Guaranteeing highly robust weakly efficient solutions for uncertain multi-objective convex programs

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

European Journal of Operational Research, 2018, vol. 270, issue 1, 40-50

Abstract: This paper deals with uncertain multi-objective convex programming problems, where the data of the objective function or the constraints or both are allowed to be uncertain within specified uncertainty sets. We present sufficient conditions for the existence of highly robust weakly efficient solutions, that is, robust feasible solutions which are weakly efficient for any possible instance of the objective function within a specified uncertainty set. This is done by way of estimating the radius of highly robust weak efficiency under linearly distributed uncertainty of the objective functions. In the particular case of robust quadratic multi-objective programs, we show that these sufficient conditions can be expressed in terms of the original data of the problem, extending and improving the corresponding results in the literature for robust multi-objective linear programs under ball uncertainty.

Keywords: Robustness and sensitivity analysis; Multi-objective optimization; Convex optimization; Robust optimization; Robust efficient solutions (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:270:y:2018:i:1:p:40-50

DOI: 10.1016/j.ejor.2018.03.018

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