交通物流大模型在物流运输路径优化中的应用研究

Research on the Application of Large Models for Transportation and Logistics in Route Optimization of Logistics Transportation

摘要: 贵州因多山地形以至于出现物流运输效率低、成本高等问题。本文从现代管理工程与供应链协同的视角出发，提出了一种基于开源大语言模型(Qwen2)微调的智能化物流运输动态决策支持框架。系统通过整合全域多源异构数据，引入卡尔曼滤波算法对复杂山区环境下的定位漂移进行动态补偿；同时，借助联邦学习机制在保障跨企业核心数据隐私与安全的前提下实现多主体联合建模。在核心算法中，采用旋转位置编码(RoPE)与SwiGLU激活函数，提升了模型对长序列路径规划和非线性多目标决策的表征能力。微调后的模型可按需输出差异化调度方案，综合方案能够实现效率、成本、碳排放与运行稳定性的最优，有效适配山区复杂物流调度需求，可为山区智能物流调度与多式联运提质降本提供理论支撑和实践方案。

Abstract: Because Guizhou is so mountainous, it faces issues like low logistics efficiency and high costs. This article looks at the problem from the perspective of modern management engineering and supply chain collaboration and proposes an intelligent logistics dynamic decision-support framework based on fine-tuning an open-source large language model (Qwen2). The system integrates diverse data from across the region, uses the Kalman filter algorithm to dynamically correct positioning drift in complex mountain environments, and leverages a federated learning approach to enable multi-party collaborative modeling while keeping core enterprise data private and secure. At the core of the algorithm, it uses Rotary Positional Encoding (RoPE) and the SwiGLU activation function to improve the model’s ability to handle long-sequence route planning and nonlinear multi-objective decision-making. The fine-tuned model can output customized scheduling plans on demand, and a comprehensive plan can optimize efficiency, cost, carbon emissions, and operational stability. This makes it well-suited for complex logistics scheduling in mountainous areas and provides both theoretical support and practical solutions for improving the quality and reducing the cost of intelligent logistics and multimodal transport in such regions.