Stochastic simulation optimization: an optimal computing budget allocation
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore ; Hackensack, NJ
World Scientific
2011
|
| Series: | System engineering and operations research
vol. 1 |
| Subjects: | |
| Item Description: | Print version record |
| Physical Description: | 1 online resource (xviii, 227 pages) illustrations |
| ISBN: | 9789814282659 9814282650 9781628702309 1628702303 |
Staff View
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| 505 | 8 | |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 | 7 | |a Stochastische Optimierung |2 gnd | |
| 650 | 7 | |a Stochastische optimale Kontrolle |2 gnd | |
| 650 | 7 | |a Mathematical optimization |2 fast | |
| 650 | 7 | |a Stochastic processes |2 fast | |
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Record in the Search Index
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| any_adam_object | |
| author | Chen, Chun-hung |
| author_facet | Chen, Chun-hung |
| author_role | aut |
| author_sort | Chen, Chun-hung |
| author_variant | c h c chc |
| building | Verbundindex |
| bvnumber | BV045344451 |
| classification_rvk | SK 870 |
| collection | ZDB-4-ENC |
| contents | 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 |
| ctrlnum | (ZDB-4-ENC)ocn742584181 (OCoLC)742584181 (DE-599)BVBBV045344451 |
| dewey-full | 620.001/171 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 620 - Engineering and allied operations |
| dewey-raw | 620.001/171 |
| dewey-search | 620.001/171 |
| dewey-sort | 3620.001 3171 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Mathematik |
| format | Electronic eBook |
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| id | DE-604.BV045344451 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:15:31Z |
| institution | BVB |
| isbn | 9789814282659 9814282650 9781628702309 1628702303 |
| language | English |
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| physical | 1 online resource (xviii, 227 pages) illustrations |
| psigel | ZDB-4-ENC |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | World Scientific |
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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 ; Hackensack, NJ World Scientific 2011 1 online resource (xviii, 227 pages) illustrations txt rdacontent c rdamedia cr rdacarrier System engineering and operations research vol. 1 Print version record 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 gnd Stochastische optimale Kontrolle gnd Mathematical optimization fast Stochastic processes fast Electronic books Systems engineering Simulation methods Stochastic processes Mathematical optimization Stochastische Optimierung (DE-588)4057625-5 gnd rswk-swf Stochastische optimale Kontrolle (DE-588)4207850-7 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 Erscheint auch als Druck-Ausgabe Chen, Chun-hung Stochastic simulation optimization Singapore ; Hackensack, NJ : World Scientific ; c2011 9789814282642 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 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 gnd Stochastische optimale Kontrolle gnd Mathematical optimization fast Stochastic processes fast Electronic books Systems engineering Simulation methods Stochastic processes Mathematical optimization Stochastische Optimierung (DE-588)4057625-5 gnd Stochastische optimale Kontrolle (DE-588)4207850-7 gnd |
| subject_GND | (DE-588)4057625-5 (DE-588)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 | TECHNOLOGY & ENGINEERING / Engineering (General) bisacsh TECHNOLOGY & ENGINEERING / Reference bisacsh Stochastische Optimierung gnd Stochastische optimale Kontrolle gnd Mathematical optimization fast Stochastic processes fast Electronic books Systems engineering Simulation methods Stochastic processes Mathematical optimization Stochastische Optimierung (DE-588)4057625-5 gnd Stochastische optimale Kontrolle (DE-588)4207850-7 gnd |
| topic_facet | TECHNOLOGY & ENGINEERING / Engineering (General) TECHNOLOGY & ENGINEERING / Reference Stochastische Optimierung Stochastische optimale Kontrolle Mathematical optimization Stochastic processes Electronic books Systems engineering Simulation methods Stochastic processes Mathematical optimization |
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