The minmax regret problem for combinatorial optimization under uncertainty can be viewed as a zero-sum game played between an optimizing player and an adversary, where the optimizing player selects a solution and the adversary selects costs with the intention of maximizing the regret of the player. Existing minmax regret models consider only deterministic solutions/strategies, and minmax regret versions of most polynomial solvable problems are NP-hard. In this paper, we consider a randomized model where the optimizing player selects a probability distribution (corresponding to a mixed strategy) over solutions and the adversary selects costs with knowledge of the player's distribution, but not its realization. We show that under this randomized model, the minmax regret version of any polynomial solvable combinatorial problem becomes polynomial solvable. This holds true for both the interval and discrete scenario representations of uncertainty. We also show that the maximum expected regret value under the randomized model is upper bounded by the regret under the deterministic model.
Mastin, Jaillet, and Chin REF consider a randomized version of the minmax (additive) regret model.
Randomized Minmax Regret for Combinatorial Optimization Under Uncertainty
ArXiv
ArXiv
Mathematics Computer Science