报废汽车逆向物流的建模与优化研究
Modeling and Optimization of Reverse Logistics for End-of-Life Vehicles

随着全球汽车数量的增长，报废汽车的管理与回收问题变得愈加紧迫。现有的逆向物流网络在效率和成本控制方面仍存在诸多瓶颈，如流程复杂、资源配置不当及高昂的再制造成本。为解决这些问题，本文提出了一个多层次供应链网络均衡优化模型，将变分不等式应用于报废汽车逆向物流中，系统刻画了供应链各方的利益博弈。通过基于多尺度模型学习算法的求解策略，有效解决了非线性和非凸性问题，显著提升了资源配置合理性和物流网络的运行效率。实验结果表明，该模型不仅降低了成本，还具备高效的实际应用价值，同时综合考虑了再制造、再循环等多种资源回收方式，增强了模型的灵活性与可持续性。该研究为实现报废汽车逆向物流的经济效益与环境可持续性提供了新的路径。
With the increase in the number of cars in the world, the management and recycling of end-of-life vehicles has become more urgent. The existing reverse logistics network still has many bottlenecks in efficiency and cost control, such as complicated process, improper allocation of resources and high remanufacturing cost. To solve these problems, a multi-level supply chain network equilibrium optimization model is proposed in this paper. Variational inequality is applied to reverse logistics of scrapped vehicles for the first time, and the interesting game of all parties in the supply chain is described systematically. Through the solution strategy based on multi-scale model learning algorithm, the nonlinear and non-convex problems are effectively solved, and the rationality of resource allocation and the operation efficiency of logistics network are significantly improved. The experimental results show that the model not only reduces the cost, but also has efficient practical application value. At the same time, it takes into account a variety of resource recovery methods such as remanufacturing and recycling, which enhances the flexibility and sustainability of the model. This study provides a new way to realize the economic benefit and environmental sustainability of reverse logistics of scrapped vehicles.