数字孪生下矿井生产中心车间调度优化问题  

Workshop scheduling optimization in mine production center under digital twins

作　　者：李世玺 樊凌云 刘明 李鹏阳[2] 赵安新[3] 赵业凯 Li Shixi;Fan Lingyun;Liu Ming;Li Pengyang;Zhao Anxin;Zhao Yekai(Production Service Center,Shanxi Zhengtong Coal Industry Co.,Ltd.,Xianyang 713600,China;School of Mechanical and Precision Instrument Engineering,Xi′an University of Technology,Xi′an 710048,China;College of Communication and Information Technology,Xi′an University of Science and Technology,Xi′an 710054,China)

机构地区：[1]陕西正通煤业有限责任公司生产服务中心,陕西咸阳713600 [2]西安理工大学机械与精密仪器工程学院,陕西西安710048 [3]西安科技大学通信与信息工程学院,陕西西安710054

出　　处：《煤炭科技》2025年第2期1-9,共9页Coal Science & Technology Magazine

基　　金：国家自然科学基金项目(52075439);陕西省重点研发计划工业领域一般项目(2024GX-YBXM-526)。

摘　　要：针对传统车间调度过程中经常忽略的加工设备故障、维修、物料运输阻塞等干扰因素及其实时性差、理论与实际偏差大等问题,提出一种基于数字孪生的遗传算法流水线车间调度方法。基于矿井实际生产过程的需求,构建了数字孪生流水线车间调度仿真模型,通过遗传算法解决生产设备故障等车间突发事件对生产进程的影响问题。以某缸盖自动化流水线车间调度为例,添加并利用甘特图分析了理想状态、双扰动、多扰动条件下调度优化,证明了该算法与实际生产相比加工速度更快,理想状态、双扰动、多扰动条件下加工时间分别为1165、1188.18、1221.08 min,同时得到多扰动条件下偏差率极低,为1.13%。通过缸盖自动化流水线车间调度实验,表明了采用数字孪生的遗传算法流水线车间调度方法的优越性,以及面对矿井生产的恶劣环境数据监测可视化的先进性。To address the deficiencies of traditional workshop scheduling processes that often overlooked interference factors such as equipment failures,maintenance,and material transport obstacles,as well as issues of poor real-time responsiveness and discrepancies between theory and practice,a method for assembly line workshop scheduling based on a genetic algorithm of digital twins was introduced.Based on the actual production process requirements in underground mines,a simulation model of the digital twins for assembly line workshop scheduling was constructed to solve the impact of workshop emergencies such as production equipment failures on the production process through genetic algorithms.The scheduling of an automated cylinder head assembly line workshop was taking as an example,a Gantt chart was added and used to analyze the scheduling optimization under ideal state,double disturbance,and multiple disturbance conditions.It was proved that the algorithm has a faster processing speed compared to actual production,with processing times of 1165,1188.18,and 1221.08 minutes under ideal,double disturbance,and multiple disturbance conditions,respectively.At the same time,the deviation rate under multiple disturbance conditions was extremely low,at 1.13%.And the experiment of automated cylinder head assembly line workshop scheduling show that the advantages of the assembly line workshop scheduling method using digital twin genetic algorithm and the progressiveness of data monitoring visualization in the face of harsh environment of mine production.

关 键 词：数字孪生 流水线车间 调度优化 遗传算法 约束偏差 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]