Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty

We provide a set of instances for robust combinatorial optimization under budget uncertainty that have been described and used for benchmarking in the paper "A Branch &amp; Bound Algorithm for Robust Binary Optimization with Budget Uncertainty", published in Mathematical Programming Computation by Christina Büsing, Timo Gersing and Arie Koster.<br> To create this set of test instances, we use nominal instances from the MIPLIB 2017, which we transform into robust instances by defining robustness budgets Γ and deviations of the objective coefficients.<br> In total, 67 instances from the collection of the MIPLIB 2017 are considered.<br> For each of these, we provide data for constructing 12 corresponding robust instances by combining four different values for Γ with three different ranges for the deviation of the objective coefficients.