摘要
在制造业生产过程中,瓶颈工序并不是一直保持不变的,当各种扰动因素出现时,瓶颈工序可能会随着生产的推进不断发生变化。针对扰动因素引起的工序产能损失导致生产瓶颈发生转移的现象展开研究,用惩罚成本识别了生产线的初始瓶颈。研究了4种主要产能扰动因素耦合对工序可用产能时间造成的损失,并用BP神经网络预测出各工序可用产能时间的损失大小,建立工序产能可用率预测模型,引入新的瓶颈转移指标计算方式间接建立瓶颈转移预测模型。最后采用瓶颈辨识与转移预测流程进行案例分析,并验证了瓶颈转移预测模型的可靠度。
In the manufacturing process,the bottleneck process does not always remain the same.When various disturbance factors appear,the bottleneck process may continue to change with the advancement of production.Research on the phenomenon that the production bottleneck is transferred due to the loss of process capacity caused by disturbance factors was carried out,the initial bottleneck of the production line was identified by the penalty cost method.Considering the loss of the available production time of the process caused by the coupling of the four main capacity disturbance factors,and the BP neural network was used to predict the loss of the available production time of each process,the process capacity availability prediction model was established,and a new bottleneck transfer index calculation method which indirectly established a bottleneck transfer prediction model was introduced.Finally,a case analysis was carried out with the whole set of bottleneck identification and transfer prediction process,and the reliability of the bottleneck transfer prediction model was verified.
作者
陈磊
王相柠
娄恒权
胡雨欣
贾记萌
CHEN Lei;WANG Xiangning;LOU Hengquan;HU Yuxin;JIA Jimeng(School of Energy&Mining,China University of Mining and Technology(Beijing),Beijing 100083,China;Zhengzhou Coal Mine Machinery Group Co.,Ltd.,Zhengzhou 450016,China;Business School,National University of Belarus,Minsk 220030,Belarus;Hebei Hongmao Daily Appliance Technology Co.,Ltd.,Handan 056200,China)
出处
《现代制造工程》
CSCD
北大核心
2022年第4期21-28,共8页
Modern Manufacturing Engineering
关键词
产能损失
惩罚成本
BP神经网络
产能可用率
瓶颈转移预测
capacity loss
penalty cost
BP neural network
capacity availability
bottleneck transfer prediction
