Last-mile Delivery Using Drones in the Healthcare Supply Chain Management

Healthcare supply chain management has many challenges, especially when it comes to last-mile delivery, where rising costs, environmental issues, and complexity are big problems. The study demonstrates that the use of drone technology can address fundamental logistical problems with the last-mile delivery of healthcare items. This innovative technology enables faster, more affordable, and more sustainable last-mile delivery options, which is a major benefit. Furthermore, by effectively delivering essential supplies during emergencies, drones improve community resilience, especially in disaster-stricken areas where traditional transportation is limited. The objective of our work is to enhance the efficiency of last-mile medical drone delivery with the consideration of a drone battery constraint such as limitation of drone battery life and charging time. Therefore, our study is concerned with ensuring higher demand satisfaction rates and decreasing battery degradation rates by improving recharging and distribution operations in the drone hub dispatching drones to hospitals that generate stochastic demands and are located at different geographic locations. To achieve this objective, Discrete Event Simulation methodology is used to model and analyze complex, stochastic systems by tracking discrete events over time. The simulation was based on a real-world case study of Zipline's drone-based medical supply delivery system across Rwanda, Africa. By implementing a DES model, two different operational scenarios and some experimental analyses were compared to identify the most advantageous approaches to using drones and recharging them. Regarding different scenarios, we discovered that they behave in the same way and there is no significant improvement in the second scenario compared to the first scenario. Concerning various experiments about charging strategies, it has been found that it is most effective to recharge a drone when the battery threshold is 24% or less immediately after returning the drone from the mission. Furthermore, we analyzed the number of drones and the results showed that a fleet size of 30-35 can guarantee a high level of demand satisfaction rate besides lowering the number of drone charging cycles. Notably, the findings of this study highlight how the stochastic nature of healthcare demands and the limitation of drone battery life can be addressed when formulating strategies for using drones in last-mile delivery in healthcare logistics. Moreover, by creating a strategic methodology for effective drone hub operations and concentrating on charging and allocation strategies that best suit the stochastic demands of healthcare delivery and the limitation of drone battery life, this thesis makes a significant contribution to the field of last-mile delivery in healthcare supply chain research. This approach improves operational efficiency as well as resource utilization.