Description
On this page, instances for the the MinMax selection problem under budgeted uncertainty set could be found. In addition, information with regard to the size of instances provided as well as an overall description of the considered method of instance generation is available. For more general purposes, the instance generator software is also accessible through a github repository. Finally, if more detail about theory or application of this method is desired, the main publication introducing this method could also be reached.
It must be noticed that in order to refer to the parameters of the robust selection problem, we use n for the number of items, p for the number of items we need to choose. Moreover, we use c_{i }as the nominal value of item i ∈ [n], d_{i }for its deviation and also Г for the parameter controlling how many items might deviate to its upper bound.
Method Description: For all i ∈ [n], we choose c_{i }uniformly as an integer number from {1, . . . , 10} and then choose d_{i} ∈ {100 − c_{i} − 1, . . . , 100}.
Instance Format
Here the instance set consists of problems with n = 40 when p = 20 and Γ ∈ {5, 10, 15, 20}. For each problem size, we generate 50 instances. Thus the instance set contains 200 instances. The instance files are named as “instance–n–p–Γ056000x”, where x represents the number of instance (1 ≤ x ≤ 50). In addition, each instance file contains three lines. The first line represents n, p and Γ the second and third lines show c_{i} and d_{i} for i ∈ [n], respectively.
Generator Software
Although it is a good idea to have a library of instances for the robust optimization problems, it is not possible to upload all possible combination of problem parameters on a website. Alternatively, the generator software could be accessed so that any instance size could be generated. Therefore, it is possible to access a C++11 code which is used as the generator software.
Reference
This page has been created based on the information provided in the following paper:

Goerigk, M., & Khosravi, M. (2022). Benchmarking Problems for Robust Discrete Optimization. arXiv preprint arXiv:2201.04985.