Cooperative Learning-Based Joint UAV and Human Courier Scheduling for Emergency Medical Delivery Service

Emergency medical delivery plays a crucial role in ensuring timely treatment for patients under the promising trend of medical resource sharing. However, traditional human courier based delivery modes face many problems, such as high costs, slow speeds, and uncertain arrival times due to traffic conditions. To address these issues, this paper proposes leveraging heterogeneous Unmanned Aerial Vehicles (UAVs) and human couriers for emergency medical delivery, and develops a Cooperative Deep Reinforcement Learning (DRL) based method for real-time joint scheduling. The problem is modelled as a multi-depot capacitated pickup and delivery problem with soft deadlines. A DRL-based method is proposed with two types of agent networks for UAVs and human couriers, respectively, which could capture their distinct features and delivery strategies. In addition, a cooperative network is introduced to coordinate their operations. Extensive computational experiments and a real-life case study are conducted that verifies the superiority of our methods over several benchmark algorithms in different scenarios, and demonstrates its feasibility and performance in practical scenarios.