Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning

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Springer Science & Business Media, Jun 30, 2003 - Business & Economics - 554 pages
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Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. Since it became possible to analyze random systems using computers, scientists and engineers have sought the means to optimize systems using simulation models. Only recently, however, has this objective had success in practice. Cutting-edge work in computational operations research, including non-linear programming (simultaneous perturbation), dynamic programming (reinforcement learning), and game theory (learning automata) has made it possible to use simulation in conjunction with optimization techniques. As a result, this research has given simulation added dimensions and power that it did not have in the recent past.

The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work.
Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are:
*An accessible introduction to reinforcement learning and parametric-optimization techniques.
*A step-by-step description of several algorithms of simulation-based optimization.
*A clear and simple introduction to the methodology of neural networks.
*A gentle introduction to convergence analysis of some of the methods enumerated above.
*Computer programs for many algorithms of simulation-based optimization.

This book is written for students and researchers in the fields of engineering (electrical, industrial and computer), computer science, operations research, management science, and applied mathematics.
  

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Contents

BACKGROUND
1
NOTATION
9
A REFRESHER
15
BASIC CONCEPTS UNDERLYING SIMULATION
29
AN OVERVIEW
47
RESPONSE SURFACES AND NEURAL NETS
57
PARAMETRIC OPTIMIZATION
93
DYNAMIC PROGRAMMING
133
MARKOV CHAIN AUTOMATA THEORY
277
BACKGROUND MATERIAL
287
PARAMETRIC OPTIMIZATION
317
CONTROL OPTIMIZATION
343
CASE STUDIES
409
CODES
433
CONCLUDING REMARKS
537
Copyright

REINFORCEMENT LEARNING
211

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About the author (2003)

Abhijit Gosavi: Department of Industrial Engineering The State University of New York, Buffalo