@article{LI2026, 
author = {Ye LI and Xinrui LUO and Xuri YIN and Yansheng CHEN},
title = {Optimization of Truck-Drone Collaborative Delivery Route Considering Dynamic Synchronization},
year = {2026},
journal = {Journal of South China University of Technology (Natural Science Edition)},
volume = {54},
number = {6},
pages = {1-11},
keywords = {low-altitude economy, truck-drone collaborative delivery, drone take-off and landing strategy, adaptive large neighborhood search algorithm},
url = {https://www.sciopen.com/article/10.12141/j.issn.1000-565X.250275},
doi = {10.12141/j.issn.1000-565X.250275},
abstract = {As a novel comprehensive economic paradigm, “low-altitude economy” has permeated various fields such as inspection, logistics, rescue, and communications. This paper focuses on a novel collaborative delivery mode involving drones and trucks, where drones are allowed to dynamically take off and land at arbitrary points along the truck’s route to reduce waiting time. Multiple drones are introduced into the delivery system to enhance overall efficiency. Specifically, the study addresses the truck--multi-drone collaborative delivery routing problem with dynamic synchronization, aiming to minimize the weighted total cost. A path optimization model is developed for this purpose. An adaptive large neighborhood search algorithm is designed to solve the model, with detailed descriptions of the initial solution construction method and the functions of various operators. The Metropolis criterion from simulated annealing algorithm is used to prevent the algorithm from converging to local optima. Experiments using instances of different scales verify the feasibility of the algorithm. Comparisons are made between scenarios where drones take off and land only at truck delivery points and those where drones dynamically operate along the truck’s route. Analyses are conducted on the weighting relationships among different cost components in the model and the impact of varying the number of drones in the system. The results show that the dynamic takeoff and landing strategy for drones along the truck path reduces 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 weighted total cost gradually decreases, and increasing the waiting cost can improve the degree of optimization achieved through dynamic synchronization to some extent. The highest level of dynamic synchronization optimization is observed when two drones are deployed in the system. The proposed truck-drone dynamic synchronization scheme can optimize delivery routes for both trucks and drones, offering a new perspective for reducing last-mile logistics costs in urban areas.}
}