Inventory management of platelets along blood supply chain to minimize wastage and shortage

The demand uncertainty of blood platelet combined with its very short shelf life has led to a significant wastage of the total platelets collected from donors. On the other hand, there is a severe shortage of platelets along the blood supply chain due to the very limited donor pool. Therefore, it is necessary to develop proper inventory models for each member of the blood supply chain to minimize wastage and shortage of platelets. To achieve this objective, in this paper, a stochastic integer programming model under demand uncertainty is developed to determine ordering policies along the blood supply chain. The proposed mathematical model becomes computationally complex with the increase in the number of members of the supply chain and hence, a variant of the genetic algorithm, called Modified Stochastic Genetic Algorithm (MSGA), is proposed for large-sized problems. The performance of the MSGA is compared against the optimal solution from the mathematical model using a case study. Sensitivity analysis is conducted by changing the coefficient of demand variation, cost parameters and network size to analyze the ordering policies. Based on the results, hospitals and blood centers can choose the best ordering policy for their particular demand and cost setting.