Robust solutions to multi-objective linear programs with uncertain data

M.A. Goberna, V. Jeyakumar, G. Li and J. Vicente-Pérez

European Journal of Operational Research, 2015, vol. 242, issue 3, 730-743

Abstract: 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 the radius of robust feasibility guaranteeing constraint feasibility for all possible scenarios within a specified uncertainty set under affine data parametrization. We then present numerically tractable optimality conditions for minmax robust weakly efficient solutions, i.e., the weakly efficient solutions of the robust counterpart. We also consider highly robust weakly efficient solutions, i.e., robust feasible solutions which are weakly efficient for any possible instance of the objective matrix within a specified uncertainty set, providing lower bounds for the radius of highly robust efficiency guaranteeing the existence of this type of solutions under affine and rank-1 objective data uncertainty. Finally, we provide numerically tractable optimality conditions for highly robust weakly efficient solutions.

Keywords: Robust optimization; Multi-objective linear programming; Data uncertainty; Robust feasibility; Robust weakly efficient solutions (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (31)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:242:y:2015:i:3:p:730-743

DOI: 10.1016/j.ejor.2014.10.027

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