Dynamic bottleneck identification and production loss evaluation for assembly lines

Li, Chen; Yu, Tian; Fan, Hua-tzu; Xiao, Guoxian; Arinez, Jorge; Chang, Qing

doi:10.1016/j.mfglet.2023.08.089

Manufacturing Letters

2023

DOI: 10.1016/j.mfglet.2023.08.089

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Dynamic bottleneck identification and production loss evaluation for assembly lines

Chen Li,

Tian Yu,

Hua-tzu Fan

et al.

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2024

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Bottleneck identification and transfer prediction for automated production lines based on FNN

Si,

Zhou,

Guo

et al. 2024

J. Phys.: Conf. Ser.

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In a re-entrant production system, the throughput of the whole system depends on the capacity of the bottleneck machine. In this study, a new definition of bottleneck is proposed for a precision forging blade shop. The reinforcement learning algorithm is used to optimize the production scheduling to determine the most suitable scheduling scheme, which lays the foundation for bottleneck identification. Subsequently, the bottleneck identification index system was established according to the optimization objective, and the bottleneck identification problem was transformed into a multi-attribute decision-making problem. Finally, a fuzzy neural network is used for training, and the basic scheduling examples of each flow shop are utilized for bottleneck identification and prediction to verify their effectiveness.

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Bottleneck identification and transfer prediction for automated production lines based on FNN

Si,

Zhou,

Guo

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

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Dynamic bottleneck identification and production loss evaluation for assembly lines

Cited by 1 publication

References 20 publications

Bottleneck identification and transfer prediction for automated production lines based on FNN

Bottleneck identification and transfer prediction for automated production lines based on FNN

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