基于遗传算法的生鲜电商平台冷链逆向物流网络优化路径研究

Optimization Path Research of Fresh E-Commerce Platform’s Cold Chain Reverse Logistics Network Based on Genetic Algorithm

摘要: 冷链物流的发展背景源于全球食品、医药等行业对温控需求的不断提升，随着经济全球化及消费升级，生鲜农产品、冷冻食品、生物制剂等对温度敏感的货物运输量显著增长。冷链逆向物流网络中心选址优化是提升逆向物流效率、降低运营成本的关键问题。本文针对冷链产品在逆向物流过程中的温控需求、运输成本及设施选址约束，构建了一个以总成本最小化为目标的混合整数规划(MIP)模型，并结合某生鲜电商平台的案例数据设计了一种改进的遗传算法(GA)进行高效求解。该模型综合考虑了退货地址、区域逆向物流中心和再发货地址的多级网络结构，优化了设施选址、运输路径及冷链资源分配。实验结果表明，所提出的遗传算法在求解质量和计算效率上均表现出显著优势，能够有效降低系统总成本并提高逆向物流网络的稳定性。本研究不仅优化了冷链逆向物流的配送效率和网络结构，还为未来动态环境下的智能决策、低碳物流及多目标协同优化提供了研究基础。

Abstract: The development of cold chain logistics stems from the growing global demand for temperature control in industries such as food and pharmaceuticals. With economic globalization and consumption upgrading, the transportation volume of temperature-sensitive goods, including fresh agricultural products, frozen foods, and biologics, has increased significantly. The optimization of cold chain reverse logistics network center location selection is a critical issue for improving reverse logistics efficiency and reducing operational costs. This study addresses the temperature control requirements, transportation costs, and facility location constraints in the reverse logistics process of cold chain products by constructing a Mixed-Integer Programming (MIP) model aimed at minimizing total costs. An improved Genetic Algorithm (GA) is designed for efficient solution, incorporating case data from a fresh e-commerce platform. The model comprehensively considers a multi-level network structure comprising return points, regional reverse logistics centers, and redistribution locations, optimizing facility location, transportation routes, and cold chain resource allocation. Experimental results demonstrate that the proposed genetic algorithm exhibits significant advantages in both solution quality and computational efficiency, effectively reducing total system costs and enhancing the stability of the reverse logistics network. This research not only optimizes the distribution efficiency and network structure of cold chain reverse logistics, but also provides a foundation for future studies on intelligent decision-making, low-carbon logistics, and multi-objective collaborative optimization in dynamic environments.