Screen LibraryStochastic simulation optimization for discrete event systems : perturbation analysis, ordinal optimization, and beyond /
Discrete event systems (DES) have become pervasive in our daily lives. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of...
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| Corporate Authors: | |
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| Group Author: | ; ; |
| Published: |
World Scientific Pub. Co.,
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| Publisher Address: | Singapore ; Hackensack, N.J. : |
| Publication Dates: | 2013. |
| Literature type: | eBook |
| Language: | English |
| Subjects: | |
| Online Access: |
http://www.worldscientific.com/worldscibooks/10.1142/8827#t=toc |
| Summary: |
Discrete event systems (DES) have become pervasive in our daily lives. Examples include (but are not restricted to) manufacturing and supply chains, transportation, healthcare, call centers, and financial engineering. However, due to their complexities that often involve millions or even billions of events with many variables and constraints, modeling these stochastic simulations has long been a "hard nut to crack". The advance in available computer technology, especially of cluster and cloud computing, has paved the way for the realization of a number of stochastic simulation optimization for complex discrete event systems. This book will introduce two important techniques initially proposed and developed by Professor Y.C. Ho and his team; namely perturbation analysis and ordinal optimization for stochastic simulation optimization, and present the state-of-the-art technology, and their future research directions. |
| Carrier Form: | 1 online resource (xxviii,245pages) : illustrations |
| Bibliography: | Includes bibliographical references . |
| ISBN: | 9789814513012 (electronic bk.) |
| Index Number: | TA343 |
| CLC: | TB111 |
| Contents: | pt. I. Perturbation analysis. ch. 1. The IPA calculus for hybrid systems. 1.1. Introduction. 1.2. Perturbation analysis of hybrid systems. 1.3. IPA properties. 1.4. General scheme for abstracting DES to SFM. 1.5. Conclusions and future work -- ch. 2. Smoothed perturbation analysis: a retrospective and prospective look. 2.1. Introduction. 2.2. Brief history of SPA. 2.3. Another example. 2.4. Overview of a general SPA framework. 2.5. Applications. 2.6. Random retrospective and prospective concluding remarks -- ch. 3. Perturbation analysis and variance reduction in Monte Carlo simulation. 3.1. Introduction. 3.2. Systematic and generic control variate selection. 3.3. Control variates for sensitivity estimation. 3.4. Database Monte Carlo (DBMC) implementation. 3.5. Conclusions -- ch. 4. Adjoints and averaging. 4.1. Introduction. 4.2. Adjoints: classical setting. 4.3. Adjoints: waiting times. 4.4. Adjoints: vector recursions. 4.5. Averaging. 4.6. Concluding remarks -- ch. 5. Infinitesimal perturbation analysis and optimization algorithms. 5.1. Preliminary remarks. 5.2. Motivation. 5.3. Single-server queues. 5.4. Convergence. 5.5. Final remarks -- ch. 6. Simulation-based optimization of failure-prone continuous flow lines. 6.1. Introduction. 6.2. Two-machine continuous flow lines. 6.3. Gradient estimation of a two-machine line. 6.4. Modeling assembly/disassembly networks subject to TDF failures with stochastic fluid event graphs. 6.5. Evolution equations and sample path gradients. 6.6. Optimization of stochastic fluid event graphs. 6.7. Conclusion -- ch. 7. Perturbation analysis, dynamic programming, and beyond. 7.1. Introduction. 7.2. Perturbation analysis of queueing systems based on perturbation realization factors. 7.3. Performance optimization of Markov systems based on performance potentials. 7.4. Beyond dynamic programming -- pt. II. Ordinal optimization. ch. 8. Fundamentals of ordinal optimization. 8.1. Two basic ideas. 8.2. The exponential convergence of order and goal softening. 8.3. Universal alignment probabilities. 8.4. Extensions. 8.5. Conclusion -- ch. 9. Optimal computing budget allocation framework. 9.1. Introduction. 9.2. History of OCBA. 9.3. Basics of OCBA. 9.4. Different extensions of OCBA. 9.5. Generalized OCBA framework. 9.6. Applications of OCBA. 9.7. Future research. 9.8. Concluding remarks -- ch. 10. Nested partitions. 10.1. Overview. 10.2. Nested partitions for deterministic optimization. 10.3. Enhancements and advanced developments. 10.4. Nested partitions for stochastic optimization. 10.5. Conclusions -- ch. 11. Applications of ordinal optimization. 11.1. Scheduling problem for apparel manufacturing. 11.2. The turbine blade manufacturing process optimization problem. 11.3. Performance optimization for a remanufacturing system. 11.4. Witsenhausen problem. 11.5. Other application researches. |