10 citations · 42 across the 47 of their papers we have counts for
6 papers · 1 filter
On Scenario Aggregation to Approximate Robust Optimization Problems
Marc Goerigk, André Chassein
As most robust combinatorial min-max and min-max regret problems with discrete uncertainty sets are NP-hard, research into approximation algorithm and approximability bounds has be…
Compromise Solutions for Robust Combinatorial Optimization with Variable-Sized Uncertainty
André Chassein, Marc Goerigk
In classic robust optimization, it is assumed that a set of possible parameter realizations, the uncertainty set, is modeled in a previous step and part of the input. As recent wor…
Improvable Knapsack Problems
Marc Goerigk, Yogish Sabharwal, Anita Schöbel +1
We consider a variant of the knapsack problem, where items are available with different possible weights. Using a separate budget for these item improvements, the question is: Whic…
Variable-Sized Uncertainty and Inverse Problems in Robust Optimization
André Chassein, Marc Goerigk
In robust optimization, the general aim is to find a solution that performs well over a set of possible parameter outcomes, the so-called uncertainty set. In this paper, we assume…
Min-Max Regret Problems with Ellipsoidal Uncertainty Sets
A. Chassein, M. Goerigk
We consider robust counterparts of uncertain combinatorial optimization problems, where the difference to the best possible solution over all scenarios is to be minimized. Such min…
A biobjective approach to robustness based on location planning
Emilio Carrizosa, Marc Goerigk, Anita Schöbel
Finding robust solutions of an optimization problem is an important issue in practice, and various concepts on how to define the robustness of a solution have been suggested. The i…