Description
On this page, instances for the the MinMax Regret selection problem under discrete 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 link to our 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 and N for the number of scenarios. Moreover, we use c_{ij }to show the cost of item i in scenario j.
Method Description: For all i ∈ [n] and j ∈ [N], if i ≤ [n/2], we choose c_{ij} ∈ {1, . . . , 100} iid uniformly; otherwise, we set c_{ij }= 100 − c_{(i[n/2])j}.
Instance Format
The instance set here consists of four datasets. In dataset1 all inputs change, which means that for parameters (N, n, p), we consider combinations (30, 30, 15), (40, 40, 20) and (40, 40, 21). In dataset2 we fix n = N = 30 but change p ∈ {10, 11, 15, 20, 21}. In dataset3 we fix n = 30 and p = 15 but change N ∈ {20, 30, 40}. Finally, dataset4 has a similar setup as dataset3, but we consider largescale problems with N ∈ {100, 200, 500, 1000, 2000, 5000}. Then, for each parameter choice, we generate 50 instances. Thus there are 150 instances in dataset1, 250 instances in dataset2, 150 instances in dataset3 and 300 instances in dataset4. These instances are named as “instancenpN03–6000x” where x represents the number of instance (1 ≤ x ≤ 50). In addition, each instance file contains N+1 lines. The first line represents n, p, N and the remaining lines forms N given scenarios, including n item costs.
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.