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

This book 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 dynamic programming, e.g., Reinforcement Learning (RL) or Approximate Dynamic Programming (ADP), and static optimization via Stochastic Adaptive Search, e.g.,  Simultaneous Perturbation and Meta-Heuristics, has made it possible to use simulation in conjunction with optimization techniques.  Some special features of the book include:

• An Accessible Introduction to Reinforcement Learning Techniques for Solving Markov Decision Processes (MDPs)
• A Step-by-Step Description of Stochastic Adaptive Search Algorithms, e.g., Simultaneous Perturbation, Simulated Annealing, Tabu Search, and Genetic Algorithms, for Static Simulation-Based Optimization 
• A Clear and Simple Introduction to the Methodology of Neural Networks
• A Gentle Introduction to Convergence Analysis of a Subset of Methods Enumerated Above
• A Clear Discussion on Dynamic Programing for Solving MDPs and Semi-MDPs (SMDPs)
• An In-Depth Treatment of RL Methods for SMDPs and Average Reward Problems
• Computer Programs

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