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Stochastic simulation optimizationan...

Chen, Chun-hung.

Stochastic simulation optimizationan optimal computing budget allocation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Stochastic simulation optimizationChun-Hung Chen, Loo Hay Lee.
其他題名:	an optimal computing budget allocation /
作者:	Chen, Chun-hung.
其他作者:	Lee, Loo Hay.
出版者:	Singapore ;World Scientific,c2011.
面頁冊數:	1 online resource (xviii, 227 p.) :ill.
標題:	Systems engineeringSimulation methods.
電子資源:	http://www.worldscientific.com/worldscibooks/10.1142/7437#t=toc
ISBN:	9789814282659 (electronic bk.)

Stochastic simulation optimizationan optimal computing budget allocation /
Chen, Chun-hung.

Stochastic simulation optimizationan optimal computing budget allocation /[electronic resource] :Chun-Hung Chen, Loo Hay Lee. - Singapore ;World Scientific,c2011. - 1 online resource (xviii, 227 p.) :ill. - Series on system engineering and operations research ;vol. 1. - System engineering and operations research ;vol. 1..

Includes bibliographical references (p. 219-224) and index.

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: 9789814282659 (electronic bk.)Subjects--Topical Terms:

575477
Systems engineering
--Simulation methods.

LC Class. No.: TA168 / .C473 2011eb

Dewey Class. No.: 620.001/171

Stochastic simulation optimizationan optimal computing budget allocation /
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490 1 $a Series on system engineering and operations research ; $v vol. 1
504 $a Includes bibliographical references (p. 219-224) and index.
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.
588 $a Description based on print version record.
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650 0 $a Stochastic processes. $3 181874
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