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Handbook of simulation optimization /
The Handbook of Simulation Optimization presents an overview of the state of the art of simulation optimization, providing a survey of the most well-established approaches for optimizing stochastic simulation models and a sampling of recent research advances in theory and methodology. Leading contri...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Springer Science and Business Media,
[2015]
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| Colección: | International series in operations research & management science ;
216. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; Contents; Contributors; Selected Abbreviations and Notation; 1 Overview of the Handbook; References; 2 Discrete Optimization via Simulation; 2.1 Introduction; 2.1.1 Designing a Highly Reliable System; 2.1.2 Flow-Line Throughput; 2.1.3 Inventory Management with Dynamic Customer Substitution; 2.1.4 Themes; 2.2 Optimality Conditions; 2.3 Ranking and Selection; 2.4 Ordinal Optimization; 2.5 Globally Convergent Random Search Algorithms; 2.6 Locally Convergent Random Search Algorithms; 2.7 Algorithm Enhancements; 2.8 Using Commercial Solvers.
- 2.8.1 Preliminary Experiment to Control Sampling Variability2.8.2 Restarting the Optimization; 2.8.3 Statistical Clean Up After Search; References; 3 Ranking and Selection: Efficient Simulation Budget Allocation; 3.1 Introduction; 3.1.1 Intuitive Explanations of Simulation Budget Allocation; 3.1.2 Overview of Ranking and Selection (R & S); 3.1.3 Organization; 3.2 Problem Formulation and Selection Procedures; 3.2.1 Problem Formulation of Selecting the Best; 3.2.2 A Generic Algorithm for Selection Procedures; 3.2.3 General Concepts for OCBA and EVI; Basic Idea of OCBA; Basic Idea of EVI.
- 3.3 Optimal Computing Budget Allocation (OCBA)3.3.1 Maximization of PCS; 3.3.2 Asymptotic Allocation Rule; 3.3.3 Sequential Heuristic Algorithm for Allocation; OCBA Algorithm; Alternative Simpler OCBA Procedure; 3.3.4 Numerical Results; 3.3.5 Minimization of EOC; 3.3.6 Other Variants; Subset Selection Problem; Handling Optimization with Multiple Performance Measures; Other Recent Developments; Generalized OCBA Notions; 3.4 Expected Value of Information (EVI); 3.4.1 Linear Loss (LL); Minimization of EOC; Allocation Rules; 3.4.2 Small-Sample EVI Allocation Rule (LL1); Small-Sample EVI.
- Sequential Algorithm3.4.3 Stopping Rules; 3.4.4 Numerical Results for LL and LL1; 3.4.5 Economics of Selection Procedures (ESP); Maximizing Expected Reward; Optimal Stopping Problem for the Special Case of k==1 Alternative; ESP Allocation Rule and Stopping Rule; 3.4.6 Other Variants of EVI; 3.5 Conclusion; References; 4 Response Surface Methodology; 4.1 Introduction; 4.2 RSM Basics; 4.3 RSM in Simulation; 4.4 Adapted Steepest Descent (ASD); 4.5 Multiple Responses: Generalized RSM; 4.6 Testing an Estimated Optimum in GRSM: KKT Conditions; 4.7 Robust Optimization; Taguchi's Robust Optimization.
- Ben-Tal et al.'s Robust Optimization4.8 Conclusions; References; 5 Stochastic Gradient Estimation; 5.1 Introduction; 5.2 Indirect Gradient Estimators; 5.2.1 Finite Differences; 5.2.2 Simultaneous Perturbation; 5.3 Direct Gradient Estimators; 5.3.1 Derivatives of Random Variables; 5.3.2 Derivatives of Measures; 5.3.3 Input Distribution Examples; 5.3.4 Output Examples; Stochastic Activity Network; Single-Server Queue; Variance Reduction; Higher Derivatives; 5.3.5 Rudimentary Theory; 5.3.6 Guidelines for the Practitioner; 5.4 Quantile Sensitivity Estimation.