Simulation-based Optimization

"Simulation-Based Optimization: Parametric OptimizationTechniques and" "Reinforcement Learning" introduces the evolvingarea of simulation-based optimization. Since it became possible toanalyze random systems using computers, scientists and engineers havesought the means to optimize systems using simulation models. Onlyrecently, however, has this objective had success in practice.Cutting-edge work in computational operations research, includingnon-linear programming (simultaneous perturbation), dynamicprogramming (reinforcement learning), and game theory (learningautomata) has made it possible to use simulation in conjunction withoptimization techniques. As a result, this research has givensimulation added dimensions and power that it did not have in therecent past.The book's objective is two-fold: (1) It examines the mathematicalgoverning principles of simulation-based optimization, therebyproviding the reader with the ability to model relevant real-lifeproblems using these techniques. (2) It outlines the computationaltechnology underlying these methods. Taken together these two aspectsdemonstrate that the mathematical and computational methods discussedin this book do work.Broadly speaking, the book has two parts: (1) parametric (static)optimization and (2) control (dynamic) optimization. Some of thebook's special features are: This book is written for students and researchers in the fields ofengineering (electrical, industrial and computer), computer science, operations research, management science, and applied mathematics.