Implementation of the IEEE TII paper Flexible Job Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning. IEEE Transactions on Industrial Informatics, 2022.

@ARTICLE{9826438,  
   author={Song, Wen and Chen, Xinyang and Li, Qiqiang and Cao, Zhiguang},  
   journal={IEEE Transactions on Industrial Informatics},   
   title={Flexible Job Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning},   
   year={2022},  
   volume={},  
   number={},  
   pages={1-11},  
   doi={10.1109/TII.2022.3189725}
 }

• python $\ge$ 3.6.13
• pytorch $\ge$ 1.8.1
• gym $\ge$ 0.18.0
• numpy $\ge$ 1.19.5
• pandas $\ge$ 1.1.5
• visdom $\ge$ 0.1.8.9

Note that pynvml is used in test.py to avoid excessive memory usage of GPU, please modify the code when using CPU.

• data_dev and data_test are the validation sets and test sets, respectively.
• data saves the instance files generated by ./utils/create_ins.py
• env contains code for the DRL environment
• graph is part of the code related to the graph neural network
• model saves the model for testing
• results saves the trained models
• save is the folder where the experimental results are saved
• utils contains some helper functions
• config.json is the configuration file
• mlp.py is the MLP code (referenced from L2D)
• PPO_model.py contains the implementation of the algorithms in this article, including HGNN and PPO algorithms
• test.py for testing
• train.py for training
• validate.py is used for validation without manual calls

There are various experiments in this article, which are difficult to be covered in a single run. Therefore, please change config.json before running.

Note that disabling the validate_gantt() function in schedule() can improve the efficiency of the program, which is used to check whether the solution is feasible.

python train.py

Note that there should be a validation set of the corresponding size in ./data_dev.

python test.py

Note that there should be model files (*.pt) in ./model.

• https://github.com/zcaicaros/L2D
• https://github.com/yd-kwon/MatNet
• https://github.com/dmlc/dgl/tree/master/examples/pytorch/han