A Cardinality-Constrained Robust Approach for the Biomedical Samples Laboratories Network Problem

Biomedical Samples Laboratories (BSLs) are fundamental in modern healthcare systems. Thus, logistic problems related to design, management and optimization of BSL networks are emerging. Uncertainty in the demand for tests has a relevant impact on the quality of the solution and should be considered. However, in the literature, this feature has not been adequately studied so far. We consider the BSL Network Problem and propose a cardinality-constrained robust model for decision making under demand uncertainty. Robust solutions evaluation through real-life scenarios shows better feasibility performances than those of its deterministic counterpart. Solution cost increase is limited and affordable.