A machine learning based EMA-DCPM algorithm for production scheduling
Author:
Funder
National Key Research and Development Program of China
Research Funds for Leading Talents Program
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-71355-w.pdf
Reference42 articles.
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2. Liu, G. S., Zhou, Y. & Yang, H. D. Minimizing energy consumption and tardiness penalty for fuzzy flow shop scheduling with state-dependent setup time. J. Clean. Prod. 147, 470–484. https://doi.org/10.1016/j.jclepro.2016.12.044 (2017).
3. Lu, P. H., Wu, M. C., Tan, H., Peng, Y. H. & Chen, C. F. A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems. J. Intell. Manuf. 29, 19–34. https://doi.org/10.1007/s10845-015-1083-z (2018).
4. Marichelvam, M. K., Geetha, M. & Tosun, Ö. An improved particle swarm optimization algorithm to solve hybrid flowshop scheduling problems with the effect of human factors–A case study. Comput. Op. Res. 114, 104812. https://doi.org/10.1016/j.cor.2019.104812 (2020).
5. Zhao, F. et al. A factorial based particle swarm optimization with a population adaptation mechanism for the no-wait flow shop scheduling problem with the makespan objective. Expert Syst. Appl. 126, 41–53. https://doi.org/10.1016/j.eswa.2019.01.084 (2019).
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