Multi-level Graph Partition via Hierarchical Learning for Large-scale Vehicle Routing Problems (JAAMAS 2026)
This is the official PyTorch implementation for the paper "Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing Problems", as well as its extended version "Multi-level Graph Partition via Hierarchical Learning for Large-scale Vehicle Routing Problems". The paper presents the HLGP framework, a new divide-and-conquer approach that combines global and local partition policies in graph partitioning tasks for subsequent node permutation. This method enables the scaling of neural solvers for the vehicle routing problem to handle over 10,000 nodes efficiently.
Before executing the training process, please ensure that the necessary requirements have been installed.
conda create -n hlgp_env python=3.8
pip install torch==1.11.0+cu113 --index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt
RL-driven HLGP
bash train_hlgp_rl.sh
SL-driven HLGP
bash train_hlgp_sl.sh
We have provided the pretrained model for you to use directly.
RL-driven HLGP
bash eval_hlgp_rl.sh [batch_size] [problem_size] [gnn_topK] [eval_revision_iters] [dataset_name]
Arguments:
[problem_size]: 1000, 2000, 5000, 7000, 10000
[gnn_topK]: 100 (problem_size <=1000), 200 (problem_size >= 2000)
[eval_revision_iters]: 5
[dataset_name]: 1000, 2000, 5000, 7000, 10000, 1000_[explosion,gaussian,rotation], 2000_[gaussian,], 5000_[explosion,], 7000_[rotation,]
SL-driven HLGP
bash eval_hlgp_sl.sh [batch_size] [beam_size] [problem_size] [knns] [eval_revision_iters] [dataset_name]
Arguments:
[problem_size]: 1000, 2000, 5000, 7000, 10000
[beam_size]: 16 (problem_size=1000, 2000), 8 (problem_size=5000), 4 (problem_size=7000, 10000)
[knns]: 250 (problem_size=1000, 5000, 7000, 10000), 400 (problem_size=2000)
[eval_revision_iters]: 5
[dataset_name]: 1000, 2000, 5000, 7000, 10000, 1000_[explosion,gaussian,rotation], 2000_[gaussian,], 5000_[explosion,], 7000_[rotation,]
We appreciate the anonymous reviewers, (S)ACs, and PCs of AAMAS 2025 for their insightful comments to improve our paper and their service to the community. Furthermore, we are sincerely grateful to the anonymous reviewers and editors of JAAMAS for their thoughtful suggestions in extending this work and for their valuable contributions to the community. We would like to thank the authors of GLOP (implementation of GLOP) and the authors of BQ (implementation of BQ) for their invaluable contributions to the community.
@article{pan2026hlgp4cvrp,
title={Multi-level Graph Partition via Hierarchical Learning for Large-scale Vehicle Routing Problems},
author={Pan, Yuxin and Liu, Ruohong and Chen, Yize and Cao Zhiguang and Lin, Fangzhen},
journal={Autonomous Agents and Multi-Agent Systems},
year={2026},
publisher={Springer}
}
@inproceedings{pan2025hlgp4cvrp,
title={Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing Problems},
author={Pan, Yuxin and Liu, Ruohong and Chen, Yize and Cao Zhiguang and Lin, Fangzhen},
booktitle={The 24th International Conference on Autonomous Agents and Multi-Agent Systems},
year={2025},
}
This source code is provided solely for academic use. Please refrain from using it for commercial purposes without obtaining proper authorization from the author.