Two-Stage Learned Decomposition for Scalable Routing on Multigraphs

📰 ArXiv cs.AI

Learn to scale routing on multigraphs using a two-stage learned decomposition approach, improving Vehicle Routing Problem solutions

advanced Published 9 May 2026

Action Steps

Apply Node-Edge Policy Factorization (NEPF) to decompose multigraphs into manageable parts
Configure a two-stage learned decomposition model to handle parallel edges with varying trade-offs
Run experiments to compare the scalability of the proposed approach against existing methods
Test the model on real-world Vehicle Routing Problems (VRPs) to evaluate its performance
Optimize the model by fine-tuning its parameters for better results

Who Needs to Know This

This benefits data scientists and AI engineers working on routing problems, particularly those dealing with complex multigraphs, as it enhances their ability to scale solutions efficiently

Key Insight

💡 Two-stage learned decomposition can efficiently scale routing on multigraphs, overcoming limitations of existing neural methods

Key Takeaways

Learn to scale routing on multigraphs using a two-stage learned decomposition approach, improving Vehicle Routing Problem solutions

Full Article

Title: Two-Stage Learned Decomposition for Scalable Routing on Multigraphs

Abstract:
arXiv:2605.05389v1 Announce Type: cross Abstract: Most neural methods for Vehicle Routing Problems (VRPs) are limited to Euclidean settings or simple graphs. In this work, we instead consider multigraphs, where parallel edges represent distinct travel options with varying trade-offs (e.g., distance vs time). Few methods are designed for such formulations and those that do exist face major scalability issues. We mitigate these scalability issues via a Node-Edge Policy Factorization (NEPF) approac

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