A robust optimization approach for multi-objective linear programming under uncertainty

This paper proposes a new robust optimization approach for solving multi-objective linear programming problems under uncertainty. The uncertainty is assumed to be in the objective function coefficients and the constraint parameters. The proposed approach is based on an alternative model for obtaining robust efficient solutions to the original problem. A numerical example is given to test and illustrate the effectiveness of the proposed approach, and a comparison with a method given in the literature is discussed based on certain performance metrics.