5 papers
Robust optimization with belief functions
Marc Goerigk, Romain Guillaume, Adam Kasperski +1
In this paper, an optimization problem with uncertain objective function coefficients is considered. The uncertainty is specified by providing a discrete scenario set, containing p…
Optimal Scenario Reduction for One- and Two-Stage Robust Optimization
Marc Goerigk, Mohammad Khosravi
Robust optimization typically follows a worst-case perspective, where a single scenario may determine the objective value of a given solution. Accordingly, it is a challenging task…
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…