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 a...

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Bibliographic Details
Main Author: Chen, Chun-hung.
Other Authors: Lee, Loo Hay.
Format: eBook
Language: English
Published: Singapore ; Hackensack, NJ : World Scientific, ©2011.
Series: System engineering and operations research ; vol. 1.
Subjects:
Systems engineering > Simulation methods.
Stochastic processes.
Mathematical optimization.
elektronické knihy
electronic books
ISBN: 9789814282659
9814282650
9781628702309
1628702303
9789814282642
9814282642
Physical Description: 1 online resource (xviii, 227 pages) : illustrations.

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Table of contents

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100 1 |a Chen, Chun-hung. 
245 1 0 |a Stochastic simulation optimization :  |b an optimal computing budget allocation /  |c Chun-Hung Chen, Loo Hay Lee. 
260 |a Singapore ;  |a Hackensack, NJ :  |b World Scientific,  |c ©2011. 
300 |a 1 online resource (xviii, 227 pages) :  |b illustrations. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Series on system engineering and operations research ;  |v vol. 1 
504 |a Includes bibliographical references (pages 219-224) and index. 
505 0 |a 1. Introduction to stochastic simulation optimization. 1.1. Introduction. 1.2. Problem definition. 1.3. Classification. 1.4. Summary -- 2. Computing budget allocation. 2.1. Simulation precision versus computing budget. 2.2. Computing budget allocation for comparison of multiple designs. 2.3. Intuitive explanations of optimal computing budget allocation. 2.4. Computing budget allocation for large simulation optimization. 2.5. Roadmap -- 3. Selecting the best from a set of alternative designs. 3.1. A Bayesian framework for simulation output modeling. 3.2. Probability of correct selection. 3.3. Maximizing the probability of correct selection. 3.4. Minimizing the total simulation cost. 3.5. Non-equal simulation costs. 3.6. Minimizing opportunity cost. 3.7. OCBA derivation based on classical model -- 4. Numerical implementation and experiments. 4.1. Numerical testing. 4.2. Parameter setting and implementation of the OCBA procedure -- 5. Selecting an optimal subset. 5.1. Introduction and problem statement. 5.2. Approximate asymptotically optimal allocation scheme. 5.3. Numerical experiments -- 6. Multi-objective optimal computing budget allocation. 6.1. Pareto optimality. 6.2. Multi-objective optimal computing budget allocation problem. 6.3. Asymptotic allocation rule. 6.4. A sequential allocation procedure. 6.5. Numerical results -- 7. Large-scale simulation and optimization. 7.1. A general framework of integration of OCBA with metaheuristics. 7.2. Problems with single objective. 7.3. Numerical experiments. 7.4. Multiple objectives. 7.5. Concluding remarks -- 8. Generalized OCBA framework and other related methods. 8.1. Optimal computing budget allocation for selecting the best by utilizing regression analysis (OCBA-OSD). 8.2. Optimal computing budget allocation for extended cross-entropy method (OCBA-CE). 8.3. Optimal computing budget allocation for variance reduction in rare-event simulation. 8.4. Optimal data collection budget allocation (ODCBA) for Monte Carlo DEA. 8.5. Other related works. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
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. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Systems engineering  |x Simulation methods. 
650 0 |a Stochastic processes. 
650 0 |a Mathematical optimization. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Lee, Loo Hay. 
776 0 8 |i Print version:  |a Chen, Chun-hung.  |t Stochastic simulation optimization.  |d Singapore ; Hackensack, NJ : World Scientific ; c2011  |z 9789814282642  |w (DLC) 2010537570  |w (OCoLC)456170891 
830 0 |a System engineering and operations research ;  |v vol. 1. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpSSOAOCBS/stochastic-simulation-optimization?kpromoter=marc  |y Full text 

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