  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.

Optimization and Control