Simulation-Based Optimization

Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning

introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are
model-free
optimization techniques especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms.

Key features of this revised and improved Second Edition include:

· Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms)

· Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming(value and policy iteration) for discounted, average, and total reward performance metrics

· An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning:
Q
-
Learning
,
SARSA
, and
R-SMART
algorithms, and policy search, via
API
,
Q
-
P
-
Learning
, actor-critics, and learning automata

· A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations

Themed around three areas in separate sets of chapters
Static Simulation Optimization, Reinforcement Learning
and
Convergence Analysis

this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.

Background. - Simulation basics. - Simulation optimization: an overview. - Response surfaces and neural nets. - Parametric optimization. - Dynamic programming. - Reinforcement learning. - Stochastic search for controls. - Convergence: background material. - Convergence: parametric optimization. - Convergence: control optimization. - Case studies.