Stochastic simulation optimization : an optimal computing budget allocation /

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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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Chen, Chun-hung
Otros Autores: Lee, Loo Hay
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore ; Hackensack, NJ : World Scientific, ©2011.
Colección:System engineering and operations research ; vol. 1.
Temas:
Systems engineering > Simulation methods.
Stochastic processes.
Mathematical optimization.
Ingénierie des systèmes > Méthodes de simulation.
Processus stochastiques.
Optimisation mathématique.
TECHNOLOGY & ENGINEERING > Engineering (General)
TECHNOLOGY & ENGINEERING > Reference.
Stochastische Optimierung
Stochastische optimale Kontrolle
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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