MARKOV DECISION PROCESSES IN OPERATIONS MANAGEMENT

Credit Hours: 3 · Graduate

This course introduces students to Markov Decision Processes, which are models of decision making under uncertainty that play a foundational role in operations management. Topics comprise the optimality conditions (Bellman equations), algorithms to obtain optimal policies, including dynamic programming, and both reinforcement learning approximations and bounding approaches to deal with intractable models.