Stochastic Simulation Optimization: An Optimal Computing Budget Allocation
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| Title: | Stochastic Simulation Optimization: An Optimal Computing Budget Allocation |
|---|---|
| Description: | 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. |
| Authors: | Chun-hung Chen, Loo Hay Lee |
| Resource Type: | eBook. |
| Subjects: | Systems engineering--Simulation methods, Stochastic processes, Mathematical optimization |
| Categories: | COMPUTERS / Computer Simulation, MATHEMATICS / Probability & Statistics / General, TECHNOLOGY & ENGINEERING / Industrial Engineering |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Stochastic Simulation Optimization: An Optimal Computing Budget Allocation – Name: Abstract Label: Description Group: Ab Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chun-hung+Chen%22">Chun-hung Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Loo+Hay+Lee%22">Loo Hay Lee</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Systems+engineering--Simulation+methods%22">Systems engineering--Simulation methods</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Computer+Simulation%22">COMPUTERS / Computer Simulation</searchLink><br /><searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink><br /><searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Industrial+Engineering%22">TECHNOLOGY & ENGINEERING / Industrial Engineering</searchLink> |
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| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 620.001171 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Systems engineering--Simulation methods Type: general – SubjectFull: Stochastic processes Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: Stochastic Simulation Optimization: An Optimal Computing Budget Allocation Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chun-hung Chen – PersonEntity: Name: NameFull: Loo Hay Lee – PersonEntity: Name: NameFull: Chun-hung Chen – PersonEntity: Name: NameFull: Loo Hay Lee IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2011 – D: 04 M: 02 Type: profile Y: 2014 Identifiers: – Type: isbn-print Value: 9789814282642 – Type: isbn-electronic Value: 9789814282659 Numbering: – Type: volume Value: 00001 Titles: – TitleFull: Stochastic Simulation Optimization: An Optimal Computing Budget Allocation Type: main |
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