Simulation-based Optimization: Parametric Optimization Techniques and Reinforcement Learning: v. 25 (Operations Research/Computer Science Interfaces Series) - Hardcover

Gosavi, Abhijit

Hardcover

ISBN 10: 1402074549 ISBN 13: 9781402074547

Publisher: Kluwer Academic Publishers, 2003

Synopsis

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization.

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.

About this title

From the Back Cover

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.

Key features of this revised and improved Second Edition include:

· Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search, and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search, and meta-heuristics (simulated annealing, tabu search, and genetic algorithms)

· Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for  discounted, average, and total reward performance metrics

· An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata

· A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online), and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations

Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning, and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical, and computer), operations research, computer science, and applied mathematics.

About the Author

Abhijit Gosavi is a leading international authority on reinforcement learning, stochastic dynamic programming and simulation-based optimization. The first edition of his Springer book “Simulation-Based Optimization” that appeared in 2003 was the first text to have appeared on that topic. He is regularly an invited speaker at major national and international conferences on operations research, reinforcement learning, adaptive/approximate dynamic programming, and systems engineering.

He has published more than fifty journal and conference articles – many of which have appeared in leading scholarly journals such as Management Science, Automatica, INFORMS Journal on Computing, Machine Learning, Journal of Retailing, Systems and Control Letters and the European Journal of Operational Research. He has also authored numerous book chapters on simulation-based optimization and operations research. His research has been funded by the National Science Foundation, Department of Defense, Missouri Department of Transportation, University of Missouri Research Board and industry. He has consulted extensively for the U.S. Department of Veterans Affairs and the mass media as a statistical/simulation analyst. He has received teaching awards from the Institute of Industrial Engineers.

He currently serves as an Associate Professor of Engineering Management and Systems Engineering at Missouri University of Science and Technology in Rolla, MO. He holds a masters degree in Mechanical Engineering from the Indian Institute of Technology and a Ph.D. in Industrial Engineering from the University of South Florida. He is a member of INFORMS, IIE and ASEE.

Edition details

• Publisher: Kluwer Academic Publishers
• Publication date: 2003
• ISBN 10: 1402074549
• ISBN 13: 9781402074547
• Binding: Hardcover
• Language: English
• Number of pages: 554