Vision-Assisted Foundation Model for Solving Multi-Task Vehicle Routing Problems

The paper introduces a vision-assisted foundation model (VaFM) designed to tackle multi-task vehicle routing problems (VRPs) by integrating vision modality with graph-based approaches. VaFM employs a convolutional neural network to encode constraint-specific images, generating patch embeddings that are fused with graph nodes, while also implementing an auxiliary task to mitigate pixel imbalance among constraints. Experimental results show that VaFM outperforms existing state-of-the-art methods across 16 VRP variants, highlighting its capability to efficiently handle complex multi-constraint scenarios, which is critical for practitioners in optimizing routing solutions.