This repository is the official implementation of the paper “Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning”. IEEE Transactions on Neural Networks and Learning Systems, 2023.

• python $=$ 3.7.11
• argparse $=$ 1.4.0
• numpy $=$ 1.21.6
• ortools $=$ 9.3.10497
• pandas $=$ 1.3.5
• torch $=$ 1.11.0+cu113
• torchaudio $=$ 0.11.0+cu113
• torchvision $=$ 0.12.0+cu113
• tqdm $=$ 4.64.0

• data saves the instance files including testing instances (in the subfolder BenchData, SD1 and SD2) and validation instances (in the subfolder data_train_vali) .
• model contains the implementation of the proposed framework.
• or_solution saves the results solved by Google OR-Tools.
• test_resultssaves the results solved by priority dispatching rules and DRL models.
• train_log saves the training log of models, including information of the reward and validation makespan.
• trained_network saves the trained models.
• common_utils.py contains some useful functions (including the implementation of priority dispatching rules mentioned in the paper) .
• data_utils.py is used for data generation, reading and format conversion.
• fjsp_env_same_op_nums.py and fjsp_env_various_op_nums.py are implementations of fjsp environments, describing fjsp instances with the same number of operations and different number of operations, respectively.
• ortools_solver.py is used for solving the instances by Google OR-Tools.
• params.py defines parameters settings.
• print_test_result.py is used for printing the experimental results into an Excel file.
• test_heuristic.py is used for solving the instances by priority dispatching rules.
• test_trained_model.py is used for evaluating the models.
• train.py is used for training.

python train.py # train the model on 10x5 FJSP instances using SD2

# options (Validation instances of corresponding size should be prepared in ./data/data_train_vali/{data_source})
python train.py 	--n_j 10		# number of jobs for training/validation instances
			--n_m 5			# number of machines for training/validation instances
    			--data_source SD2	# data source (SD1 / SD2)
        		--data_suffix mix	# mode for SD2 data generation
            					# 'mix' is thedefault mode as defined in the paper
                				# 'nf' means 'no flexibility' (generating JSP data) 
        		--model_suffix demo	# annotations for the model

python test_trained_model.py # evaluate the model trained on '10x5+mix' of SD2 using the testing instances of the same size using the greedy strategy

# options (Model files should be prepared in ./trained_network/{model_source})
python test_trained_model.py 	--data_source SD2	# source of testing instances
				--model_source SD2	# source of instances that the model trained on 
    				--test_data 10x5+mix	# list of instance names for testing
        			--test_model 10x5+mix	# list of model names for testing
            			--test_mode False	# whether using the sampling strategy
                		--sample_times 100	# set the number of sampling times

@ARTICLE{10246328,
  author={Wang, Runqing and Wang, Gang and Sun, Jian and Deng, Fang and Chen, Jie},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Flexible Job Shop Scheduling via Dual Attention Network-Based Reinforcement Learning}, 
  year={2023},
  volume={},
  number={},
  pages={1-12},
  doi={10.1109/TNNLS.2023.3306421}
}

• https://github.com/songwenas12/fjsp-drl/
• https://github.com/zcaicaros/L2D
• https://github.com/google/or-tools
• https://github.com/Diego999/pyGAT