电子商务环境下考虑碳减排收益的物流车与无人机协同配送路径优化

Optimization of Collaborative Truck-Drone Delivery Routing Considering Carbon Abatement Benefits in E-Commerce Environment

摘要: 为应对电子商务快速发展带来的物流配送挑战，解决城市“最后一公里”配送难题，提出电商环境下兼顾环境效益和企业效益的物流车与无人机协同配送方法。针对电商物流配送时效性要求高、订单分散等特点，本研究以总成本最小为目标，构建考虑碳减排收益的物流车与无人机协同配送路径优化模型，提出改进的K-means聚类算法确定物流车停靠点，设计混合遗传–模拟退火算法求解模型。采用经典Solomon算例集进行仿真实验，验证算法性能，对比分析不同配送策略下的成本、车公里数与碳排放的表现。仿真结果显示，本研究所提出的算法具有较高的精度和计算速度，协同模式较传统物流车配送模式平均降低碳排放54.01%，减少车公里数56.67%，减少配送总成本4.65%。研究成果有助于提高电商末端配送时物流车的工作效率和经济性，为实现交通领域“双碳”目标提供了理论依据。

Abstract: To address last-mile delivery challenges arising from the rapid development of e-commerce and urban logistics, this study proposes a coordinated logistics trucks-unmanned aerial vehicles delivery method that optimizes both environmental sustainability and corporate profitability. Accounting for high timeliness requirements and geographically dispersed orders in e-commerce logistics, we develop a coordinated truck-drone delivery routing optimization model incorporating carbon abatement benefits is constructed. An improved K-means clustering algorithm is proposed to determine truck parking locations, and a hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm is designed to solve the model. Simulation experiments conducted using the classic Solomon benchmark dataset verify the algorithm’s performance and compare the cost, vehicle kilometers traveled, and carbon emissions across different delivery strategies. The simulation results demonstrate that the proposed algorithm achieves high computational accuracy and speed. Compared to traditional truck-only delivery, the coordinated approach reduces emissions by 54.01%, decreases vehicle-kilometers by 56.67%, and lowers total costs by 4.65% on average. These research findings contribute to enhancing the operational efficiency and economic benefits of logistic trucks in e-commerce last-mile delivery, while providing theoretical underpinnings for achieving the dual-carbon goals in the transportation sector.