A New Model for Stochastic Facility Location Modeling

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 which minimizes the expected regret with respect to an endogenously selected subset of worst-case scenarios whose collective probability of occurrence is exactly 1-α. We demonstrate the effectiveness of this new approach by comparing it to the “α-reliable p-median Minimax regret” model and by presenting computation results for large-scale problems. We also present a heuristic, which involves solving a series of α-reliable Mean-excess regret sub-problems, for the α-reliable p-median Minimax regret model.