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.
