Copyright (c) 2023 CIAM Group

The code can only be used for non-commercial purposes. Please contact the authors if you want to use this code for business matters.
If this repository is helpful for your research, please cite our paper:
"Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang, and Zhenkun Wang, Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization, The 37th Anniversary Conference on Neural Information Processing Systems, NeurIPS 2023"

OR

@inproceedings{luo2023neural,
  title={Neural Combinatorial Optimization with Heavy Decoder: Toward Large Scale Generalization},
  author={Fu Luo, Xi Lin, Fei Liu, Qingfu Zhang, Zhenkun Wang},
  booktitle={The 37th Anniversary Conference on Neural Information Processing Systems, NeurIPS 2023},
  year={2023}
}

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This code can develop an NCO model with a Light Encoder and a Heavy Decoder for solving TSP and CVRP.

Dependencies

Python=3.8.6
torch==1.12.1
numpy==1.23.3
matplotlib==3.5.2
tqdm==4.64.1
pytz==2022.1

We don't use any hard-to-install packages. If any package is missing, just install it following the prompts.

Download the datasets

The training and test datasets can be downloaded from Google Drive:

https://drive.google.com/drive/folders/1LptBUGVxQlCZeWVxmCzUOf9WPlsqOROR?usp=sharing

or Baidu Cloud:

https://pan.baidu.com/s/12uxjol_5pAlnm0j4F6D_RQ?pwd=rzja

• For TSP, download the training/testing datasets and put them to the path <LEHD_main/TSP/data>.

• For CVRP, download the training/testing datasets and put them to the path <LEHD_main/CVRP/data>. See <LEHD_main/CVRP/Transform_data/Format_of_CVRP_datatset.md> for more details about the format of the CVRP dataset.

Implementation

This project's structure is clear, the codes are based on .py files, and they should be easy to read, understand, and run.

Acknowledgements

LEHD's code implementation is based on the code of POMO. Thanks to them.