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A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED

Author

Listed:
  • Xiaowu Chen

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
    Research Center of Biologic Manipulator and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Guozhang Jiang

    (Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
    3D Printing and Intelligent Manufacturing Engineering Institute, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Yongmao Xiao

    (School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 558000, China)

  • Gongfa Li

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    Research Center of Biologic Manipulator and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Feng Xiang

    (Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
    Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China)

Abstract

Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.

Suggested Citation

  • Xiaowu Chen & Guozhang Jiang & Yongmao Xiao & Gongfa Li & Feng Xiang, 2021. "A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED," Mathematics, MDPI, vol. 9(18), pages 1-25, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2256-:d:635209
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    References listed on IDEAS

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    1. Antonio Jiménez-Martín & Alfonso Mateos & Josefa Z. Hernández, 2021. "Aluminium Parts Casting Scheduling Based on Simulated Annealing," Mathematics, MDPI, vol. 9(7), pages 1-18, March.
    2. Jianyu Long & Zhong Zheng & Xiaoqiang Gao, 2017. "Dynamic scheduling in steelmaking-continuous casting production for continuous caster breakdown," International Journal of Production Research, Taylor & Francis Journals, vol. 55(11), pages 3197-3216, June.
    3. Lixin Tang & Ying Meng & Zhi-Long Chen & Jiyin Liu, 2016. "Coil Batching to Improve Productivity and Energy Utilization in Steel Production," Manufacturing & Service Operations Management, INFORMS, vol. 18(2), pages 262-279, May.
    4. Hao-Xiang Wang & Hong-Sen Yan, 2016. "An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1085-1095, October.
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    Cited by:

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