A robust location-allocation model for optimizing a multi-echelon blood supply chain network under uncertainty

Designing and planning blood supply chains is very complicated due to its uncertain nature, such as uncertain blood demand, high vulnerability to disruptions, irregular donation, and blood perishability. In this vein, this paper seeks to optimize the cost and efficiency of a multi-echelon blood supply chain network under uncertainty by designing a robust location-allocation model. To this end, the magnitude of the earthquake as disaster intensity (destruction radius), affecting the severity of the disruption at the blood donation facilities' location, is considered. The Data Envelopment Analysis (DEA) model is mixed into the developed model to evaluate the efficiency of the locations and select high-efficient locations. On the other hand, this study aims to eliminate the gap between blood donors and consumers (patients) to balance the shortage and the wastage of blood units efficiently. As uncertainty plays a central role in the blood supply chain during an earthquake disaster, a specific robust optimization approach is utilized to handle the inherent uncertainty of the model. Finally, a case study of Mashhad city, Iran, is used to validate and show the applicability of the model and its solution approaches. The results showed that a flexible, efficient blood supply chain would be achievable by simultaneously setting an optimal trade-off between costs and blood shortage and wastage based on the decision maker's preferences.