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An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants

Author

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  • Dayong Han

    (Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430000, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430000, China)

  • Qiuhua Tang

    (Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430000, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430000, China)

  • Zikai Zhang

    (Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430000, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430000, China)

  • Zixiang Li

    (Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430000, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430000, China)

Abstract

Steelmaking and the continuous-casting (SCC) scheduling problem is a realistic hybrid flow shop scheduling problem with continuous-casting production at the last stage. This study considers the SCC scheduling problem with diverse products, which is a vital and difficult problem in steel plants. To tackle this problem, this study first presents the mixed-integer linear programming (MILP) model to minimize the objective of makespan. Then, an improved migrating birds optimization algorithm (IMBO) is proposed to tackle this considered NP-hard problem. In the proposed IMBO, several improvements are employed to achieve the proper balance between exploration and exploitation. Specifically, a two-level decoding procedure is designed to achieve feasible solutions; the simulated annealing-based acceptance criterion is employed to ensure the diversity of the population and help the algorithm to escape from being trapped in local optima; a competitive mechanism is developed to emphasize exploitation capacity by searching around the most promising solution space. The computational experiments demonstrate that the proposed IMBO obtains competing performance and it outperforms seven other implemented algorithms in the comparative study.

Suggested Citation

  • Dayong Han & Qiuhua Tang & Zikai Zhang & Zixiang Li, 2020. "An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants," Mathematics, MDPI, vol. 8(10), pages 1-28, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:10:p:1661-:d:420128
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    References listed on IDEAS

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    1. Ruiz, Rubén & Vázquez-Rodríguez, José Antonio, 2010. "The hybrid flow shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 205(1), pages 1-18, August.
    2. Pan, Quan-Ke, 2016. "An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling," European Journal of Operational Research, Elsevier, vol. 250(3), pages 702-714.
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    2. Bingyin Lei & Yue Ren & Ziyang Wang & Xinquan Ge & Xiaolin Li & Kaiye Gao, 2023. "The Optimization of Working Time for a Consecutively Connected Production Line," Mathematics, MDPI, vol. 11(2), pages 1-12, January.

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