Abstract: Considering the regional restrictions imposed on vehicles and drones due to traffic congestion and air transport control, as well as the issues of prolonged delivery times and low efficiency in logistics distribution, a vehicle-drone cooperative delivery mode is proposed. This mode leverages the unique characteristics of both drones and vehicles. To address the challenge of vehicle-drone cooperative path planning under regional restrictions, a two-stage algorithm is developed with the primary aim of minimizing the total delivery time for vehicle-drone operations. In the first stage, an initial path is generated using the nearest neighbor algorithm, while the second stage employs an improved sub-heuristic algorithm to optimize the path. By integrating simulated annealing mechanisms into the genetic algorithm, the risk of falling into local optima is mitigated. Moreover, various crossover and mutation operators are designed to enhance the global optimization capability of the algorithm. The performance and superiority of the model and algorithm are robustly evaluated through comprehensive numerical experiments on a diverse set of examples. The results demonstrate that the proposed algorithm effectively solves the problem of vehicle-drone cooperative path planning under regional restrictions.
Keywords: vehicle-drone cooperation; restricted area; path planning; drone delivery for multi-customer; two-stage sub-heuristic algorithm(TSA)