A Robust Optimization Model for Blood Supply Chain Network Design

The eternal need for humans blood as a crucial commodity is the main stimulus for the healthcare systems to provide efficient blood supply chains (BSCs) by which the requirements are satisfied at the maximum level. To have an efficient supply of blood, an appropriate planning for blood supply chain is of a challenge which requires more attention. In this paper, we address a mixed integer linear programming model for blood supply chain network design (BSCND) with the need for making both strategic and tactical decisions throughout a multiple planning period. A robust programming approach is devised to deal with inherent randomness in parameters of the model. To illustrate the usefulness of the model as well as its solution approach, it is tested into a set of numerical examples, and the sensitivity analysis is conducted. Finally, two performance criteria, i.e., the mean and standard deviation of constraint violations, under a number of random realizations are employed to evaluate the performance of both of the proposed robust and deterministic models. For all test problems, the results imply the domination of robust approach over the deterministic one.