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Stochastic Simulation Optimization An Optimal Computing Budget Allocation Chunhung Chen Loo Hay Lee

Name: Stochastic Simulation Optimization An Optimal Computing Budget Allocation Chunhung Chen Loo Hay Lee
SKU: BELL-4720110
Price: 31.00 USD
Availability: InStock
Rating: 4.4 (62 reviews)

SKU: BELL-4720110

$ 31.00 ~~$ 45.00~~ (-31%)

4.4

62 reviews

Stochastic Simulation Optimization An Optimal Computing Budget Allocation Chunhung Chen Loo Hay Lee instant download after payment.

Publisher: World Scientific

File Extension: PDF

File size: 3.45 MB

Pages: 246

Author: Chun-hung Chen; Loo Hay Lee

ISBN: 9781628702309, 9789814282659, 1628702303, 9814282650

Language: English

Year: 2011

Product desciption

Stochastic Simulation Optimization An Optimal Computing Budget Allocation Chunhung Chen Loo Hay Lee by Chun-hung Chen; Loo Hay Lee 9781628702309, 9789814282659, 1628702303, 9814282650 instant download after payment.

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.