Engineering

Production Line Optimization Using ANNs-Based Models

Authors

  • E. M. Badawi

    Department of Mechanical Engineering, Sudan University of Science and Technology, Khartoum, Sudan
    Author

  • Hassan Osman

    Department of Mechanical Engineering, Sudan University of Science \& Technology, Khartoum, Sudan
    Author

  • KHOBAIB ELZAIN

    Khobaib Omer Osman Elzain, Department of Mechanical Engineering, Sudan University of Science and Technology, Khartoum - Sudan, Email: khopo122113@gmail.com
    Author
    https://orcid.org/0009-0005-7101-5423

DOI:

https://doi.org/10.71107/6g7rdd93

Keywords:

Artificial Neural Networks , Average Throughout , Overall Equipment Effectiveness

Abstract

Using artificial neural networks (ANNs), this study created two predictive models for production line performance optimization. The AT-ANNs and OEE-ANNs models are ANN-based models that estimate average throughput (AT) and overall equipment effectiveness (OEE). Product type, batch size, target, and availability were used as input parameters in the design of the AT-ANNs model, and the average throughput was used as the output. Product type, batch size, target, availability, and average productivity are all input parameters in the OEE-ANNs model, which outputs OEE. Over the course of three weeks, 200 samples were gathered from a production line simulation. The network was trained using the Levenberg-Marquardt algorithm, which kept the two hidden layers' structure constant. The results demonstrated the superiority of ANNs over traditional regression models, with AT-ANN and OEE-ANN achieving significantly lower RMSE values (1.84 and 0.0316, respectively) compared to the regression model (2.92 and 0.3056). To evaluate the model's practicality, a case study was carried out. In Case~4, the anticipated AT of 146.39 closely matched the actual value of 145, demonstrating that predicted values well matched measured production outputs. The most important factor influencing both AT and OEE, according to the regression analysis, was availability. These results demonstrate the potential of ANN-based predictive models to boost manufacturing efficiency and optimize production scheduling.

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Author Biographies

  • E. M. Badawi, Department of Mechanical Engineering, Sudan University of Science and Technology, Khartoum, Sudan

    Department of Mechanical Engineering, Sudan University of Science and Technology, Khartoum, Sudan

  • Hassan Osman, Department of Mechanical Engineering, Sudan University of Science \& Technology, Khartoum, Sudan

    Department of Mechanical Engineering, Sudan University of Science \& Technology, Khartoum, Sudan

  • KHOBAIB ELZAIN, Khobaib Omer Osman Elzain, Department of Mechanical Engineering, Sudan University of Science and Technology, Khartoum - Sudan, Email: khopo122113@gmail.com

    Khobaib Omer is a mechanical engineer in the Department of Mechanical Engineering at Sudan University of Science and Technology, where he is currently pursuing his Master's degree. He graduated with a Bachelor's degree in Manufacturing Engineering from Nile Valley University in 2017. From 2021 to 2024, he trained at the Sudanese Petroleum Pipelines Company, gaining hands-on experience in pipeline systems, quality control, and project management in the oil and gas sector.

     

     

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Published

2026-05-08

Data Availability Statement

The data will be provided by the request

Issue

Vol. 2 No. 1 (2026): Conclusions in Engineering

Section

Articles

License

Copyright (c) 2026 E. M. Badawi, Hassan Osman, KHOBAIB ELZAIN (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Production Line Optimization Using ANNs-Based Models. (2026). Conclusions in Engineering, 2(1), 23-30. https://doi.org/10.71107/6g7rdd93

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