6 papers
The complexity landscape of robust (integer) linear programming
Michael Poss, Jannis Kurtz, Marc Goerigk +1
We study the computational complexity of the decision versions of three classic robust optimization problems: static robust optimization, two-stage (adjustable) robust optimization…
The Complexity Landscape of Two-Stage Robust Selection Problems with Budgeted Uncertainty
Marc Goerigk, Dorothee Henke, Lasse Wulf
A standard type of uncertainty set in robust optimization is budgeted uncertainty, where an interval of possible values for each parameter is given and the total deviation from the…
An extension of Ordered Weighted Averaging over intervals with application to optimization under risk
Werner Baak, Marc Goerigk, Adam Kasperski +1
The Ordered Weighted Averaging (OWA) operator is a traditional and commonly used criterion for aggregating discrete values of uncertain quantities. In this paper, it is shown that…
A fast approximate column-and-constraint generation method for two-stage robust mixed-integer programs
Marc Goerigk, Dorothee Henke, Johannes Kager +2
This paper presents a new column-and-constraint generation method for two-stage robust mixed-integer programs with finite uncertainty sets. Our method combines and extends speed-up…
The robust selection problem with information discovery
Xiaoyu Chen, Marc Goerigk, Michael Poss
We explore a multiple-stage variant of the min-max robust selection problem with budgeted uncertainty that includes queries. First, one queries a subset of items and gets the exact…
Problem-Driven Scenario Reduction and Scenario Approximation for Robust Optimization
Jamie Fairbrother, Marc Goerigk, Mohammad Khosravi
In robust optimization, we would like to find a solution that is immunized against all scenarios that are modeled in an uncertainty set. Which scenarios to include in such a set is…