Blood inventory-routing problem under uncertainty

The problem of delivering blood products from community blood centers to the demand points including hospital blood banks falls within the context of perishable inventory-routing problems (PIRP). This is due to the fact that the delivery should be made on the right time with the right delivery quan tity at the right place such that the total possible perished items as well as routing and inventory costs are minimized. However, some unique characteristics of blood logistics including assigned and unassigned inventories, crossmatch release period, transfusion to crossmatch ratio and older-first policy have made the problem more difficult than the routine PIRPs and thus proposing a new modeling of the problem is required. In this paper, we first develop a mixed integer programming formulation for blood inventory-routing problem. Then, to cope with uncertainties, a novel robust possibilistic programming (RPP) approach is proposed. Afterward, a novel iterative branch-and-cut is developed to solve a number of numerical examples to optimality. Finally, by implementing a test scenario on the data inspired from a real Iranian blood supply chain, the significance and applicability of the proposed model and RPP approach is proven.