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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Cited by 3 publications

References 44 publications

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A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops

Liu,

Zhang,

Qin

et al. 2025

Measurement

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A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops

Liu,

Zhang,

Qin

et al. 2025

Measurement

View full text Add to dashboard Cite

Multi-task learning with multi-gate mixture of transformer-based experts for predictive process monitoring in manufacturing

Wang,

Sui,

Liu

et al. 2024

Science Progress

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Manufacturing industries involve both business processes and complex manufacturing processes. Predictive process monitoring techniques are effective for managing process executions by making multi-perspective real-time predictions, preventing issues such as delivery delays. Conventional predictive process monitoring for business processes focuses on predicting the next activity, next event time, and remaining time using single-task learning, which is costly and complex. For complex manufacturing processes, predictive process monitoring primarily aims to predict the remaining time, that is, product cycle time. However, single-task learning methods fail to capture all the variations within the historical process executions. To address them, we propose the multi-gate mixture of transformer-based experts framework, which leverages a transformer network within the multi-gate mixture-of-experts multi-task learning architecture to extract sequential features and employs gated expert networks to model task commonalities and differences. Empirical results demonstrate that multi-gate mixture of transformer-based experts outperforms three alternative architectures across five real-life event logs, highlighting its generalization, effectiveness, and efficiency in predictive process monitoring.

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

Cited by 3 publications

References 44 publications

A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops

A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing workshops

Multi-task learning with multi-gate mixture of transformer-based experts for predictive process monitoring in manufacturing

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

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