Fix-and-optimize metaheuristics for minmax regret binary integer programming problems under interval uncertainty

The Binary Integer Programming problem (BIP) is a mathematical optimization problem, with linear objective function and constraints, on which the domain of all variables {0, 1}. In BIP, every variable associated determined cost coefficient. Minmax regret under interval uncertainty (M-BIP) generalization BIP in coefficient to not known advance, but are assumed be bounded by an interval. M-BIP find solution that possesses minimum maximum among possible solutions for problem. this paper, we show decision version Σ p 2 -complete. Furthermore, tackle both exact heuristic algorithms. We extend three algorithms from literature propose two fix-and-optimize Computational experiments, performed Weighted Set Covering Interval Uncertainties (M-WSCP) as test case, indicates one outperforms others. it shows proposed heuristics, can easily employed solve any minmax uncertainty, competitive ad-hoc M-WSCP.