A spatial-temporal feature fusion network for order remaining completion time prediction in discrete manufacturing workshop

Wang, Shengbo; Guo, Yu; Huang, Shaohua; Liu, Daoyuan; Tang, Pengzhou; Zhang, Litong

doi:10.1080/00207543.2023.2245487

International Journal of Production Research

2023

DOI: 10.1080/00207543.2023.2245487

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A spatial-temporal feature fusion network for order remaining completion time prediction in discrete manufacturing workshop

Shengbo Wang,

Yu Guo,

Shaohua Huang

et al.

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2024

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A probabilistic prediction model based on stacking ensemble learning for completion time in flexible shop-floor

Chang,

Jia,

Fan

2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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Completion time prediction is crucial for analyzing and monitoring the execution of shop-floor’s production planning. However, predicting completion time is still challenging because production process has high uncertainty and influenced by the interaction of various factors. To address above challenges, the stacking ensemble learning based probabilistic prediction model (SEL-PP) to predict completion time is developed. Thereinto, fully connected neural network (FCNN), random forest (RF) and gradient boosted regression tree (GBRT) are used as the first-level base learner to exhibit better nonlinear characteristics, and quantile regression neural network (QRNN) is used as the second-level meta-learner to rectify the mistakes in the prediction of the base learner. After that, the kernel density estimation (KDE) is employed for achieving probability density prediction of completion time. Moreover, particle swarm optimization (PSO) is adopted for key parameter optimization of SEL-PP. Taking the aircraft overhaul shop-floor as an example, a case study is constructed to demonstrate the feasibility and effectiveness of SEL-PP. Through comparing the results, it indicates that the SEL-PP model has better performance in predicting completion time of flexible shop-floor.

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A probabilistic prediction model based on stacking ensemble learning for completion time in flexible shop-floor

Chang,

Jia,

Fan

2024

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

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A spatial-temporal feature fusion network for order remaining completion time prediction in discrete manufacturing workshop

Cited by 1 publication

References 44 publications

A probabilistic prediction model based on stacking ensemble learning for completion time in flexible shop-floor

A probabilistic prediction model based on stacking ensemble learning for completion time in flexible shop-floor

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