Abstract: To effectively coordinate forward and reverse logistics, taking advantage of the characteristics of low cost and less impact of ground traffic conditions of drones, a truck-drone joint pick-up and delivery model was built and a two-stage solution method was proposed, considering the payload-induced battery consumption and the simultaneous pick-up and delivery services. In the first stage, the “maximum weight judgment method” was proposed to construct the initial feasible solution, assign pickup or delivery vehicles for the goods, and determine the required number of trucks. In the second stage, an improved simulated annealing algorithm based on node characteristics was used to optimize the truck-drone path and minimize the total cost. The two stages were all implemented using Python programming. The simulation results show that the truck-drone joint pickup and delivery model can effectively reduce logistics cost compared with other modes. The “maximum weight judgment method” can significantly improve the convergence speed and effect of the algorithm.

Key words: truck-drone joint pick-up and delivery model, load-induced battery consumption, maximum weight judgment method

摘要： 为有效协调正逆向物流，利用无人机成本低、受地面交通状况影响少的特点，在无人机电池能耗随载重变化、同时取送货情形下，提出卡车-无人机联合取送货模式，构建两阶段求解方法。第1阶段，提出“最大重量判断法”构造初始可行解，为货物指派拾取或配送车辆并确定需要卡车数量；第2阶段，采用基于节点特征的改进模拟退火算法优化卡车-无人机路径，最小化总成本。两阶段均采用Python编程实现。仿真结果表明，提出的卡车-无人机联合取送货模式较其他模式可有效减少物流成本。“最大重量判断法”可显著提高算法收敛速度和效果。

关键词: 卡车-无人机联合取送货, 基于载重的电池消耗, 最大重量判断法