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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]
|
| Colección: | International series in operations research & management science ;
216. |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Handbook of simulation optimization / |c Michael C. Fu, Editor. |
| 264 | 1 | |a New York : |b Springer Science and Business Media, |c [2015] | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource (xvi, 387 pages) : |b illustrations (some color) | ||
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| 490 | 1 | |a International Series in Operations Research & Management Science ; |v 216 | |
| 588 | 0 | |a Vendor-supplied metadata. | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 520 | |a 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 contributors cover such topics as discrete optimization via simulation, ranking and selection, efficient simulation budget allocation, random search methods, response surface methodology, stochastic gradient estimation, stochastic approximation, sample average approximation, stochastic constraints, varia. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Simulation methods. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Stochastic processes. | |
| 650 | 0 | |a Operations research. | |
| 650 | 6 | |a Méthodes de simulation. | |
| 650 | 6 | |a Optimisation mathématique. | |
| 650 | 6 | |a Processus stochastiques. | |
| 650 | 6 | |a Recherche opérationnelle. | |
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| 650 | 7 | |a Stochastic processes |2 fast | |
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| 830 | 0 | |a International series in operations research & management science ; |v 216. | |
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