基于遗传算法的城市生鲜电商物流配送路径优化

Optimization of Urban Fresh E-Commerce Logistics Distribution Routes Based on Genetic Algorithms

摘要: 在生鲜电商快速发展背景下，电子商务评论中频繁出现“配送超时致食材变质”“高价值品类配送优先级低”“配送费与体验不匹配”等反馈，暴露出传统配送路径规划与消费者实际需求脱节的问题。本文以襄阳市A生鲜电商公司为研究对象，将电子商务评论中的核心诉求纳入优化体系，构建融合企业运营成本和消费者评论需求的多约束配送路径优化模型。通过遗传算法的编码方式与搜索策略，结合MATLAB仿真验证，结果表明优化后企业总成本降至793.3元，负面电商评论大幅减少，为生鲜电商企业减少负面评论、提升用户满意度提供了理论依据与实践指南。

Abstract: Amid the rapid expansion of fresh-food e-commerce, reviews increasingly cite “food spoilage caused by late delivery”, “low shipping priority for high-value items”, and “delivery fees that do not match the service experience”, revealing a growing disconnect between traditional route-planning models and consumers’ real needs. This study takes Company A, a fresh food e-commerce company in Xiangyang City, as the research subject, incorporating the core demands from e-commerce reviews into the optimization system to construct a multi-constraint delivery route optimization model that integrates both enterprise operating costs and consumer review requirements. By improving the encoding method and search strategy of the genetic algorithm and verifying it through MATLAB simulation, the results show that the optimized total enterprise cost decreased to 793.3 yuan, and negative e-commerce reviews were significantly reduced, providing theoretical support and practical guidance for fresh food e-commerce companies to reduce negative reviews and enhance user satisfaction.