Datasets for the healthcare social enterprise location problem

Solving complex multi-objective healthcare social enterprise location problems requires suitable metaheuristics capable of solving the problems. Moreover, data are required to conduct computational experiments to compare the metaheuristics. Due to a lack of benchmark data pertaining to healthcare social enterprise location problems in the literature, a synthetic problem instance generator was developed in Python to create reproducible synthetic problems. Four synthetic datasets were generated using a synthetic problem instance generator. The purpose of the synthetic datasets is mainly to compare metaheuristics for solving healthcare social enterprise location problems and then employ the best metaheuristic on real-world data in order to select suitable locations for a healthcare social enterprise based in South Africa.