Benchmark Instances for Robust Combinatorial Optimization with Budgeted Uncertainty (Q6699060)

We provide test instances for robust combinatorial optimization with budget uncertainty in the objective function. The set contains nominal problems from the MIPLIB 2017 that have been converted into robust problems and instances of the robust knapsack problem. Both problem sets have been described and used for benchmarking in the paper A Branch Bound Algorithm for Robust Binary Optimization with Budget Uncertainty, published in Mathematical Programming Computation by Christina Bsing, Timo Gersing and Arie Koster. Furthermore, we provide instances for robust weighted matching on bipartite graphs and robust weighted independent set. The latter are based on graphs for the clique problem of the second DIMACS implementation challenge (1993). Both problem sets have been described and used for benchmarking in the paper Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty, presented at IPCO 2023 by the same authors. Paper A Branch Bound Algorithm for Robust Binary Optimization with Budget Uncertainty: https://doi.org/10.1007/s12532-022-00232-2 Paper Recycling Inequalities for Robust Combinatorial Optimization with Budget Uncertainty: https://doi.org/10.1007/978-3-031-32726-1_5 For algorithms solving these problems see: https://doi.org/10.5281/zenodo.7463371