Research on Optimizing Logistics Transportation Routes Using AI Large Models

Abstract

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 optimization algorithms, AI large models not only significantly improve computation speed but also demonstrate higher accuracy in route selection and better control over transportation costs. The optimized route plans resulted in an average reduction of transportation time by approximately 15% and transportation costs by about 10%.

Discussion: The findings indicate that the application of AI large models in optimizing logistics transportation routes holds broad prospects and practical value. However, the models still have certain limitations when dealing with extremely complex transportation networks. Future research can further enhance the flexibility and adaptability of the models. Additionally, exploring the application of AI large models in other logistics segments (such as warehousing and sorting) by integrating more diversified data sources and more complex logistics scenarios is also an important research direction.

Conclusion: This study demonstrates through experiments that AI large models are effective in optimizing logistics transportation routes, providing logistics companies with an efficient and reliable route planning tool. In the future, as technology continues to advance, the application prospects of AI large models in the logistics industry will become even broader, with further potential to improve logistics efficiency and reduce costs.