소장자료
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| 005 | 20240521162014▲ | ||
| 007 | ta ▲ | ||
| 008 | 110202s2011 si a bi 001 0 eng c▲ | ||
| 010 | ▼a2010537570▲ | ||
| 020 | ▼a9789814282642 (hbk.)▲ | ||
| 020 | ▼a9814282642▲ | ||
| 035 | ▼a(KERIS)REF000016215540▲ | ||
| 040 | ▼aBTCTA▼cBTCTA▼dYDXCP▼dBWX▼dIUP▼dHNK▼dTXA▼dMUU▼dDLC▼d221016▲ | ||
| 050 | 0 | 0 | ▼aTA168▼b.C475 2011▲ |
| 082 | 0 | 4 | ▼a658.4▼223▲ |
| 090 | ▼a658.4▼bC518s▲ | ||
| 100 | 1 | ▼aChen, Chun-hung.▲ | |
| 245 | 1 | 0 | ▼aStochastic simulation optimization :▼ban optimal computing budget allocation /▼cby Chun-Hung Chen, Loo Hay Lee.▲ |
| 260 | ▼aSingapore ;▼aHackensack, NJ :▼bWorld Scientific ;▼c2011.▲ | ||
| 300 | ▼axviii, 227 p. :▼bill. ;▼c24 cm.▲ | ||
| 336 | ▼atext▼btxt▼2rdacontent▲ | ||
| 337 | ▼aunmediated▼bn▼2rdamedia▲ | ||
| 338 | ▼avolume▼bnc▼2rdacarrier▲ | ||
| 490 | 0 | ▼aSystem engineering and operations research ;▼v1▲ | |
| 504 | ▼aIncludes bibliographical references (p. 219-224) and index.▲ | ||
| 505 | 0 | ▼aIntroduction to Stochastic Simulation and Optimization; Computing Budget Allocation; Selecting the Best from A Set of Alternative Designs; Implementation and Numerical Experiments; Selecting An Optimal Subset; Multiobjective Optimal Computing Budget Allocation; Large-Scale Simulation and Optimization; Generalized Computing Budget Allocation; Appendices: Fundamentals of Stochastic Simulation; Some Basic Probability and Statistics.▲ | |
| 520 | ▼aWith 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 | ▼aSystems engineering▼xSimulation methods.▲ | |
| 650 | 0 | ▼aStochastic processes.▲ | |
| 650 | 0 | ▼aMathematical optimization.▲ | |
| 700 | 1 | ▼aLee, Loo Hay.▲ |
Stochastic simulation optimization : an optimal computing budget allocation
자료유형
국외단행본
서명/책임사항
Stochastic simulation optimization : an optimal computing budget allocation / by Chun-Hung Chen, Loo Hay Lee.
발행사항
Singapore ; Hackensack, NJ : World Scientific ; 2011.
형태사항
xviii, 227 p. : ill. ; 24 cm.
서지주기
Includes bibliographical references (p. 219-224) and index.
내용주기
Introduction to Stochastic Simulation and Optimization; Computing Budget Allocation; Selecting the Best from A Set of Alternative Designs; Implementation and Numerical Experiments; Selecting An Optimal Subset; Multiobjective Optimal Computing Budget Allocation; Large-Scale Simulation and Optimization; Generalized Computing Budget Allocation; Appendices: Fundamentals of Stochastic Simulation; Some Basic Probability and Statistics.
요약주기
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.
ISBN
9789814282642 (hbk.) 9814282642
청구기호
658.4 C518s
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