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.
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.