基于机器学习的炼钢区域天车调度方法  

Machine learning based scheduling method for cranes in steelmaking areas

作　　者：文静 贾树晋[1] WEN Jing;JIA Shujin(Central Research Institute,Baoshan Iron&Steel Co.,Ltd.,Shanghai 201900,China)

机构地区：[1]宝山钢铁股份有限公司中央研究院,上海201900

出　　处：《冶金自动化》2024年第5期53-60,共8页Metallurgical Industry Automation

摘　　要：提高炼钢区域的天车运行效率能够在有效衔接前后工序的前提下减少运输能源消耗,对于绿色生产和降本增效均具有一定价值。基于此,本文提出了由仿真建模和机器学习驱动的天车调度优化方法。首先,采用多智能体技术建立了炼钢区域的生产组织仿真模型,该模型由历史生产计划和天车调度工作流驱动。然后,多次运行仿真模型,通过内置的样本评估公式获得大量高质的天车运行样本。最后,采用随机森林模型对样本进行学习,获得用于匹配天车与运输任务的机器学习模型。实验分析表明,将机器学习模型应用于天车调度决策,能够提高天车有效运输时间占比,从而减少因为运输任务错配、路径避让等带来的能耗损失。在生产负荷较重的情景下,其优势更为显著。此外,天车调度机器学习模型与炼钢计划剥离开来,在实际应用中具有较高的柔性。Improving the operation efficiency of cranes in the steelmaking area can reduce energy consumption for transportation while effectively linking the preceding and succeeding processes,which is of certain value for green production,cost reduction,and efficiency increase.In this regard,this article proposed a crane scheduling optimization method driven by simulation modeling and machine learning.Firstly,multi-agent is used to establish a production simulation model for the steelmaking area,which is driven by historical production plans and crane scheduling workflows.Subsequently,the simulation model is run multiple times to obtain a large number of high-quality crane operation samples through built-in sample evaluation formulas.Finally,a random forest model is employed to learn from the samples and obtain a machine learning model for matching cranes with transportation tasks.Experimental analysis shows that applying the machine learning model to crane scheduling decisions can increase the proportion of effective transportation time,thereby reducing energy consumption losses caused by mismatched transportation tasks,path avoidance,etc.This advantage is particularly significant under heavy production loads.Furthermore,the crane scheduling machine learning model is decoupled from the steelmaking plan,exhibiting high flexibility in practical app lications.

关 键 词：天车调度 炼钢 仿真建模 随机森林 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TF341[自动化与计算机技术—控制科学与工程]