A Q-learning-based multi-population algorithm for multi-objective distributed heterogeneous assembly no-idle flowshop scheduling with batch delivery

Distributed heterogeneous factory environments have become the mainstream in real-world manufacturing enterprises. Scheduling the assembly no-idle flowshops in such distributed heterogeneous environments is significant for practitioners. This problem takes into account the heterogeneity between diff...

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Veröffentlicht in:	Expert systems with applications Jg. 263; S. 125690
Hauptverfasser:	Zhang, Zikai, Tang, Qiuhua, Zhang, Liping, Li, Zixiang, Cheng, Lixin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 05.03.2025
Schlagworte:	Assembly flowshop Distributed heterogeneous scheduling Multi-objective optimization Q-learning Distributed heterogeneous scheduling Q-learning Multi-objective optimization Assembly flowshop
ISSN:	0957-4174
Online-Zugang:	Volltext
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