Table of contents

  1. Front Matter
    Pages i-xxvii
  2. Abhijit Gosavi
    Pages 1-8
  3. Abhijit Gosavi
    Pages 9-13
  4. Abhijit Gosavi
    Pages 15-28
  5. Abhijit Gosavi
    Pages 29-45
  6. Abhijit Gosavi
    Pages 47-55
  7. Abhijit Gosavi
    Pages 277-285
  8. Abhijit Gosavi
    Pages 287-315
  9. Abhijit Gosavi
    Pages 409-431
  10. Abhijit Gosavi
    Pages 433-535
  11. Abhijit Gosavi
    Pages 537-538
  12. Back Matter
    Pages 539-554

About this book


Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization.

The book's objective is two-fold: (1) It examines 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) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work.
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 several 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.


Response surface methodology Simulation Stochastic Processes algorithms game theory linear optimization model modeling operations research optimization sets

Authors and affiliations

  1. 1.Department of Industrial EngineeringThe State University of New YorkBuffaloUSA

Bibliographic information