Collaborative Delivery Route Planning for Trucks and Drones Utilizing an Optimized Genetic Algorithm

Abstract

Collaborative delivery involving trucks and drones symbolizes one of the emerging trends in future logistics and distribution. This paper proposed a collaborative delivery model that integrated trucks and drones to address the path planning challenges for diverse scenarios. In detail, this model encompasses multifarious practical constraints, including vehicle load limits, drone flight range restrictions, and time window constraints. To explore optimized solution for this model, this paper developed an improved genetic algorithm, which integrated the simulated annealing algorithm with a large-scale neighborhood search approach to bolster algorithm performance. Two numerical case studies have validated the feasibility and efficiency of the proposed algorithm. The results show that the collaborative delivery model can substantially reinforce delivery efficiency, which brings about an average abatement in delivery cost by 13.61%. This research offers a brand new viewpoint and innovative methodology for optimizing urban logistics distribution systems.