Application of Reinforcement Learning Methods Combining Graph Neural Networks and Self-Attention Mechanisms in Supply Chain Route Optimization

Optimizing transportation routes to improve delivery efficiency and resource utilization in dynamic supply chain scenarios is a challenging task. Traditional route optimization methods often struggle with complex supply chain network structures and dynamic changes, which require a more efficient and flexible solution. This study proposes a method that integrates Graph Neural Networks (GNNs), self-attention mechanisms, and meta-reinforcement learning (Meta-RL) in order to address route optimization in supply chains. The goal is to develop a path planning method that excels in both static and dynamic environments. First, GNNs model the supply chain network, converting node and edge features into high-dimensional graph representations in order to capture local and global network information. Next, a Transformer-based strategy network captures global dependencies, optimizing path planning. Finally, Meta-RL enables rapid strategy adaptation to dynamic changes (e.g., new demand points or route disruptions) with minimal sample support. Experiments on multiple supply chain datasets show that our method improves path planning quality by about 7%, compared to traditional methods, achieving a path coverage of 92.29%. Ablation studies reveal that the on-time delivery rate improves by nearly 30% over the baseline model. These results demonstrate that the proposed method not only optimizes routes but also significantly enhances the overall efficiency and robustness of supply chain networks. This research provides an efficient route optimization framework applicable to complex supply chain management and other scheduling fields, offering new insights and technical solutions for future research and applications.