Nicola Secomandi

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.

Repeatable for Credit.

This course introduces students to a structured approach for managing and valuing physical energy assets. The focus is on investment and operating decisions in the context of modern energy value chains in the ongoing transition toward net zero emissions. Students build and apply business analytics models of the physical assets to (i) develop a conceptual understanding of how operational levers and market and technical risks affect optimal choices and (ii) enhance managerial effectiveness in this space.