Literature Review of Scheduling Problems Using Artificial Intelligence Technologies Based on Machine Learning
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-67152-4_36
Reference48 articles.
1. Baer, S., Bakakeu, J., Meyes, R., Meisen, T.: Multi-agent reinforcement learning for job shop scheduling in flexible manufacturing systems. In: 2019 Second International Conference on Artificial Intelligence for Industries (AI4I), pp. 22–25. IEEE (2019)
2. Benda, F., Braune, R., Doerner, K.F., Hartl, R.F.: A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking. OR Spect. 41, 871–893 (2019)
3. Bouška, M., Šůcha, P., Novák, A., Hanzálek, Z.: Deep learningdriven scheduling algorithm for a single machine problem minimizing the total tardiness. Eur. J. Oper. Res. 308(3), 990–1006 (2023)
4. Chang, J., Yu, D., Hu, Y., He, W., Yu, H.: Deep reinforcement learning for dynamic flexible job shop scheduling with random job arrival. Processes 10(4), 760 (2022)
5. Chen, R., Yang, B., Li, S., Wang, S.: A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Comput. Ind. Eng. 149, 106778 (2020)
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