Robust Counterparts of Inequalities Containing Sums of Maxima of Linear Functions
CentER Discussion Paper Series No. 2011-115
28 Pages Posted: 26 Oct 2011 Last revised: 8 Dec 2011
Date Written: October 25, 2011
This paper addresses the robust counterparts of optimization problems containing sums of maxima of linear functions and proposes several reformulations. These problems include many practical problems, e.g. problems with sums of absolute values, and arise when taking the robust counterpart of a linear inequality that is affine in the decision variables, affine in a parameter with box uncertainty, and affine in a parameter with general uncertainty. In the literature, often the reformulation that is exact when there is no uncertainty is used. However, in robust optimization this reformulation gives an inferior solution and provides a pessimistic view. We observe that in many papers this conservatism is not mentioned. Some papers have recognized this problem, but existing solutions are either too conservative or their performance for different uncertainty regions is not known, a comparison between them is not available, and they are restricted to specfic problems. We provide techniques for general problems and compare them with numerical examples in inventory management, regression and brachytherapy. Based on these examples, we give tractable recommendations for reducing the conservatism.
Keywords: robust optimization, sum of maxima of linear functions, biffine uncertainty
JEL Classification: C61
Suggested Citation: Suggested Citation