G-UniRouting: A Graph-Based Unified Neural Model for Solving Multi-Attribute Vehicle Routing Problems

Real-world Vehicle Routing Problems (VRPs) involve multiple operational attributes such as capacity limits, time windows, backhauls, open routes, and route duration constraints. Most existing methods are tailored to specific variants, limiting adaptability and reuse. This paper introduces G-UniRouting: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> Graph-Based Unified Neural Model for Solving Multi-Attribute Vehicle Routing Problems. Our approach integrates a Graph Neural Network (GNN) encoder that captures spatial and relational structure with a constraint-aware decoder that dynamically enforces feasibility through dynamic masking and distance-aware attention mechanisms. Our proposed model is an end-to-end framework trained using the REINFORCE algorithm with a rollout baseline to minimize routing costs. G-UniRouting is evaluated on 24 distinct VRP variants within a unified framework and employs multi-start inference to enhance solution quality. Experimental results show that our unified model generalizes across diverse constraints and consistently outperforms or rivals specialized baselines, offering a scalable and flexible solution to complex, multi-attribute routing problems.