Aplicación de algoritmos de aprendizaje automático a la programación de órdenes de producción en talleres de trabajo: una revisión de la literatura reciente

Pérez-Cubero, Efraín; Mula, Josefa

doi:10.37610/dyo.v0i72.588

Cited by 4 publications

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References 17 publications

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“…On the other hand, designing accurate production plans would facilitate a continuous workflow at the bending station, reducing the idle times in posterior processes. Pérez-Cubero and Poler [4] highlight the importance of accounting for processing lead time variations on job-shop production scheduling. Additionally, given the relevance of reworks, it is essential that the schedules of the bending station can be recalculated with accuracy, so as to reduce the impact on the overall lead times of the rest of the units.…”

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confidence: 99%

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

et al. 2022

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This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability.

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confidence: 99%