Reinforcement-learning-based parallel genetic algorithm for the multi-objective hot-rolling scheduling problem of wide-thick slab
Hot rolling is the core process of steel production, and its scheduling problem is the key to determining the rolling rhythm. Compared with traditional hot-rolling scheduling, hot-rolling scheduling optimisation of wide-thick slabs needs to consider the characteristics of cross rolling and the influ...
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| Published in: | Applied soft computing Vol. 179; p. 113355 |
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| Format: | Journal Article |
| Language: | English |
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01.07.2025
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| ISSN: | 1568-4946 |
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| Abstract | Hot rolling is the core process of steel production, and its scheduling problem is the key to determining the rolling rhythm. Compared with traditional hot-rolling scheduling, hot-rolling scheduling optimisation of wide-thick slabs needs to consider the characteristics of cross rolling and the influence of the reheating furnace, which significantly increase the difficulty of problem-solving. In this paper, the characteristics of cross rolling and the influence of the reheating furnace are analysed, and the hot-rolling scheduling problem of the wide-thick slab (WTS-HRSP) is mapped to a multi-objective asymmetric vehicle routing problem (MAVRP) model. Combined with the multi-objective characteristics of MAVRP, a reinforcement-learning-based parallel genetic algorithm (RPGA) was designed, which implements parallel computation of multiple populations based on the master-slave island model, designs three priority rules to initialise populations, and optimises the parameters of the genetic operation using Q-Learning. Based on the rolling data of the wide-thick slab hot-rolling mill of a Chinese iron and steel group, experiments were conducted to compare RPGA with five advanced multi-objective optimisation algorithms, and the results showed that RPGA had better convergence and stability and was more suitable for the WTS-HRSP. The findings can help optimise hot-rolling scheduling.
•The multi-objective hot-rolling scheduling model of wide-thick slab is proposed.•Designing a reinforcement-learning-based parallel genetic algorithm.•Designing a parallel computation based on the master-slave island model.•Designing the priority rules and Q-Learning to improve the genetic algorithm.•Experiments demonstrate the proposed scheduling algorithm has the best performance. |
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| AbstractList | Hot rolling is the core process of steel production, and its scheduling problem is the key to determining the rolling rhythm. Compared with traditional hot-rolling scheduling, hot-rolling scheduling optimisation of wide-thick slabs needs to consider the characteristics of cross rolling and the influence of the reheating furnace, which significantly increase the difficulty of problem-solving. In this paper, the characteristics of cross rolling and the influence of the reheating furnace are analysed, and the hot-rolling scheduling problem of the wide-thick slab (WTS-HRSP) is mapped to a multi-objective asymmetric vehicle routing problem (MAVRP) model. Combined with the multi-objective characteristics of MAVRP, a reinforcement-learning-based parallel genetic algorithm (RPGA) was designed, which implements parallel computation of multiple populations based on the master-slave island model, designs three priority rules to initialise populations, and optimises the parameters of the genetic operation using Q-Learning. Based on the rolling data of the wide-thick slab hot-rolling mill of a Chinese iron and steel group, experiments were conducted to compare RPGA with five advanced multi-objective optimisation algorithms, and the results showed that RPGA had better convergence and stability and was more suitable for the WTS-HRSP. The findings can help optimise hot-rolling scheduling.
•The multi-objective hot-rolling scheduling model of wide-thick slab is proposed.•Designing a reinforcement-learning-based parallel genetic algorithm.•Designing a parallel computation based on the master-slave island model.•Designing the priority rules and Q-Learning to improve the genetic algorithm.•Experiments demonstrate the proposed scheduling algorithm has the best performance. |
| ArticleNumber | 113355 |
| Author | Yuan, Shuaipeng Wang, Xiqing Li, Tieke Li, Yiren Zhang, Zhuolun Wang, Bailin |
| Author_xml | – sequence: 1 givenname: Zhuolun orcidid: 0000-0002-2134-9629 surname: Zhang fullname: Zhang, Zhuolun email: zhangzl0036@163.com organization: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China – sequence: 2 givenname: Bailin surname: Wang fullname: Wang, Bailin email: wangbl@ustb.edu.cn organization: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China – sequence: 3 givenname: Shuaipeng surname: Yuan fullname: Yuan, Shuaipeng email: shuaipengyuan@163.com organization: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China – sequence: 4 givenname: Yiren surname: Li fullname: Li, Yiren email: liyiren@hbisco.com organization: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China – sequence: 5 givenname: Xiqing surname: Wang fullname: Wang, Xiqing email: wangxiqing@njsteel.com.cn organization: Plate Division of Nanjing Iron and Steel Group, Nanjing 210044, China – sequence: 6 givenname: Tieke surname: Li fullname: Li, Tieke email: tieke@ustb.edu.cn organization: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China |
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| Keywords | Multi-objective asymmetric vehicle routing model Parallel genetic algorithm Q-learning Island model Hot rolling scheduling |
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| Title | Reinforcement-learning-based parallel genetic algorithm for the multi-objective hot-rolling scheduling problem of wide-thick slab |
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