  We address the Interval Data Min-Max Regret 0-1 Integer Linear Programming
problem (MMR-ILP), a variant of the 0-1 Integer Linear Programming problem
where the objective function coefficients are uncertain. We solve MMR-ILP using
a Benders-like Decomposition Algorithm and two metaheuristics for min-max
regret problems with interval data. Computational experiments developed on
variations of MIPLIB instances show that the heuristics obtain good results in
a reasonable computational time when compared to the Benders-like Decomposition
algorithm.
