Operations Research/Computer Science Interfaces Series

Simulation-Based Optimization

Parametric Optimization Techniques and Reinforcement Learning

Authors: Gosavi, Abhijit

  • Brings the field completely up to date
  • All computer code brought up to date
  • New material not covered in first edition includes nested partitions, simultaneous perturbation, backtracking adaptive search and the stochastic ruler method
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eBook $69.99
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  • ISBN 978-1-4899-7491-4
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  • Immediate eBook download after purchase
Hardcover $99.00
price for USA
  • ISBN 978-1-4899-7490-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $99.00
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  • ISBN 978-1-4899-7731-1
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  • Usually dispatched within 3 to 5 business days.
About this Textbook

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce 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 authors

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.

Table of contents (12 chapters)

  • Background

    Gosavi, Abhijit

    Pages 1-12

  • Simulation Basics

    Gosavi, Abhijit

    Pages 13-27

  • Simulation-Based Optimization: An Overview

    Gosavi, Abhijit

    Pages 29-35

  • Parametric Optimization: Response Surfaces and Neural Networks

    Gosavi, Abhijit

    Pages 37-69

  • Parametric Optimization: Stochastic Gradients and Adaptive Search

    Gosavi, Abhijit

    Pages 71-122

Buy this book

eBook $69.99
price for USA (gross)
  • ISBN 978-1-4899-7491-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $99.00
price for USA
  • ISBN 978-1-4899-7490-7
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $99.00
price for USA
  • ISBN 978-1-4899-7731-1
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Simulation-Based Optimization
Book Subtitle
Parametric Optimization Techniques and Reinforcement Learning
Authors
Series Title
Operations Research/Computer Science Interfaces Series
Series Volume
55
Copyright
2015
Publisher
Springer US
Copyright Holder
Springer Science+Business Media New York
eBook ISBN
978-1-4899-7491-4
DOI
10.1007/978-1-4899-7491-4
Hardcover ISBN
978-1-4899-7490-7
Softcover ISBN
978-1-4899-7731-1
Series ISSN
1387-666X
Edition Number
2
Number of Pages
XXVI, 508
Number of Illustrations and Tables
42 b/w illustrations
Topics