考虑加热炉生产因素的热轧板坯轧制计划模型与算法
DOI:
作者:
作者单位:

北京科技大学经济管理学院

作者简介:

通讯作者:

中图分类号:

TP301

基金项目:

国家自然科学基金资助项目(71701016,71231001);教育部人文社会科学研究青年基金项目资助(17YJC630143);北京市自然科学基金项目(9174038);中央高校基本科研业务费资助项目(FRF-BD-20-16A)


Model and Algorithm for Rolling Planning of Hot-rolled Slab with Reheating Furnace Production Factors
Author:
Affiliation:

School of Economics and Management,University of Science and Technology Beijing

Fund Project:

Supported by National Natural Science Foundation of China (No. 71701016,71231001), Humanity and Social Science Youth foundation of Ministry of Education of China (No. 17YJC630143), Beijing Natural Science Foundation (No. 9174038), and the Fundamental Research Funds for Central Universities (No. FRF-BD-20-16A)

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    加热炉生产是影响热轧机组利用率和轧制计划质量的重要环节之一。通过分析加热炉对热轧生产的影响,抽取出板坯标准在炉时间和出炉温度这两个关键因素,建立了热轧板坯轧制计划的整数规划模型,并提出了自适应邻域搜索算法。在算法中设计了约束满足策略、自适应搜索策略和反向学习邻域搜索策略,其中约束满足策略针对目标特征和加热炉因素设计了两种值选择规则,用于生成高质量初始解;自适应搜索策略能够自主选择邻域结构和终止邻域搜索,有效优化邻域结构选择方式和算法收敛速度;反向学习邻域搜索策略基于反向学习技术增强解空间多样性,提高全局搜索能力。基于实际生产数据设计了多种规模的实验,验证了算法的有效性。

    Abstract:

    Reheating furnace production is one of the important procedures that affect the utilization rate of hot rolling mills and the quality of rolling plans. By analyzing the influence of reheating furnace on hot rolling production, two key factors of slab, standard time in furnace and discharge temperature, are extracted. The integer programming model of hot-rolled slab rolling plan is established, and an adaptive neighborhood search algorithm is proposed. Constraint satisfaction strategy, adaptive search strategy and reverse learning neighborhood search strategy are designed in the algorithm. Two value selection rules of the constraint satisfaction strategy are designed for target characteristics and furnace factors to generate high-quality initial solutions; By using the adaptive search strategy, neighborhood structure can be autonomously selected and neighborhood search can be terminated autonomously, the neighborhood structure selection process and algorithm convergence speed are effectively optimized; the reverse learning neighborhood search strategy is based on the reverse learning technology to enhance the diversity of the solution space and improve the global search ability. Based on actual production data, experiments of various scales are designed to verify the effectiveness of the algorithm.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-01-29
  • 最后修改日期:2021-03-30
  • 录用日期:2021-04-07
  • 在线发布日期:
  • 出版日期: