چكيده به لاتين
Platelet is the most expensive blood product with the complication of having an extremely short life span. The platelet transfusion has a broad range of usage in therapies. Therefore, providing an efficient plan for the management of platelet transfusion services is of great significance. Furthermore, the platelet supply chains are confronted with inevitable disruptions that jeopardize the network's efficient and effective performance. Previous studies in blood and platelet supply chain network design often follow proactive approaches in protecting the chain against disruptions. However, in many real-world situations, disruptions cannot be adequately measured in advance. Moreover, using disruptions in the designing phase through the proactive approaches impose high costs on the network since they cannot be updated based on unpredicted disruptions. This paper proposes a two-phase proactive-reactive approach by exploiting proactive and reactive strategies to handle uncertainties and disruptions in platelet supply chains. In the first step, called the proactive phase, a nominal platelet supply chain network is designed under operational uncertainty using the whole-blood collection method. In the event of disruptions, the second step, called the reactive phase, is applied, and the tailored network is updated based on the realized data, using apheresis as the collection mechanism. The operational risks are captured using a fuzzy programming approach in the model. The proposed approach is implemented in a real case of Iran, Fars province, and a series of sensitivity analyses are conducted. Besides, the proposed two-phase approach is compared with the commonly used proactive approaches in the literature. The results indicate that the proposed network can absorb the effects of disruptions efficiently and adapt its plan to enhance the performance of the network; thus, resulting in more resiliency for the network. Furthermore, the two-phase approach's performance is significantly improved compared with proactive approaches regarding the quantity of the shortage units and the supply chain's total cost.
