Robust policies for a multi-period fleet sizing problem with demand uncertainty in the European Journal of Operational Research

After studying robust optimization (RO) for a while, we were able to publish our first paper about it in the European Journal of Operational Research. The paper is available online.

In particular, we define a multi-period fleet sizing problem motivated by a real-life case study, where we model uncertain demands with a cardinality-constrained uncertainty set. First, we provide lower- and upper-bounding formulations using also robust optimization. Then, tackle the problem as a Markov Decision Process with Approximate Dynamic Programming (ADP). We define myopic, policy function approximations, and robust look-ahead policies, which we compare on synthetic and actual instances. As far as we know, few works design ADP policies by exploiting RO techniques.