Abstract
A two-stage planning model for the carrier–vehicle problem with drone (CVP-D) is established in this paper, with the objective of minimizing the delivery time of the drone and the distance traveled by the truck while considering the impact of payload on the drone flight distance. Firstly, based on the customer coordinates, an improved K-Means ++ clustering algorithm is designed to plan the vehicle stopping points, and the vehicle departs from the warehouse to traverse all stopping points in order. Based on the vehicle stopping points, a multi-chromosome genetic algorithm is designed to optimize the vehicle driving path. Then, the drone route is optimized without considering the no-fly zone. Finally, the real data of Jiangsu Province are introduced as a case study to calculate the cost and total time required before and after improvement. The results showed an approximate savings of 16% in time and 19% in cost.
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