关键词: 低空经济, 卡车-无人机协同配送, 无人机起降策略, 自适应大邻域搜索算法

Abstract:

The “low-altitude economy” is a new and comprehensive economic model that has permeated multiple fields such as inspection, logistics, rescue, and communication. This paper mainly investigates a new delivery mode involving drones taking off and landing at truck delivery points, considering dynamic takeoff and landing at any point along the truck’s path to reduce the drone’s flight distance and waiting time. Additionally, multiple drones are introduced into the delivery system to enhance delivery efficiency. This paper focuses on a collaborative delivery path optimization problem with one truck and multiple drones, incorporating dynamic synchronization. The goal is to minimize the weighted total cost, and a path optimization model is developed for this purpose. An adaptive large neighborhood search algorithm is designed to solve the model, with a clear construction method for the algorithm’s initial solution and the functionality of each operator. The Metropolis criterion of the simulated annealing algorithm is used to avoid the algorithm from falling into local optima. The feasibility of the algorithm is verified through examples of various scales, and the results of drones taking off and landing at truck delivery points are compared with those of drones dynamically taking off and landing along the truck’s path. The weight relationships of various costs in the model and the number of drones in the delivery system are also analyzed. The results show that the dynamic takeoff and landing strategy for drones along the truck path optimizes the weighted total cost by 0.80% to 2.96%, and the dynamic synchronization mechanism is highly sensitive to changes in waiting costs. As the weight of waiting costs increases, the optimized delivery system’s weighted total cost gradually decreases. Meanwhile, increasing the waiting cost can, to some extent, improve the dynamic synchronization optimization degree. The highest degree of dynamic synchronization optimization was achieved when the system operated with two drones. The truck-drone dynamic synchronization scheme proposed in this paper can optimize the delivery paths for both trucks and drones, offering new insights into reducing the cost of urban last-mile logistics delivery.

Key words: low altitude economy, truck-drone collaborative delivery, drone take-off and landing strategy, adaptive large neighborhood search algorithm