A Hybrid Genetic Algorithm on Routing and Scheduling for Vehicle-Assisted Multi-Drone Parcel Delivery

In recent years, the unmanned aerial vehicles (UAVs) have exhibited significant market potential to greatly reduce the cost and time in the field of logistics. The use of UAVs to provide commercial courier has become an emerging industry, remarkably shifting the energy use of the freight sector. However, due to limited battery capacities, the flight duration of civilian rotorcraft UAVs is still short, hindering them from performing remote jobs. In this case, people customarily utilize ground vehicles to carry and assist UAVs in various applications, including cargo delivery. Most previous studies on vehicle-drone cooperative parcel delivery considered only one UAV, thereby suffering from low efficiency when serving a large number of customers. In this paper, we propose a novel hybrid genetic algorithm, which supports the cooperation of a ground vehicle and multiple UAVs for efficient parcel delivery. Our routing and scheduling algorithm allows multiple UAVs carried by the vehicle to simultaneously deliver multiple parcels to customers residing in different locations. The proposed algorithm consists of a pipeline of several modules: population management, heuristic population initialization, and population education. The performance evaluation results show that the proposed algorithm has significant efficiency over existing algorithms.