Abstract: The rapid development of e-commerce forces the continuous transformation and upgrading of the logistics industry. In view of the fact that local governments encourage the development of public transport and advocate green and low-carbon logistics distribution mode, a distribution mode of bus-assisted drone is studied. After explaining the problem, a mathematical model with the lowest distribution cost is constructed, and a heuristic algorithm of smart general variable neighborhood search metaheuristic is designed to solve the problem. At the same time, in order to improve the efficiency of the algorithm, K-means clustering and greedy algorithm are introduced to generate the initial solution. Firstly, aiming at different scale examples, a variety of local search strategies and a variety of algorithms are compared to verify the effectiveness of the algorithm. Secondly, by selecting the standard CVRP as example, the single truck distribution mode and truck-drone collaborative distribution mode are compared with the distribution mode of bus-assisted drone to prove its cost and time advantages. Finally, Beijing Bus Rapid Transit Line 2 and its surrounding customer points are selected, and sensitivity analysis is made by changing the bus stop spacing and departure interval, result shows that the impact of increasing the stop spacing is greater than the change of departure interval.

Key words: urban logistics, bus-assisted drone, smart general variable neighborhood search, path optimization

摘要： 电子商务迅猛发展倒逼物流行业不断转型升级，针对各地政府鼓励公共交通发展，倡导绿色低碳的物流配送方式，研究了一种公交辅助无人机的配送模式。对问题做出说明后，构建了以配送成本最小的数学模型，并设计了智能通用变邻域搜索算法对问题求解，同时为提高算法求解效率，引入K-means分簇与贪婪算法生成初始解。针对不同规模算例，进行多种局部搜索策略、多种算法对比实验，验证了算法有效性；选取标准CVRP算例，将单卡车配送、卡车无人机协同配送与公交辅助无人机配送模式进行对比，证明其成本、时间优势；选取北京快速公交2号线及周边客户点，通过改变公交站点间距、发车间隔做出敏感度分析，实验结果证明增大站点间距的影响大于发车间隔的改变。

关键词: 城市物流, 公交辅助无人机, 智能通用变邻域搜索, 路径优化