A double oracle approach for minmax regret optimization problems with interval data

TL;DR

This paper introduces a double oracle algorithm for minmax regret optimization with interval data, providing a stronger lower bound and demonstrating improved computational efficiency in robust shortest path problems.

Contribution

It develops a generic double oracle method to compute tight lower bounds for minmax regret problems, enhancing solution efficiency and accuracy.

Findings

Lower bound is at least as accurate as previous methods

Significant reduction in computation times for robust shortest path

Effective integration into branch and bound procedures

Abstract

In this paper, we provide a generic anytime lower bounding procedure for minmax regret optimization problems. We show that the lower bound obtained is always at least as accurate as the lower bound recently proposed by Chassein and Goerigk (2015). This lower bound can be viewed as the optimal value of a linear programming relaxation of a mixed integer programming formulation of minmax regret optimization, but the contribution of the paper is to compute this lower bound via a double oracle algorithm (McMahan et al., 2003) that we specify. The double oracle algorithm is designed by relying on a game theoretic view of robust optimization, similar to the one developed by Mastin et al. (2015), and it can be efficiently implemented for any minmax regret optimization problem whose standard version is "easy". We describe how to efficiently embed this lower bound in a branch and bound procedure. Finally, we apply our approach to the robust shortest path problem. Our numerical results show a significant gain in the computation times compared to previous approaches in the literature.