Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem

Paper: https://arxiv.org/abs/2402.17606

If you make use of the code/experiment or TBGAT algorithm in your work, please cite our paper (Bibtex below).

@InProceedings{zhanglearning2024,
  title = 	 {Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem},
  author =       {Zhang, Cong and Cao, Zhiguang and Wu, Yaoxin and Song, Wen and Sun, Jing},
  booktitle = 	 {Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence},
  year = 	 {2024},
}

Manual Setup

python 3.9.x

cuda 10.2 + torch 1.10.0

pip3 install torch==1.10.0 torchvision==0.11.0 torchaudio===0.10.0 -f https://download.pytorch.org/whl/cu102/torch_stable.html

Install dependencies:

pip install --upgrade pip
pip install torch-scatter==2.0.9 -f https://pytorch-geometric.com/whl/torch-1.10.0+cu102.html
pip install torch-sparse==0.6.12 -f https://pytorch-geometric.com/whl/torch-1.10.0+cu102.html
pip install torch-geometric==2.0.3
pip install matplotlib==3.4.3
pip install ortools==9.3.10497
pip install openpyxl

Docker Setup

Clone this repo and within the repo folder run the following command.

Create image neural-tabu-jssp-image:

docker build -t neural-tabu-jssp-image .

Create container neural-tabu-jssp-container from neural-tabu-jssp-image, and activate it:

docker run --gpus all --name neural-tabu-jssp-container -it neural-tabu-jssp-image