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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Vydané v:	International journal of production research Ročník 63; číslo 1; s. 119 - 147
Hlavní autori:	Chang, Xiao, Jia, Xiaoliang, Ren, Jiahao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Taylor & Francis 02.01.2025 Taylor & Francis LLC
Predmet:	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
On-line prístup:	Získať plný text
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