Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics

This repository is the official implementation of methods from the paper Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics.

Requirements

To install dependencies, run

pip install -r requirements.txt

Running experiments

To run experiments on the baselines and the GNN model, run the following command:

python main.py

The results will be stored in the results.log file.

To train the GNN models from scratch, run the following command:

python train.py

The results will be stored in the training_gnn folder.

Experimental results

The total cost (the lower the better) of solutions to the problems in the LMSC dataset.

Tasks-Operations- Enterprises	No.	Optimal solution	Random solution	Greedy algorithm	Genetic algorithm	GNN (Ours)
5-5-5	1	2648.63	3484.19	3495.44	2648.63	2780.38
	2	5944.42	8704.79	8266.23	5944.42	5944.42
	3	6653.88	8896.25	8868.77	6653.88	6653.88
5-10-10	1	5086.08	12599.87	6325.05	5145.03	5141.23
	2	7352.66	18310.91	10974.05	7409.04	7352.66
	3	7652.32	19556.74	12521.69	7714.85	7652.32
10-10-10	1	12221.76	33980.4	14849.09	12656.17	15306.85
	2	14275.53	39574.01	21835.97	14322.24	14275.53
	3	14200.75	40062.74	23082.48	14303.49	14200.75
5-10-20	1	4252.04	11542.44	6091.69	4289.83	4701.41
	2	5497.29	18336.67	9377.16	5543.21	5497.29
	3	5866.18	20916.08	9414.35	5925.18	5866.18
5-20-10	1	13512.96	36326.45	15446.19	13805.60	13921.15
	2	15803.26	42108.59	19195.71	16137.08	16116.05
	3	16703.56	45502.72	20396.13	17243.44	17491.83
5-20-20	1	11392.82	34663.67	14002.05	11875.55	13500.28
	2	13045.16	42312.09	17038.35	13580.50	14550.33
	3	14366.02	45445.61	18597.85	14899.99	16083.00

Please note that results may differ slightly from the paper due to randomness.