Graph-To-Sequence Approach for Job Shop Scheduling Problem
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-71633-1_31
Reference18 articles.
1. Applegate, D., Cook, W.: A computational study of the job-shop scheduling problem. ORSA J. Comput. 3(2), 149–156 (1991)
2. Bonetta, G., Zago, D., Cancelliere, R., Grosso, A.: Job shop scheduling via deep reinforcement learning: a sequence to sequence approach. In: International Conference on Learning and Intelligent Optimization, pp. 475–490. Springer (2023). https://doi.org/10.1007/978-3-031-44505-7_32
3. Caserta, M., Voß, S.: Metaheuristics: intelligent problem solving. In: Matheuristics: Hybridizing Metaheuristics and Mathematical Programming, pp. 1–38. Springer (2009). https://doi.org/10.1007/978-1-4419-1306-7_1
4. Chen, R., Li, W., Yang, H.: A deep reinforcement learning framework based on an attention mechanism and disjunctive graph embedding for the job-shop scheduling problem. IEEE Trans. Industr. Inf. 19(2), 1322–1331 (2022)
5. Cho, Y.I., Nam, S.H., Cho, K.Y., Yoon, H.C., Woo, J.H.: Minimize makespan of permutation flowshop using pointer network. J. Comput. Design Eng. 9(1), 51–67 (2022)
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