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Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach

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Listed:
  • Kenny-Jesús Flores-Huamán

    (Departamento de Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Cm. de los Descubrimientos, s/n, 41092 Seville, Spain)

  • Alejandro Escudero-Santana

    (Departamento de Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Cm. de los Descubrimientos, s/n, 41092 Seville, Spain)

  • María-Luisa Muñoz-Díaz

    (Departamento de Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Cm. de los Descubrimientos, s/n, 41092 Seville, Spain)

  • Pablo Cortés

    (Departamento de Organización Industrial y Gestión de Empresas II, Escuela Técnica Superior de Ingeniería, Universidad de Sevilla, Cm. de los Descubrimientos, s/n, 41092 Seville, Spain)

Abstract

This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available.

Suggested Citation

  • Kenny-Jesús Flores-Huamán & Alejandro Escudero-Santana & María-Luisa Muñoz-Díaz & Pablo Cortés, 2024. "Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach," Mathematics, MDPI, vol. 12(15), pages 1-34, July.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2347-:d:1444072
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    References listed on IDEAS

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    1. Dong, Hongyang & Zhang, Jincheng & Zhao, Xiaowei, 2021. "Intelligent wind farm control via deep reinforcement learning and high-fidelity simulations," Applied Energy, Elsevier, vol. 292(C).
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    3. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    4. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    5. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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