PRAGMATIC DISTRIBUTIONALLY ROBUST OPTIMIZATION FOR SIMPLE INTEGER RECOURSE MODELS.

Inspired by its success for their continuous counterparts, the standard approach to deal with mixed-integer recourse (MIR) models under distributional uncertainty is to use distribu- tionally robust optimization (DRO). We argue, however, that this modeling choice is not always justified since DRO techniques are generally computationally challenging when integer decision variables are involved. That is why we propose an alternative approach for dealing with distributional uncertainty for the special case of simple integer recourse (SIR) models, which is aimed at obtaining models with improved computational tractability. We show that such models can be obtained by pragmatically selecting the uncertainty set. Here, we consider uncertainty sets based on the Wasserstein distance and also on generalized moment conditions. We compare our approach with standard DRO both numerically and theoretically. An important side result of our analysis is the derivation of performance guarantees for convex approximations of SIR models. In contrast to the literature, these error bounds are not only valid for a continuous distribution but hold for any distribution. [ABSTRACT FROM AUTHOR]