Processing Time Estimation in the Textile Warp Preparation Shop with Machine Learning Approaches

Yunus Demir; Müzeyyen Göksel

doi:10.38088/jise.1791714

Processing Time Estimation in the Textile Warp Preparation Shop with Machine Learning Approaches

Abstract

Accurate determination of processing time has critical importance in the production planning process for several reasons, such as effective planning of production resources and ensuring customer satisfaction by meeting due dates. Traditionally, processing time is often determined through time studies or simple calculations and is usually assumed to be known prior to planning. However, in some special cases, processing time varies depending on many parameters. Such a situation occurs in the warp preparation process, which is one of the important steps of woven fabric production. In this study, supervised machine learning approaches were used to estimate the warp preparation process time based on data obtained from the ERP system of the enterprise where the application was implemented. Twelve different supervised machine learning algorithms were applied to both training and test datasets, and the results are presented comparatively. It was observed that boosting algorithms outperform others in terms of both training/tuning time and estimation accuracy.

Keywords

Ethical Statement

Ethics committee approval was not required for this study because there was no study on animals or humans.

Thanks

I would like to thank all Küçükçalık Textile family in the presence of Planning Manager Zeynep Işıkdoğdu and department Chief İsmail Hakkı Demir for their valuable contribution to this study.

References

Alidaee, B., & Womer, N. K. (1999). Scheduling with time dependent processing times: Review and extensions. Journal of the Operational Research Society, 50(7), 711–720.
Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175–185.
Aytekin, H. T. (2021). Makine öğreniminin araştırmacıların veri analizi bağlamında potansiyel önemi. Ufuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 10(19), 85–106.
Backus, P., Janakiram, M., Mowzoon, S., Runger, C., & Bhargava, A. (2006). Factory cycle-time prediction with a data-mining approach. IEEE Transactions on Semiconductor Manufacturing, 19(2), 252–258.
Bento, C. (2021). Multilayer perceptron explained with a real-life example and Python code: Sentiment analysis. Towards Data Science. https://towardsdatascience.com/multilayer-perceptron-explained-with-a-real-life-example-and-python-code-sentiment-analysis-cb408ee93141
Bishop, C. M. (2006). Pattern recognition and machine learning (Information Science and Statistics). Springer.
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017). Classification and regression trees. Routledge.
Brownlee, J. (2019). A gentle introduction to model selection for machine learning. Machine Learning Mastery. https://machinelearningmastery.com/a-gentle-introduction-to-model-selection-for-machine-learning/

Burggräf, P., Wagner, J., Lück, K., & Adlon, T. (2017). Cost-benefit analysis for disruption prevention in low-volume assembly. Production Engineering, 11(3), 331–342.
Business Dictionary. (2022, August 15). http://www.businessdictionary.com/
CatBoost Documentation. (2022). https://catboost.ai/
Chen, C. Y., George, E. I., & Tardif, V. (2001). A Bayesian model of cycle time prediction. IIE Transactions, 33(10), 921–930.
Chen, T. (2006). A hybrid SOM-BPN approach to lot output time prediction in a wafer fab. Neural Processing Letters, 24(3), 271–288.
Chen, T. (2007). An intelligent hybrid system for wafer lot output time prediction. Advanced Engineering Informatics, 21(1), 55–65.
Chen, T., Wang, Y. C., & Tsai, H. R. (2009). Lot cycle time prediction in a ramping-up semiconductor manufacturing factory with a SOM–FBPN-ensemble approach. The International Journal of Advanced Manufacturing Technology, 42(11), 1206–1216.
Chen, T. C. T., & Wang, Y. C. (2020). An explainable deep-learning approach for job cycle time prediction. SSRN. https://doi.org/10.2139/ssrn.4136028
Cheng, Y., Chen, K., Zhang, Y., & Tao, F. (2017). Data and knowledge mining with big data towards smart production. Journal of Industrial Information Integration. https://doi.org/10.1016/j.jii.2017.08.001
Chibani, S., & Coudert, F. X. (2020). Machine learning approaches for the prediction of materials properties. APL Materials, 8(8), 080701.
Chien, C. F., Hsiao, C. W., Meng, C., Hong, K. T., & Wang, S. T. (2005). Cycle time prediction and control based on production line status and manufacturing data mining. In ISSM 2005: IEEE International Symposium on Semiconductor Manufacturing (pp. 327–330). IEEE.
Dash, R. K., Nguyen, T. N., Cengiz, K., & Sharma, A. (2021). Fine-tuned support vector regression model for stock predictions. Neural Computing and Applications, 1–15.
Ding, J., Tarokh, V., & Yang, Y. (2018). Model selection techniques: An overview. IEEE Signal Processing Magazine, 35(6), 16–34.
Dorogush, A. V., Gulin, A., Gusev, G., Ostroumova Prokhorenkova, L., & Vorobev, A. (2017). CatBoost: Unbiased boosting with categorical features. arXiv:1706.09516.
Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features 1-7.
Ebiç, O. (2012). Tek bobinden çözgü hazırlama makinesinin tasarımı ve değerlendirilmesi (Yüksek lisans tezi). Gazi Üniversitesi, Ankara.
El-Dereny, M., & Rashwan, N. (2011). Solving multicollinearity problem using ridge regression models. International Journal of Contemporary Mathematical Sciences, 6, 585–600.
Fidanoğlu, I., & Akdeniz, F. (t.y.). İstatistiksel daraltıcı (shrinkage) model ve uygulamaları.
Fitrianto, A., & Yik, L. C. (2014). Performance of ridge regression estimator methods on small sample size. Journal of Mathematics and Statistics, 10, 25–29.
Gao, K. Z., Suganthan, P. N., Pan, Q. K., Chua, T. J., Chong, C. S., & Cai, T. X. (2016). An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Systems with Applications, 65, 52–67.
Garthwaite, P. H. (1994). An interpretation of partial least squares. Journal of the American Statistical Association, 89(425), 122–137.
Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. International Journal of Operations & Production Management, 21(1/2), 71–87.
Gupta, J. N., & Gupta, S. K. (1988). Single facility scheduling with nonlinear processing times. Computers & Industrial Engineering, 14(4), 387–393.
Gyulai, D., Pfeiffer, A., Nick, G., Gallina, V., Sihn, W., & Monostori, L. (2018). Lead time prediction in a flow-shop environment. IFAC-PapersOnLine, 51(11), 1029–1034.
Gyulai, D., Pfeiffer, A., Bergmann, J., & Gallina, V. (2018). Online lead time prediction supporting situation-aware production control. Procedia CIRP, 78, 190–195.
Hans, C. (2011). Elastic net regression modeling with the orthant normal prior. Journal of the American Statistical Association, 106(496), 1383–1393.
He, J., Chu, F., Zheng, F., & Liu, M. (2021). A green-oriented bi-objective disassembly line balancing problem. Annals of Operations Research, 296(1), 71–93.
Horng, S. C., Lin, S. S., & Yang, F. Y. (2012). Evolutionary algorithm for stochastic job shop scheduling. Expert Systems with Applications, 39(3), 3603–3610.
Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2), 251–277.
Huang, J., Chang, Q., & Arinez, J. (2020). Product completion time prediction. Journal of Manufacturing Systems, 57, 311–322.
Jabeur, S. B., Gharib, C., Mefteh-Wali, S., & Arfi, W. B. (2021). CatBoost model. Technological Forecasting and Social Change, 166, 120658.
Bergstra, J. S., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neural Information Processing Systems, 2546–2554.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.
Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.
Joo, B. J., Shim, S. O., Chua, T. J., & Cai, T. X. (2018). Multi-level job scheduling. Computers & Industrial Engineering, 120, 480–487.
Ke, G., Meng, Q., Finley, T., et al. (2017). LightGBM. Advances in Neural Information Processing Systems, 30.
Khaled, M. S., Shaban, I. A., Karam, A., Hussain, M., Zahran, I., & Hussein, M. (2022). Research trends in sustainability of production planning. Energies, 15(2), 483.
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Springer.
Kumar, S. M. (2014). Industrial practices in weaving preparatory. Woodhead Publishing.
Kunnathur, A. S., & Gupta, S. K. (1990). Minimizing the makespan with late start penalties added to processing times in a single facility scheduling problem. European Journal of Operational Research, 47(1), 56-64.
Lei, D. (2011). Population-based neighborhood search. Computers & Industrial Engineering, 61(4), 1200–1208.
Kurnaz, F. S. (2020). Temel bileşenlere dayalı bazı regresyon yöntemleri. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 10(3), 581–591.
Lingitz, L., Gallina, V., Ansari, F., Gyulai, D., Pfeiffer, A., Sihn, W., & Monostori, L. (2018). Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer. Procedia CIRP, 72, 1051–1056.
Li, X., Gao, L., Wang, W., Wang, C., & Wen, L. (2019). Particle swarm optimization hybridized with genetic algorithm. Computers & Industrial Engineering, 135, 1036–1046.
LightGBM Documentation. (2022). https://lightgbm.readthedocs.io/en/v3.3.2/
Lödding, H. (2005). Verfahren der fertigungssteuerung. Springer.
Machado, M. R., Karray, S., & de Sousa, I. T. (2019). LightGBM: Predicting customer loyalty. In 2019 International Conference on Computer Science & Education (pp. 1111–1116). IEEE.
Meidan, Y., Lerner, B., Rabinowitz, G., & Hassoun, M. (2011). Cycle-time key factor identification and prediction in semiconductor manufacturing using machine learning and data mining. IEEE Transactions on Semiconductor Manufacturing, 24(2), 237–248.
Moore, R. E. (1979). Methods and applications of interval analysis. Society for Industrial and Applied Mathematics.
Mosheiov, G. (1996). A-shaped policies for schedule deteriorating jobs. Journal of the Operational Research Society, 47, 1184–1191.
Muratlar, E. R. (2020). LightGBM. https://www.veribilimiokulu.com/lightgbm/
Nagasawa, K., Ikeda, Y., & Irohara, T. (2015). Robust flow shop scheduling with random processing times for reduction of peak power consumption. Simulation Modelling Practice and Theory, 59, 102–113.
Navas, J. (2022). What is hyperparameter tuning? Anyscale. https://www.anyscale.com/blog/what-is-hyperparameter-tuning
Ouyang, F. S., Guo, B. L., Ouyang, L. Z., Liu, Z. W., Lin, S. J., Meng, W., ... & Yang, S. M. (2019). Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules. European Journal of Radiology, 113, 251–257.
Quinlan, J. R. (1987). Simplifying decision trees. International Journal of Man-Machine Studies, 27, 221–234.
Phatak, A., & De Jong, S. (1997). The geometry of partial least squares. Journal of Chemometrics, 11(4), 311–338.
Rainer, C. (2013). Data mining as technique to generate planning rules for manufacturing control in a complex production system. In Robust Manufacturing Control (pp. 203–214). Springer.
Raaymakers, W. H., & Weijters, A. J. M. M. (2003). Makespan estimation in batch process industries: A comparison between regression analysis and neural networks. European Journal of Operational Research, 145(1), 14–30.
Seçkin, M., Seçkin, A. Ç., & Coşkun, A. (2019). Production fault simulation and forecasting from time series data with machine learning in glove textile industry. Journal of Engineered Fibers and Fabrics, 14, 1558925019883462.
Scher, S., & Messori, G. (2018). Predicting weather forecast uncertainty with machine learning. Quarterly Journal of the Royal Meteorological Society, 144(717), 2830–2841.
Scikit-learn. (n.d.). Elastic net. https://scikit-learn.org/stable/modules/linear_model.html#elastic-net
Schuh, G., & Stich, V. (2012). Konzeptentwicklung in der Produktionsplanung. In Produktionsplanung und-steuerung 2 (pp. 149–415). Springer.
Sun, C., Dominguez-Caballero, J., Ward, R., Ayvar-Soberanis, S., & Curtis, D. (2022). Machining cycle time prediction: Data-driven modelling of machine tool feedrate behavior with neural networks. Robotics and Computer-Integrated Manufacturing, 75, 102293.
Thomas, L. J., & McClain, J. O. (1993). An overview of production planning. In Handbooks in Operations Research and Management Science (Vol. 4, pp. 333–370).
Tibshirani, R. J. (1996). Regression shrinkage via the LASSO. Journal of the Royal Statistical Society: Series B, 58, 267–288.
Tirkel, I. (2011). Cycle time prediction in wafer fabrication line by applying data mining methods. In IEEE/SEMI Advanced Semiconductor Manufacturing Conference (pp. 1–5).
Villalobos-Arias, L., Quesada-López, C., Guevara-Coto, J., Martínez, A., & Jenkins, M. (2020). Evaluating hyper-parameter tuning using random search in support vector machines for software effort estimation. Proceedings of the ACM Conference on Predictive Models and Data Analytics.
Wang, J., Zhang, J., & Wang, X. (2018). A data driven cycle time prediction with feature selection in a semiconductor wafer fabrication system. IEEE Transactions on Semiconductor Manufacturing, 31(1), 173–182.
Wang, J., Yang, J., Zhang, J., Wang, X., & Zhang, W. (2018). Big data driven cycle time parallel prediction for production planning in wafer manufacturing. Enterprise Information Systems, 12(6), 714–732.
Wold, H. (1966). E). Estimation of principal components and related models by iterative least squares. In Multivariate Analysis (pp. 391–420). Academic Press.
Zarte, M., Pechmann, A., & Nunes, I. (2021). Fuzzy Inference Model for Decision Support in Sustainable Production Planning Processes: A Case Study. Sustainability, 13, 1355.
Zou, H., & Hastie, T. (2003). Regression shrinkage and selection via the elastic net, with applications to microarrays. Journal of the Royal Statistical Society: Series B, 67, 301–320.

Details

Primary Language

English

Subjects

Industrial Engineering

Journal Section

Research Article

Authors

Yunus Demir ^*
0000-0003-3868-1860
Türkiye

Müzeyyen Göksel
0000-0002-5131-8874
Türkiye

Publication Date

April 11, 2026

Submission Date

September 26, 2025

Acceptance Date

November 4, 2025

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.38088/jise.1791714

IZ

https://izlik.org/JA62YL95ZM

Cite

RIS / Bibtex

APA

Demir, Y., & Göksel, M. (2026). Processing Time Estimation in the Textile Warp Preparation Shop with Machine Learning Approaches. Journal of Innovative Science and Engineering, 10(1), 105-117. https://doi.org/10.38088/jise.1791714

AMA

1.Demir Y, Göksel M. Processing Time Estimation in the Textile Warp Preparation Shop with Machine Learning Approaches. JISE. 2026;10(1):105-117. doi:10.38088/jise.1791714

Chicago

Demir, Yunus, and Müzeyyen Göksel. 2026. “Processing Time Estimation in the Textile Warp Preparation Shop With Machine Learning Approaches”. Journal of Innovative Science and Engineering 10 (1): 105-17. https://doi.org/10.38088/jise.1791714.

EndNote

Demir Y, Göksel M (April 1, 2026) Processing Time Estimation in the Textile Warp Preparation Shop with Machine Learning Approaches. Journal of Innovative Science and Engineering 10 1 105–117.

IEEE

[1]Y. Demir and M. Göksel, “Processing Time Estimation in the Textile Warp Preparation Shop with Machine Learning Approaches”, JISE, vol. 10, no. 1, pp. 105–117, Apr. 2026, doi: 10.38088/jise.1791714.

ISNAD

Demir, Yunus - Göksel, Müzeyyen. “Processing Time Estimation in the Textile Warp Preparation Shop With Machine Learning Approaches”. Journal of Innovative Science and Engineering 10/1 (April 1, 2026): 105-117. https://doi.org/10.38088/jise.1791714.

JAMA

1.Demir Y, Göksel M. Processing Time Estimation in the Textile Warp Preparation Shop with Machine Learning Approaches. JISE. 2026;10:105–117.

MLA

Demir, Yunus, and Müzeyyen Göksel. “Processing Time Estimation in the Textile Warp Preparation Shop With Machine Learning Approaches”. Journal of Innovative Science and Engineering, vol. 10, no. 1, Apr. 2026, pp. 105-17, doi:10.38088/jise.1791714.

Vancouver

1.Yunus Demir, Müzeyyen Göksel. Processing Time Estimation in the Textile Warp Preparation Shop with Machine Learning Approaches. JISE. 2026 Apr. 1;10(1):105-17. doi:10.38088/jise.1791714