Algorithms the min-max regret 0-1 Integer Linear Programming Problem with Interval Data

2019/08/14 by Iago A. Carvalho, Carvalho, Iago A., Thiago F. Noronha +3
Decision Sciences · Computer Science · #Risk and Portfolio Optimization #Bayesian Modeling and Causal Inference #Multi-Criteria Decision Making

paper · pdf · doi:10.48550/arxiv.1908.05082

Abstract

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.

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