The Application of AI in Optimizing Fresh Food Logistics Routes

Abstract

The fresh food logistics industry is confronted with inherent challenges, which stem from the perishability of products, strict quality requirements, and increasingly fragmented and dynamic demand patterns. Traditional logistics models and optimization techniques often struggle in this high-risk environment, leading to low efficiency, high costs and significant food waste. This article explores the potential of artificial intelligence in addressing these key issues. Through a systematic literature review, the author comprehensively analyzed the application of artificial intelligence technology - including predictive analytics, intelligent path planning, computer vision, and autonomous systems - in the fresh food delivery model. Analysis shows that artificial intelligence technology can significantly enhance operational efficiency: dynamic path optimization can reduce transportation distance and fuel consumption, and predictive analysis can minimize inventory loss to the greatest extent. In addition, integrating AI-driven automation systems in warehouses can not only enhance processing speed but also reduce physical damage to goods. This article explores these findings in specific contexts and demonstrates that artificial intelligence is not only the key to coordinating the transformation of fresh food logistics and distribution, but also can enhance the resilience and sustainability of the supply chain. However, this field still faces challenges in terms of cost, data integration and regulation. The research ultimately indicates that artificial intelligence marks a paradigm shift in fresh food logistics, providing a powerful toolset for building a more efficient, flexible, and zero-waste supply chain system, and pointing out the key path for future research and practical application.