基于“炉–机对应”的炼钢–连铸排产灰狼优化算法  

Steelmaking-Continuous Casting Scheduling Model Based on Grey Wolf Algorithm with“Furnace-Caster Matching”Mode

作　　者：陈博[1] 邵鑫 张江山[1] 高山 李宏辉 刘青[1] CHEN Bo;SHAO Xin;ZHANG Jiangshan;GAO Shan;LI Honghui;LIU Qing(State Key Laboratory of Advanced Metallurgy,University of Science and Technology Beijing,Beijing 100083,China;Laiwu Iron and Steel Group Yinshan Section Steel Co.Ltd.,Laiwu 271104,China)

机构地区：[1]北京科技大学绿色低碳钢铁冶金全国重点实验室,北京100083 [2]莱芜钢铁集团银山型钢有限公司,山东莱芜271104

出　　处：《工程科学与技术》2024年第6期73-81,共9页Advanced Engineering Sciences

基　　金：国家自然科学基金面上项目(52374321)。

摘　　要：针对多品种、小批量、多规格、高质量的生产订单导致的前后工序/设备作业周期不匹配、炼钢–连铸区段复杂车间布局导致炉次在工序/设备前的等待时间过长影响生产顺行等问题,本文提出一种基于“炉–机对应”策略的灰狼优化算法,解决炉次在工序/设备前的等待时间最短的排产问题。首先,建立以浇次计划内炉次总等待时间最短为优化目标的炼钢–连铸过程排产模型;其次,引入“炉–机对应”策略求解所建模型,考虑车间布局和运输时间因素,在位置更新时判断个体是否满足“炉–机对应”策略,同时设置炉次等待时间为约束因素,当某一炉次在某道工序的等待时间超过约束值时,则对当前炉次重新求解排产计划。对某无精炼跨大中型炼钢厂的10个实际生产算例进行仿真,结果表明:本文提出的基于“炉–机对应”的灰狼优化算法的性能优于启发式算法和遗传算法;针对某炼钢厂产量占比超过80%的4炉对3机的生产运行模式,基于“炉–机对应”的灰狼优化算法求解计划内炉次的总等待时间平均减少20%,工序/设备前的等待时间超过30 min的炉次占比降低3%;前后工序炉机匹配度明显提升,以算例10的4号精炼炉对应的4号连铸机产线为例,本文算法的层流式“一一对应”的钢水占比由遗传算法的45%提升至51%。本文提出的算法为炼钢厂复杂排产提供了可行的解决方案。Objective The steelmaking plant’s production is characterized by multiple varieties,small batches,and multi-specification orders,which,coupled with a complex workshop layout,lead to low production efficiency in the steelmaking–continuous casting section and an unreasonable production schedule.It becomes evident that random device assignments result in low device operation efficiency and poor coordination by formulating different scheduling plans based on orders and considering the significant differences in operation cycles between upstream and downstream pro-cesses/devices. This misalignment often causes ladles to wait excessively before processing, leading to production delays and potential interrup-tions. Thus, it is essential to consider the operating cycles of steelmaking, refining, and continuous casting, as well as the transportation time between different processes in the workshop, to generate a reasonable production schedule. Methods When the number of casts, steel grades, and heat sequences are known, excluding emergencies such as rush orders and device failures, a steelmaking-continuous casting scheduling model is established with the minimum waiting time as the objective function. This model selects the average transportation time for ladles between devices. Although the refining process offers some buffering capacity, the heat transported along the same process path is not identical in actual production;they are treated as identical in the production schedule. The buffering capacity mainly adjusts the time for different situations;for example, if the transportation time for a particular heat is too long, the refining time can be appropri-ately shortened to ensure the smooth progress of the production schedule. The grey wolf optimization algorithm, enhanced by the “furnace-caster matching” strategy, addresses the issue that the algorithm can be easily affected by the initial population and prone to local optima. The “furnace-caster matching” strategy includes two main components: 1) When t

关 键 词：炼钢–连铸 排产计划 灰狼优化算法 炉–机对应 

分 类 号：TF087[冶金工程—冶金物理化学]