rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Stochastic simulation optimization
    sub title: an optimal computing budget allocation
  name:
    name part: Chen, Chun-hung.
    role:
      role term: creator
  name:
    name part: Lee, Loo Hay.
  type of resource: text
  genre: bibliography
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: si
    place:
      place term: Singapore
    place:
      place term: Hackensack, NJ
    publisher: World Scientific
    date issued: ©2011
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xviii, 227 pages) : illustrations.
  abstract: 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.
  table of contents: 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…
  note: Chun-Hung Chen, Loo Hay Lee.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  note: Includes bibliographical references (pages 219-224) and index.
  subject:
    topic: Systems engineering
    topic: Simulation methods
  subject:
    topic: Stochastic processes
  subject:
    topic: Mathematical optimization
  related item:
    title info:
      title: Stochastic simulation optimization
    name:
      name part: Chen, Chun-hung.
    origin info:
      publisher: Singapore ; Hackensack, NJ : World Scientific ; c2011
    identifier: (DLC) 2010537570
    identifier: (OCoLC)456170891
  related item:
    title info:
      title: System engineering and operations research ; vol. 1
  identifier: 9789814282659
  identifier: 9814282650
  identifier: 9781628702309
  identifier: 1628702303
  identifier: 9789814282642
  identifier: 9814282642
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: N$T
    record creation date: 110726
    record change date: 20240717213016.0
    record identifier: kn-ocn742584181
    language of cataloging:
      language term: eng