The α-reliable mean-excess regret model for stochastic facility location modeling

In this paper, we study a strategic facility location problem under uncertainty. The uncertainty associated with future events is modeled by defining alternative future scenarios with probabilities. We present a new model called the α-reliable meanexcess model that minimizes the expected regret with respect to an endogenously selected subset of worst-case scenarios whose collective probability of occurrence is no more than 1-α. Our mean-excess risk measure is coherent and computationally efficient. Computational experiments also show that the α-reliable mean-excess criterion matches the α-reliable minimax criterion closely.