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

Researchers propose VaFM, a vision-assisted foundation model that combines visual and graph-based approaches to solve multi-task vehicle routing problems more effectively. The model addresses key limitations of existing solvers by incorporating constraint representations through image data, achieving superior performance across 16 VRP variants with complex constraints.

This research represents a meaningful advancement in optimization technology by introducing a hybrid multimodal approach to a classical logistics problem. Vehicle routing problems constitute a fundamental challenge in supply chain management and service delivery, directly affecting operational costs for industries ranging from e-commerce to emergency services. The conventional graph-based methods have proven insufficient for capturing the full complexity of real-world constraints such as time windows, vehicle capacity limits, and service prerequisites. VaFM addresses this limitation by leveraging computer vision to extract semantic information from constraint visualizations, effectively bridging the gap between human-interpretable constraints and machine learning models. The research tackles three specific technical challenges: inadequate constraint representation in existing training data, limitations in patch-based visual encoding with fixed receptive fields, and the model's tendency to ignore low-frequency constraints due to imbalanced pixel distribution. The proposed solution employs convolutional neural networks to process constraint images while incorporating an auxiliary task to mitigate the pixel imbalance problem. The experimental validation across 16 VRP variants demonstrates consistent improvements, particularly for problems with intricate multi-constraint scenarios. From an industry perspective, superior VRP solving capabilities translate directly to reduced logistics costs, faster delivery times, and improved resource allocation for enterprises operating at scale. The foundation model approach suggests potential for transfer learning across related optimization domains. Future developments may extend this methodology to other combinatorial optimization problems, creating broader applications in manufacturing, scheduling, and network design. The research validates the hypothesis that vision modality can effectively encode abstract problem semantics beyond traditional graph representations.

• →VaFM combines visual and graph-based models to solve vehicle routing problems with multiple complex constraints simultaneously.
• →The model addresses pixel imbalance issues through auxiliary tasks, improving detection of low-frequency constraints.
• →Vision-assisted approach enables more accurate constraint representation compared to graph-only methods.
• →Performance validated across 16 VRP variants with particular strength in complex constraint scenarios.
• →Foundation model architecture suggests potential applicability to broader combinatorial optimization problems.