Friday March 13, 2020, 13:15
Lecture Hall BE01
Steyrergasse 30, ground floor
Marc Goerigk
University of Siegen
Two-Stage Robust Combinatorial Problems
I focus on combinatorial optimization problems under uncertainty. In the
most basic (robust) setting, there is a set of cost vectors given, and
the question is to find one solution such that the largest of the
possible objective functions is as small as possible. A variant of this
setting is two-stage robustness: Here, we only need to make a partial
decision in the first stage, under known first-stage costs. Then the
uncertain second-stage cost vector is revealed, and we can complete our
partial solution to a full solution. While one-stage robust
combinatorial problems have been studied quite thoroughly over the past
two decades, progress on two-stage problems has been only recent. I give
an accessible overview to those who are new to robust optimization, and
will also present some of these recent results to highlight where the
current research is at.