Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

Area of Science:

• Operations Research
• Computer Science
• Artificial Intelligence

Background:

• Multitask vehicle routing problems (VRPs) are crucial for optimizing logistics and services.
• Current VRP solvers primarily use graph-based methods, struggling with complex, multi-constraint variants.
• The vision modality offers potential for encoding diverse VRP constraints, but existing methods face challenges.

Purpose of the Study:

• To develop a novel approach for solving multitask VRPs by integrating vision and graph modalities.
• To address limitations in representing VRP constraints visually and handling varying task requirements.
• To improve the efficiency and accuracy of VRP solvers for complex, multi-constraint scenarios.

Main Methods:

• Proposed a vision-assisted foundation model (VaFM) integrating convolutional neural networks (CNNs) and graph-based approaches.
• Developed a hybrid cross-attention fusion module for adaptive receptive fields and enhanced feature integration.
• Implemented a constraint-aware auxiliary task with binary cross-entropy (BCE) loss to manage imbalanced pixel distributions.

Main Results:

• VaFM demonstrated superior performance across 16 different VRP variants compared to state-of-the-art methods.
• The model effectively handled complex constraints by learning patch-level semantics from vision data.
• The hybrid cross-attention module enabled adaptive receptive fields, improving task-specific feature extraction.

Conclusions:

• The vision-assisted foundation model (VaFM) offers a powerful new paradigm for solving multitask VRPs.
• Integrating visual information significantly enhances the ability to address complex VRP constraints.
• VaFM represents a significant advancement in VRP solving, particularly for real-world applications with diverse requirements.