Treffer: Research on Optimizing Logistics Transportation Routes Using AI Large Models

Background: With the rapid development of global e-commerce, the logistics industry faces unprecedented challenges. The efficiency and cost control of logistics transportation have become critical factors affecting the competitiveness of enterprises. However, computational complexity and lack of flexibility limit traditional methods for optimizing transportation routes, making it difficult to meet the ever-changing and increasingly complex logistics demands. In recent years, large AI models have emerged with their powerful data processing capabilities and predictive accuracy, becoming an important application in optimizing logistics transportation routes. Objective: This study explores how to utilize AI large models to optimize logistics transportation routes, enhancing the efficiency and accuracy of route planning to reduce transportation costs, shorten transportation time, and improve overall logistics service levels. Specifically, this research will address the gap in current studies on large-scale data processing and complex route optimization, providing an efficient and flexible route optimization solution. Methods: This paper employs AI large models based on deep learning to train and test real logistics transportation data from open-source platforms such as Kaggle. The data includes transportation route data, transportation time, transportation costs, and other relevant logistics information. By building and training deep neural network models combined with reinforcement learning algorithms, transportation routes are optimized. Additionally, a series of comparative experiments were designed to verify the effectiveness and practicality of the models. Data processing and analysis were primarily conducted using Python and related data science libraries. Findings: Experimental results show that the AI large model-based transportation route optimization methods exhibit significant advantages in various scenarios. Specifically, compared to traditional route optimiz