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

Abhijit Gosavi (Author)

Book Description

Series: Operations Research/Computer Science Interfaces Series | Publication Date: June 30, 2003

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning 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 (Reinforcement Learning or Approximate Dynamic Programming) and static optimization (Simultaneous Perturbation and Meta-Heuristics), has made it possible to use simulation in conjunction with optimization techniques.  As a result, this research has given simulation-based optimization added dimensions and power that it did not have in the past. The book's objective is two-fold: (1) To examine 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) To outline the computational technology underlying these techniques.

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 numerous 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.
• An in-depth treatment of semi-Markov control and the average reward (cost) case.

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.

Product Details

• Hardcover: 584 pages
• Publisher: Springer; 1 edition (June 30, 2003)
• Language: English
• ISBN-10: 1402074549
• ISBN-13: 978-1402074547
• Product Dimensions: 9 x 7.1 x 1.3 inches
• Shipping Weight: 2.1 pounds (View shipping rates and policies)
• Amazon Best Sellers Rank: #2,515,610 in Books (See Top 100 in Books)

More About the Author

Dr. Abhijit Gosavi is the author of the Springer book "Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning." An errata for this book and other reference materials can be found at: web.mst.edu/~gosavia/book.html

The book presents numerous topics on simulation-based optimization, including dynamic simulation optimization, via reinforcement learning (or approximate dynamic programming), and static simulation optimization, via simultaneous perturbation and meta-heuristics. The book presents an in-depth treatment of semi-Markov and average cost control.