DOI：10.3969/j.issn.1672-3872.2024.21.042
作 者：徐 鹏 ，胡昌辉 （江西五十铃汽车有限公司，江西 南昌 330100）
摘 要：【目的】解决汽车制造企业零部件循环取货中存在的效率低下、成本高昂及应变能力不足等问题。【方法】提出了一种基于大数据分析与人工智能技术相结合的自适应路径规划方法。通过梳理汽车零部件供应链的基本特性，分析了循环取货车辆作业流程中的关键约束条件，详细介绍了数据预处理阶段及特征工程，并采用强化学习技术，结合深度学习算法的优点，设计出一种能够实时感知环境变化、自我学习与调整的智能路径规划系统。【结果】该自适应汽车零部件循环取货车辆智能路径规划方法可显著缩短取货周期，减少空驶率，提高车辆利用率；通过引入深度学习算法，实现了路径规划的自适应优化，增强了系统的智能性和灵活性，能动态响应市场变化，增强供应链弹性和灵活性；同时可减少燃料消耗，降低碳排放，促进绿色物流的发展。【结论】本研究对于提高汽车制造商供应链的效率、降低成本、提升客户满意度具有重要意义，且该自适应汽车零部件循环取货车辆智能路径规划方法可拓展应用于其他行业复杂的物流配送场景中。
关键词：循环取货；自适应；路径规划；深度学习算法
Research on Intelligent Path Planning Method for Self-Adaptive Vehicle for Circular Pickup of Automotive Parts
Xu Peng, Hu Changhui (Jiangxi Isuzu Automobile Co., Ltd., Jiangxi Nanchang 330100)
Abstract: [Objective] To solve the problems of low efficiency, high cost and insufficient adaptability in the circular pickup of parts in automobile manufacturing enterprises. [Method] A self-adaptive path planning method based on the combination of big data analysis and artificial intelligence technology is proposed. By sorting out the basic characteristics of the automotive parts supply chain, analyzing the key constraints in the operation process of circular pickup vehicles, detailing the data preprocessing stage and feature engineering, and using reinforcement learning technology combined with the advantages of deep learning algorithms, an intelligent path planning system capable of real-time perception of environmental changes, self-learning and adjustment is designed. [Result] The intelligent path planning method for adaptive automotive parts circular pickup vehicles can significantly shorten the pickup cycle, reduce the empty driving rate, and improve vehicle utilization. By introducing deep learning algorithms, adaptive optimization of path planning has been achieved, enhancing the intelligence and flexibility of the system, dynamically responding to market changes, and improving the resilience and flexibility of the supply chain. At the same time, it can reduce fuel consumption, lower carbon emissions, and promote the development of green logistics. [Conclusion] This study is of great significance for improving the efficiency of automobile manufacturers’ supply chains, reducing costs, and enhancing customer satisfaction. Moreover, the intelligent path planning method for adaptive automotive parts circular pickup vehicles can be extended to complex logistics and distribution scenarios in other industries.
Keywords: circular pickup; self-adaptive; path planning; deep learning algorithm
引文信息：[1]徐鹏，胡昌辉.自适应汽车零部件循环取货车辆智能路径规划方法研究[J].南方农机，2024，55(21)：160-164.
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