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
non-linear programming (simultaneous perturbation), dynamic
programming (reinforcement learning), and game theory (learning
automata) has made it possible to use simulation in conjunction
with optimization techniques. As a result, this research has given
simulation added dimensions and power that it did not have in the
recent past.
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. This book is written for students and researchers in
the fields of engineering (electrical, industrial and computer),
computer science, operations research, management science, and
applied mathematics.
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