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.