目的 针对农村地区末端物流面临配送难度大、运营成本高及配送效率低等严峻挑战这一现实需求,构建一种以总配送成本最小化为目标的卡车-无人机协同配送路径优化模型,并基于该模型设计两阶段求解的优化算法框架。方法 第一阶段引入自适应大规模邻域搜索思想,设计混合遗传算法求解载重约束下的卡车路径问题。第二阶段提出自适应K-medoids聚类方法对客户点进行自适应聚类,确定无人机发射点并优化无人机配送路径,随后对卡车路径与无人机路径进行协同再优化,实现配送成本的最优控制。结果 通过不同规模算例的实验验证,结果表明,所提求解方法在优化性能及适用性方面均优于传统方法和CPLEX求解器。结论 本文构建了契合农村电商发展需求的高效末端物流体系,为降低配送成本与提升配送效率提供了理论支撑与实践指导。
Abstract
In response to the practical needs arising from severe challenges faced by last-mile logistics in rural areas, such as high delivery difficulty, substantial operating costs, and low delivery efficiency, the work aims to construct a truck-drone collaborative delivery route optimization model targeted at minimizing total delivery costs and design a two-stage optimization algorithm framework based on this model. In the first stage, a hybrid genetic algorithm incorporating adaptive large neighborhood search was designed to solve the truck route problem under load constraints. In the second stage, an adaptive K-medoids clustering method was proposed to perform adaptive clustering of customer nodes, determine drone launch nodes, and optimize drone delivery routes. Subsequently, collaborative re-optimization of truck routes and drone routes was conducted to achieve optimal control of delivery costs. Experimental results based on different-scale instances demonstrated that the proposed solution method outperformed traditional methods and the CPLEX solver in terms of optimization performance and universality. An efficient last-mile logistics system tailored to the development needs of rural e-commerce is developed in this study, offering theoretical underpinnings and practical guidelines for reducing distribution costs and enhancing distribution efficiency.
关键词
卡车-无人机协同配送 /
两阶段算法 /
自适应K-medoids聚类方法 /
协同优化
Key words
truck-drone collaborative delivery /
two-stage algorithm /
adaptive K-medoids clustering method /
collaborative optimization
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基金
国家社会科学基金一般项目（24FGLB055）; 辽宁省社会科学规划基金（L20CGL012）
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