Minmax regret combinatorial optimization problems with ellipsoidal uncertainty sets

We consider robust counterparts of uncertain combinatorial optimization problems, where the difference to the best possible solution over all scenarios is to be minimized. Such minmax regret problems are typically harder to solve than their nominal, non-robust counterparts. While current literature almost exclusively focuses on simple uncertainty sets that are either finite or hyperboxes, we consider problems with more flexible and realistic ellipsoidal uncertainty sets. We present complexity results for the unconstrained combinatorial optimization problem, the shortest path problem, and the minimum spanning tree problem. To solve such problems, two types of cuts are introduced, and compared in a computational experiment.