SEEM5320 Markov Decision Process

This course covers the fundamental concepts and theories of stochastic dynamic programming (Markov decision processes) and aims to give the central ideas of how they are applied to model and solve various problems in different contexts. We start with finite-stage models and then discuss infinite-stage models under both discounted return and average return criteria. Basic structural properties of models and computational methods will be introduced and explored. Various applications in supply chain management and other areas will be discussed.