A reinforcement learning enhanced memetic algorithm for multi-objective flexible job shop scheduling toward Industry 5.0

Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and its importance in production processes. It is normally assumed that each machine is typically operated by one worker at an...

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Veröffentlicht in:International journal of production research Jg. 63; H. 1; S. 119 - 147
Hauptverfasser: Chang, Xiao, Jia, Xiaoliang, Ren, Jiahao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Taylor & Francis 02.01.2025
Taylor & Francis LLC
Schlagworte:
Aircraft
Aircraft performance
Algorithms
Flexible hours
Flexible job shop scheduling
Industry 5.0
innovation and infrastructure
Job shop scheduling
Job shops
learning effect
Machine learning
multi-objective memetic algorithm
Multiple objective analysis
Production scheduling
reinforcement learning
SDG 9: Industry
uncertain processing time
Workload
Workloads
ISSN:0020-7543, 1366-588X
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Zusammenfassung:Flexible job shop scheduling problem (FJSP) with worker flexibility has gained significant attention in the upcoming Industry 5.0 era because of its computational complexity and its importance in production processes. It is normally assumed that each machine is typically operated by one worker at any time; therefore, shop-floor managers need to decide on the most efficient assignments for machines and workers. However, the processing time is variable and uncertain due to the fluctuating production environment caused by unsteady operating conditions of machines and learning effect of workers. Meanwhile, they also need to balance the worker workload while meeting production efficiency. Thus a dual resource-constrained FJSP with worker's learning effect and fuzzy processing time (F-DRCFJSP-WL) is investigated to simultaneously minimise makespan, total machine workloads and maximum worker workload. Subsequently, the reinforcement learning enhanced multi-objective memetic algorithm based on decomposition (RL-MOMA/D) is proposed for solving F-DRCFJSP-WL. For RL-MOMA/D, the Q-learning is incorporated into memetic algorithm to perform variable neighbourhood search and further strengthen the exploitation capability for the algorithm. Finally, comprehensive experiments on extensive test instances and a case study of aircraft overhaul shop-floor are conducted to demonstrate effectiveness and superiority of the proposed method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2024.2357740