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Recently, drones, have been utilized in many real-life applications including healthcare services. For example, providing medical supplies, blood samples, and vaccines to people in remote areas or during emergencies. In this study, the maximum coverage facility location problem with drones (MCFLPD) was studied. The problem is the application of drones in the context of the facility location and routing. It involves selecting the locations of drone launching centers, which maximizes patient service coverage within certain drone range constraints. In this study, a heuristic named the maximum coverage greedy randomized heuristic (MCGRH) is developed. The idea of the algorithm is to first choose some facilities to open at random from among those that can handle the most weight of the patient demands. After that, patients are assigned to the closest opened facility with the capacity to serve them. Finally, drones are assigned to patients based on the least amount of battery consumed between the patient and the facility. Extensive testing of MCGRH indicated that it ranks efficiently alongside other methods in the literature that tried to solve the MCFLPD. It was able to achieve a high coverage of patients (more than 80% on average) within a very fast processing time (less than 1 s on average).

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Unmanned Aerial Vehicles (UAVs) play crucial roles in numerous applications, such as healthcare services. For example, UAVs can help in disaster relief and rescue missions, such as by delivering blood samples and medical supplies. In this work, we studied a problem related to the routing of UAVs in a healthcare approach known as the UAV-based Capacitated Vehicle Routing Problem (UCVRP). This is classified as an NP-hard problem. The problem deals with utilizing UAVs to deliver blood to patients in emergency situations while minimizing the number of UAVs and the total routing distance. The UCVRP is a variant of the well-known capacitated vehicle routing problem, with additional constraints that fit the shipment type and the characteristics of the UAV. To solve this problem, we developed a heuristic known as the Greedy Battery—Distance Optimizing Heuristic (GBDOH). The idea was to assign patients to a UAV in such a way as to minimize the battery consumption and the number of UAVs. Then, we rearranged the patients of each UAV in order to minimize the total routing distance. We performed extensive experiments on the proposed GBDOH using instances tested by other methods in the literature. The results reveal that GBDOH demonstrates a more efficient performance with lower computational complexity and provides a better objective value by approximately 27% compared to the best methods used in the literature.