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.

Brings the field completely up to date All computer code brought up to date New material not covered in first edition includes nested partitions, simultaneous perturbation, backtracking adaptive search and the stochastic ruler method Includes supplementary material: sn.pub/extras

Autorentext

Abhijit Gosavi is a leading international authority on reinforcement learning, stochastic dynamic programming and simulation-based optimization. The first edition of his Springer book Simulation-Based Optimization that appeared in 2003 was the first text to have appeared on that topic. He is regularly an invited speaker at major national and international conferences on operations research, reinforcement learning, adaptive/approximate dynamic programming, and systems engineering.

He has published more than fifty journal and conference articles many of which have appeared in leading scholarly journals such as Management Science, Automatica, INFORMS Journal on Computing, Machine Learning, Journal of Retailing, Systems and Control Letters and the European Journal of Operational Research. He has also authored numerous book chapters on simulation-based optimization and operations research. His research has been funded by the National Science Foundation, Department of Defense, Missouri Department of Transportation, University of Missouri Research Board and industry. He has consulted extensively for the U.S. Department of Veterans Affairs and the mass media as a statistical/simulation analyst. He has received teaching awards from the Institute of Industrial Engineers.

He currently serves as an Associate Professor of Engineering Management and Systems Engineering at Missouri University of Science and Technology in Rolla, MO. He holds a masters degree in Mechanical Engineering from the Indian Institute of Technology and a Ph.D. in Industrial Engineering from the University of South Florida. He is a member of INFORMS, IIE and ASEE.

Inhalt

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.