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Deriving robust and globalized robust solutions of uncertain linear programs with general convex uncertainty sets

Bram L. Gorissen (b.l.gorissen***at***tilburguniversity.edu)
Aharon Ben-Tal (abental***at***ie.technion.ac.il)
Hans Blanc (J.P.C.Blanc***at***tilburguniversity.edu)
Dick Den Hertog (D.denHertog***at***tilburguniversity.edu)

Abstract: We propose a new way to derive tractable robust counterparts of a linear program by using the theory of Beck and Ben-Tal (2009) on the duality between the robust (``pessimistic'') primal problem and its ``optimistic'' dual. First, we obtain a new {\it convex} reformulation of the dual problem of a robust linear program, and then show how to construct the primal robust solution from the dual optimal solution. Our result allows many new uncertainty regions to be considered. We give examples of tractable uncertainty regions that were previously intractable. The results are illustrated by solving a multi-item newsvendor problem. We also apply the new method to the globalized robust counterpart scheme and show its tractability.

Keywords: robust optimization; general convex uncertainty regions; linear optimization

Category 1: Robust Optimization

Citation: B. L. Gorissen, A. Ben-Tal, J. P. C. Blanc and D. den Hertog. Deriving robust and globalized robust solutions of uncertain linear programs with general convex uncertainty sets. Operations Research, 62(3), 672-679, 2014.

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Entry Submitted: 10/16/2012
Entry Accepted: 10/16/2012
Entry Last Modified: 07/24/2015

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