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Optimizing production planning and sequencing in hot strip mills: an approach using multi-objective genetic algorithms

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Abstract

Planning and sequencing for hot strip mills in the steel industry is a challenging, complex problem that has fascinated optimization researchers and practitioners alike. This paper applies a combinatory heuristic search and a multi-objective metaheuristic that is a novel approach called HSMO-NSGA-II and employs the HSMO heuristic search method and NSGA-II multi-objective genetic optimization as a metaheuristic algorithm to address complex hot strip mills scheduling tasks. This research aims to enhance the efficiency and effectiveness of production planning and sequencing in hot strip mill, while minimizing operational costs and maximizing rolling utilization. The output consists of slabs categorized into three parts, which converge toward a set of Pareto-optimal solutions while maintaining diversity across the entire solution space. The results demonstrate a significant improvement in comparing the base methods with the HSMO-NSGA-II method, and the proposed method shows better average performance at 23.01%. Notably, the HSMO-NSGA-II method demonstrated a remarkable improvement in performance across the evaluated scenarios, showcasing its potential to enhance productivity and operational efficiency in industrial applications significantly. These findings not only support the viability of using advanced genetic algorithms in complex industrial settings but also open avenues for future research into hybrid optimization techniques.

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Data availability

No datasets were generated or analysed during the current study.

Notes

  1. www.msc.ir

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Acknowledgements

The authors wish to express their deepest gratitude to Mobarakeh Steel Company (MSC) for their invaluable collaboration and for providing the essential data for this research. They are particularly indebted to Engineer Mehdi Ashrafi, Chief of the Digital Transformation Department at Mobarakeh Steel Company, whose expert guidance and unwavering support were instrumental to the success of this work.

Funding

This research did not receive financial support from any source.

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Authors and Affiliations

  1. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Hamidreza Fardad, Faramarz Safi-Esfahani & Behrang Barekatain

  2. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Hamidreza Fardad, Faramarz Safi-Esfahani & Behrang Barekatain

Authors
  1. Hamidreza Fardad
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  2. Faramarz Safi-Esfahani
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Contributions

In terms of author contributions: Hamidreza Fardad, Faramarz Safi-Esfahani (Corresponding author), and Behrang Barekatain all contributed to the manuscript. Their specific contributions are as follows: —Hamidreza Fardad, as a Ph.D. student, engaged in conceptualization, formalization, programming, and drafting and manuscript preparation.—Faramarz Safi-Esfahani is identified as the corresponding author, supervisor, and significantly contributed to the conceptualization, methodology, and overall coordination of the research project.—Behrang Barekatain, as a consultant, contributed to the initial idea preparation and reviewed the draft.

Corresponding author

Correspondence to Faramarz Safi-Esfahani.

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The study did not involve the participation of human subjects or animals. Therefore, no information related to human participants or animals is provided in this research. All necessary ethical approvals required for the execution of our research have been diligently obtained and are explicitly detailed in this manuscript.

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Fardad, H., Safi-Esfahani, F. & Barekatain, B. Optimizing production planning and sequencing in hot strip mills: an approach using multi-objective genetic algorithms. J Supercomput 81, 88 (2025). https://doi.org/10.1007/s11227-024-06469-z

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