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Robust Solutions to Multi-Objective Linear Programs with Uncertain Data

In this paper we examine multi-objective linear programming problems in the face of data uncertainty both in the objective function and the constraints. First, we derive a formula for radius of robust feasibility guaranteeing constraint feasibility for all possible uncertainties within a specified uncertainty set under affine data parametrization. We then present a complete characterization of robust weakly effcient solutions that are immunized against rank one objective matrix data uncertainty. We also provide classes of commonly used constraint data uncertainty sets under which a robust feasible solution of an uncertain multi-objective linear program can be numerically checked whether or not it is a robust weakly efficient solution.