Abstract
The open shop scheduling problem (OSSP) is one of the classical production scheduling problems, which usually has complex constraints and huge solution space. Given that the traditional meta-heuristic algorithms are difficult to solve the large-scale OSSP efficiently, a method to solve the large-scale OSSP via graph convolution network-based link prediction (GCN-LP) is proposed. Firstly, the state of OSSP is represented using a disjunctive graph, and the features of the operation nodes are designed. Secondly, a GCN-based open shop scheduling model is designed by embedding the operation node features in OSSP. Finally, an open shop scheduling algorithm based on link prediction is designed by combining with the GCN-based open shop scheduling model, which improves the efficiency and quality of solving the large-scale OSSP. Experimental results show that the solution quality of the proposed GCN-LP method is comparable to the meta-heuristic algorithms in the OSSP benchmark instances, but the solution quality and solution efficiency of the GCN-LP method are significantly better than the meta-heuristic algorithms in the large-scale OSSP random instances. Compared with the other graph neural network (GNN) models, the proposed GCN-based link prediction method can obtain better and more stable scheduling results when solving the large-scale OSSP random instances.
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Acknowledgements
This work was supported by the Excellent Youth Foundation of Education Bureau of Hunan Province, China under Grant 21B0547, the National Natural Science Foundation for Young Scientists of China under Grant 61702177, the Natural Science Foundation of Hunan Province under Grant 2019JJ60048, and the Research Foundation of Education Bureau of Hunan Province under Grant 21A0356.
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Wan, L., Zhao, H., Cui, X., Li, C., Deng, X. (2023). Solving Large-Scale Open Shop Scheduling Problem via Link Prediction Based on Graph Convolution Network. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_9
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