Lehr- und Forschungsgebiet Diskrete Optimierung

Timo Gersing, M.Sc.

Research Assistant

Lehr und Forschungsgebiet Kombinatorische Optimierung

RWTH Aachen University

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Robust Binary Optimization

We provide all instances used in the computational study in the paper A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty by Christina Büsing, Timo Gersing and Arie Koster.
The instances are based on a list of 67 nominal instances from the collection of the MIPLIB 2017. For each nominal instance, we constructed 12 robust instances by combining four different values for Γ with three different ranges for the deviation of the objective coefficients. Together with the nominal instances, the robustness components, stating the value of Γ and the deviations of the objective coefficients for each instance, constitute our robust instances.
We also provide a list of computational results for all instances and algorithms tested in the paper.

last modified: 08/10/2021 - 14:26