Stochastic simulation optimization :: an optimal computing budget allocation /
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 a...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Singapore ; Hackensack, NJ :
World Scientific,
©2011.
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| Schriftenreihe: | System engineering and operations research ;
vol. 1. |
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| Online-Zugang: | Volltext |
| Zusammenfassung: | 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 resource (xviii, 227 pages :) |
| Bibliographie: | Includes bibliographical references (pages 219-224) and index. |
| ISBN: | 9789814282659 9814282650 9781628702309 1628702303 |
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| 505 | 0 | |a 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. | |
| 520 | |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. | ||
| 650 | 0 | |a Systems engineering |x Simulation methods. | |
| 650 | 0 | |a Stochastic processes. |0 http://id.loc.gov/authorities/subjects/sh85128181 | |
| 650 | 0 | |a Mathematical optimization. |0 http://id.loc.gov/authorities/subjects/sh85082127 | |
| 650 | 6 | |a Ingénierie des systèmes |x Méthodes de simulation. | |
| 650 | 6 | |a Processus stochastiques. | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Engineering (General) |2 bisacsh | |
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| 650 | 7 | |a Stochastische Optimierung |2 gnd |0 http://d-nb.info/gnd/4057625-5 | |
| 650 | 7 | |a Stochastische optimale Kontrolle |2 gnd |0 http://d-nb.info/gnd/4207850-7 | |
| 655 | 4 | |a Electronic book. | |
| 700 | 1 | |a Lee, Loo Hay. |0 http://id.loc.gov/authorities/names/no2010154231 | |
| 776 | 0 | 8 | |i Print version: |a Chen, Chun-hung. |t Stochastic simulation optimization. |d Singapore ; Hackensack, NJ : World Scientific ; c2011 |z 9789814282642 |w (DLC) 2010537570 |w (OCoLC)456170891 |
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| contents | 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. |
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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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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. 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| genre | Electronic book. |
| genre_facet | Electronic book. |
| id | ZDB-4-EBA-ocn742584181 |
| illustrated | Illustrated |
| indexdate | 2024-10-25T16:18:12Z |
| institution | BVB |
| isbn | 9789814282659 9814282650 9781628702309 1628702303 |
| language | English |
| oclc_num | 742584181 |
| open_access_boolean | |
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| owner_facet | MAIN |
| physical | 1 online resource (xviii, 227 pages :) |
| psigel | ZDB-4-EBA |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | World Scientific, |
| record_format | marc |
| series | System engineering and operations research ; |
| series2 | Series on system engineering and operations research ; |
| spelling | Chen, Chun-hung. http://id.loc.gov/authorities/names/no2007051730 Stochastic simulation optimization : an optimal computing budget allocation / Chun-Hung Chen, Loo Hay Lee. Singapore ; Hackensack, NJ : World Scientific, ©2011. 1 online resource (xviii, 227 pages :) text txt rdacontent computer c rdamedia online resource cr rdacarrier Series on system engineering and operations research ; vol. 1 Includes bibliographical references (pages 219-224) and index. Print version record. 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. Systems engineering Simulation methods. Stochastic processes. http://id.loc.gov/authorities/subjects/sh85128181 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Ingénierie des systèmes Méthodes de simulation. Processus stochastiques. Optimisation mathématique. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Mathematical optimization fast Stochastic processes fast Stochastische Optimierung gnd http://d-nb.info/gnd/4057625-5 Stochastische optimale Kontrolle gnd http://d-nb.info/gnd/4207850-7 Electronic book. Lee, Loo Hay. http://id.loc.gov/authorities/names/no2010154231 Print version: Chen, Chun-hung. Stochastic simulation optimization. Singapore ; Hackensack, NJ : World Scientific ; c2011 9789814282642 (DLC) 2010537570 (OCoLC)456170891 System engineering and operations research ; vol. 1. http://id.loc.gov/authorities/names/no2010155520 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=374808 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=374808 Volltext |
| spellingShingle | Chen, Chun-hung Stochastic simulation optimization : an optimal computing budget allocation / System engineering and operations research ; 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. Systems engineering Simulation methods. Stochastic processes. http://id.loc.gov/authorities/subjects/sh85128181 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Ingénierie des systèmes Méthodes de simulation. Processus stochastiques. Optimisation mathématique. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Mathematical optimization fast Stochastic processes fast Stochastische Optimierung gnd http://d-nb.info/gnd/4057625-5 Stochastische optimale Kontrolle gnd http://d-nb.info/gnd/4207850-7 |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85128181 http://id.loc.gov/authorities/subjects/sh85082127 http://d-nb.info/gnd/4057625-5 http://d-nb.info/gnd/4207850-7 |
| 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 | Systems engineering Simulation methods. Stochastic processes. http://id.loc.gov/authorities/subjects/sh85128181 Mathematical optimization. http://id.loc.gov/authorities/subjects/sh85082127 Ingénierie des systèmes Méthodes de simulation. Processus stochastiques. Optimisation mathématique. TECHNOLOGY & ENGINEERING Engineering (General) bisacsh TECHNOLOGY & ENGINEERING Reference. bisacsh Mathematical optimization fast Stochastic processes fast Stochastische Optimierung gnd http://d-nb.info/gnd/4057625-5 Stochastische optimale Kontrolle gnd http://d-nb.info/gnd/4207850-7 |
| topic_facet | Systems engineering Simulation methods. Stochastic processes. Mathematical optimization. Ingénierie des systèmes Méthodes de simulation. Processus stochastiques. Optimisation mathématique. TECHNOLOGY & ENGINEERING Engineering (General) TECHNOLOGY & ENGINEERING Reference. Mathematical optimization Stochastic processes Stochastische Optimierung Stochastische optimale Kontrolle Electronic book. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=374808 |
| work_keys_str_mv | AT chenchunhung stochasticsimulationoptimizationanoptimalcomputingbudgetallocation AT leeloohay stochasticsimulationoptimizationanoptimalcomputingbudgetallocation |