE-588)4207850-7 (DE-588)4057625-5 |
| title | Stochastic simulation optimization an optimal computing budget allocation |
| title_auth | Stochastic simulation optimization an optimal computing budget allocation |
| title_exact_search | Stochastic simulation optimization an optimal computing budget allocation |
| title_full | Stochastic simulation optimization an optimal computing budget allocation Chun-Hung Chen, Loo Hay Lee |
| title_fullStr | Stochastic simulation optimization an optimal computing budget allocation Chun-Hung Chen, Loo Hay Lee |
| title_full_unstemmed | Stochastic simulation optimization an optimal computing budget allocation Chun-Hung Chen, Loo Hay Lee |
| title_short | Stochastic simulation optimization |
| title_sort | stochastic simulation optimization an optimal computing budget allocation |
| title_sub | an optimal computing budget allocation |
| topic | TECHNOLOGY & ENGINEERING / Engineering (General) bisacsh TECHNOLOGY & ENGINEERING / Reference bisacsh Stochastische Optimierung swd Stochastische optimale Kontrolle swd Systems engineering Simulation methods Stochastic processes Mathematical optimization Stochastische optimale Kontrolle (DE-588)4207850-7 gnd Stochastische Optimierung (DE-588)4057625-5 gnd |
| topic_facet | TECHNOLOGY & ENGINEERING / Engineering (General) TECHNOLOGY & ENGINEERING / Reference Stochastische Optimierung Stochastische optimale Kontrolle Systems engineering Simulation methods Stochastic processes Mathematical optimization |
| url | http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=374808 |
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