Robust Box Approach for Blood Supply Chain Network Design under Uncertainty: Hybrid Moth-Flame Optimization and Genetic Algorithm

In this paper, a blood supply chain network (BSCN) is designed to reduce the total cost of the supply chain network under demand and transportation costs. The network levels considered for modeling include blood donation clusters, permanent and temporary blood transfusion centers, major laboratory centers and blood supply points. Other goals included determining the optimal number and location of potential facilities, optimal allocation of the flow of goods between the selected facilities and determining the most suitable transport route to distribute the goods to customer areas in uncertainty conditions. This study addresses the issue of blood prishability from blood sampling to distribution to customer demand areas. Given that the model was NP-hard, the MFGO algorithm were used to solve the model with a priority-based solution. The results of the design of the experiments showed the high efficiency of the MFGO algorithm in comparison with the PSO algorithm in finding efficient solutions. Also, the mean of the objective function in robust approach is more than the one in the deterministic approach, while the standard deviation of the first objective function in the robust approach is less than the one in the deterministic approach at all levels of the uncertainty factor.