The design of effective inventory control policies for models with stochastic demands and forecast updates that evolve dynamically over time is a fundamental problem in supply chain management. In particular, this has been a very challenging theoretical and practical problem, even for models with a very simple forecast update mechanism. In this work, we present new algorithms for this problem and present extensive computational results that demonstrate their empirical performance.

Our primary contribution to the study of this problem is a new policy iteration algorithm that yields a well-performing, computationally tractable approximation to the solution. In addition, we build on work of Levi et al. and extend their new Minimizing and Balancing policies for the problem. Furthermore, we perform an extensive computational investigation of all our new policies and compare their performance to the Myopic policy.