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
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Zusammenfassung: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 different factories and the batch delivery process. To minimize inventory and tardiness costs of this new problem, a novel Q-learning-based multi-population multi-objective evolutionary algorithm is proposed. The algorithm incorporates five problem-specific properties to design the solution representation and calculate objectives. To obtain high-quality initial dual populations, a collaborative initialization technique combining four NEH heuristics and a completely random approach is proposed. To enhance algorithm’s search efficiency, a Q-learning-based selection method is developed to coordinate four job-related and four product-related operators. The dual-cooperation strategy is introduced to improve the global search capability. Experimental results demonstrate the effectiveness of all the designed components, indicating that the proposed algorithm outperforms five existing algorithms. It is well-suited for addressing real-world distributed heterogeneous scheduling scenarios by the proposed algorithm. •Define distributed heterogeneous assembly no-idle flowshop scheduling with batch delivery.•Mine five problem-specific properties and design objective calculation.•Define four job-related and four product-related knowledge-based evolutionary operators.•Develop a Q-learning-based multi-population multi-objective evolutionary algorithm
ISSN:0957-4174
DOI:10.1016/j.eswa.2024.125690