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.
@article{arxiv.1908.05082,
title = {Algorithms the min-max regret 0-1 Integer Linear Programming Problem with Interval Data},
author = {Iago A. Carvalho and Thiago F. Noronha and Christophe Duhamel},
journal= {arXiv preprint arXiv:1908.05082},
year = {2019}
}
Comments
3 pages, 1 table. Published at Metaheuristics International Conference 2019