Recoverable Robust Representatives Selection Problems with Discrete Budgeted Uncertainty

Activity: Talk or presentationTalk at workshop, seminar or courseScience to science


We analyze recoverable robust optimization for a class of selection problems. The aim is to choose a fixed number of items (the "representatives") from several disjoint sets, such that the worst-case costs after taking a recovery action are as small as possible. The uncertainty is modeled as a discrete budgeted set, where the adversary can increase the costs of a fixed number of items. While special cases of this problem have been studied before, its complexity has remained open since it was first mentioned in 2011 as an open problem in the PhD thesis of Büsing [1]. We make several contributions towards closing this gap. We show that the problem is NP-hard and identify a special case that remains solvable in polynomial time. We also provide a compact mixed-integer programming formulation.
Period1 Oct 2020
Event title2021 Robust Optimization Webinar
Event typeSeminar
LocationvirtuellShow on map
Degree of RecognitionInternational

Fields of Expertise

  • Information, Communication & Computing