考虑加热炉生产因素的热轧板坯轧制计划模型与算法

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,抽取板坯标准在炉时间和出炉温度这两个关键因素,建立热轧板坯轧制计划的整数规划模型,并提出自适应邻域搜索算法.在算法中设计约束满足策略、自适应搜索策略和反向学习邻域搜索策略.约束满足策略针对目标特征和加热炉因素设计两种值选择规则,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多种规模的实验,通过实验验证了算法的有效性.

Model and algorithm for rolling planning of hot-rolled slab with reheating furnace production factors

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 plans is established, and an adaptive neighborhood search algorithm is proposed. The constraint satisfaction strategy, the adaptive search strategy and the 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. Using the adaptive search strategy can select neighborhood structure and terminate neighborhood search autonomously, and optimize the neighborhood structure selection process and algorithm convergence speed effectively. The reverse learning neighborhood search strategy enhances the diversity of the solution space based on the reverse learning technology, which can improve the global search ability. Based on actual production data, experiments of various scales are designed to verify the effectiveness of the proposed algorithm.