A drone-based logistics network for blood supplies: a genetic algorithm based on greedy search

In many countries, the accessibility of blood and blood products remains a challenge due to the complex supply chain, short product lifetime, fluctuating demand at the hospital level, and timely access. To address this challenge, some countries have implemented the use of drones to deliver blood products to remote healthcare facilities, resulting in improved availability and delivery times. To further improve the efficiency of blood supply logistics, this study proposes a drone-based logistics network for the transportation of blood groups from blood banks to hospitals. The proposed methodology employs a genetic algorithm, in combination with Greedy Search, to establish an optimal drone-based logistics network for blood supplies. The algorithm allocates blood requests to launching and refueling stations and determines the most efficient routes for the drones, with the objective of minimizing total logistics costs, including the construction costs of launching and refueling stations and the delivery costs between different locations. Moreover, the algorithm utilizes natural selection processes, such as selection, reproduction, and mutation, to converge toward a near-optimal solution. The results indicate that the proposed algorithm outperforms the CPLEX solver in terms of computational time for solving the proposed integer linear programming problem. Furthermore, the proposed methodology provides a near-optimal solution with a small gap between the CPLEX optimal and algorithm solutions, demonstrating its efficacy and potential for improving the delivery of blood products in a cost-effective manner.