Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning (Operations Research/Computer Science Interfaces Series)

This book introduces the reader to the evolving area of simulation-based optimization, also known as simulation optimization. The book should serve as an accessible introduction to this topic and requires a background only in elementary mathematics. It brings the reader up to date on cutting-edge advances in simulation-optimization methodologies, including dynamic controls, also called Reinforcement Learning (RL) or Approximate Dynamic Programming (ADP), and static optimization techniques, e.g.,Simultaneous Perturbation, Nested Partitions, Backtracking Adaptive Search, Response Surfaces, and Meta-Heuristics. Special features of this book include:

Stochastic Control Optimization:

• An Accessible Introduction to Reinforcement Learning Techniques for Solving Markov Decision Processes (MDPs), with Step-by-Step Descriptions of Numerous Algorithms, e.g., Q-Learning, SARSA, R-SMART, Actor-Critics, Q-P-Learning, and Classical Approximate Policy Iteration
• A Detailed Discussion on Dynamic Programing for Solving MDPs and Semi-MDPs (SMDPs), Including Steps for Value Iteration and Policy Iteration
• An Introduction to Function Approximation with Reinforcement Learning
• An In-Depth Treatment of Reinforcement Learning Methods for SMDPs, Average Reward Problems, Finite Horizon Problems, and Two Time Scales
• Computer Programs (available online)
• A Gentle Introduction to Convergence Analysis via Banach Fixed Point Theory and Ordinary Differential Equations (ODEs)

Stochastic Static Optimization:

• A Step-by-Step Description of Stochastic Adaptive Search Algorithms, e.g., Simultaneous Perturbation, Nested Partitions, Backtracking Adaptive Search, Stochastic Ruler, and Meta-Heuristics, e.g., Simulated Annealing, Tabu Search, and Genetic Algorithms 
• A Clear and Simple Introduction to the Methodology of Neural Networks 

The book ends with a chapter on case studies that explain how these methods can be applied in real-world settings; an online repository of computer programs that can be downloaded from a website is also available.

The book was written for students and researchers in the fields ofengineering (industrial, electrical, and computer), computer science,operations research, management science, and applied mathematics.  Anattractive feature of this book is its accessibility to readers new to this topic.

Abhijit Gosavi is a researcher who works in the area of 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 academic conferences.

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.


"The statements of science are not of what is true and what is not true, but statements of what is known with different degrees of certainty." --- Richard Feynman