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Simulation-Based Optimization

Parametric Optimization Techniques and Reinforcement Learning

  • Abhijit Gosavi

Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 55)

Table of contents

  1. Front Matter
    Pages i-xxvi
  2. Abhijit Gosavi
    Pages 1-12
  3. Abhijit Gosavi
    Pages 13-27
  4. Abhijit Gosavi
    Pages 29-35
  5. Abhijit Gosavi
    Pages 269-280
  6. Abhijit Gosavi
    Pages 281-318
  7. Abhijit Gosavi
    Pages 451-471
  8. Back Matter
    Pages 473-508

About this book

Introduction

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.

Keywords

Computational Operations Research Operations Research Optimization Reinforcement Learning Simulation

Authors and affiliations

  • Abhijit Gosavi
    • 1
  1. 1.Department of Engineering Management and Systems EngineeringMissouri University of Science and TechnologyRollaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4899-7491-4
  • Copyright Information Springer Science+Business Media New York 2015
  • Publisher Name Springer, Boston, MA
  • eBook Packages Business and Economics
  • Print ISBN 978-1-4899-7490-7
  • Online ISBN 978-1-4899-7491-4
  • Series Print ISSN 1387-666X
  • Buy this book on publisher's site