基于数据驱动方法的再制造系统瓶颈分析

• 作 者: 薛政 李涛 彭世通 张超勇 张洪潮 FANG Jing-hua
• 出 处: 中南大学学报（英文版）
• 发表日期: 2022年1期
• 关键词: bottleneck identification dynamic bottleneck remanufacturing system auto-regress

再制造系统存在不确定因素和随机事件,使得系统的瓶颈随着外部条件和系统状态变化在时间、空间上发生变动.当前系统存在的瓶颈在下一时刻可能不再是瓶颈,而当前瓶颈对系统的影响将持续到系统平衡发生改变.因此,对瓶颈漂移现象的提前预测成为生产管理领域亟需解决的问题.本文使用Arena建立再制造生产线仿真模型,使用瓶颈指数进行瓶颈识别.然后在获取生产线样本数据的基础上,将工序各时段的瓶颈指数值看作是一组时间序列,基于自回归滑动平均(ARMA)模型预测未来同一时段内各工位的瓶颈指数值,完成对这一时段的瓶颈工位的预测.“,”The remanufacturing system is remolding the manufacturing industry by bringing scrapped products back to such a condition that reintegrated performance is just as good as new. The remanufacturing environment is featured by a far deeper level of uncertainty than new manufacturing, such as probabilistic routing files, and highly variable processing time. The stochastic disturbances result in the production bottlenecks, which constrain the productivity of the job shop. The uncertainties in the remanufacturing process cause the bottlenecks to shift when the workshop is processing. Considering this outstanding problem, many researchers try to optimize the production process to mitigate dynamic bottlenecks toward a balanced state. This paper proposes a data-driven method to predict bottlenecks in the remanufacturing system with multi-variant uncertainties. Firstly, discrete event simulation technology is applied to establish a simulation model of the remanufacturing production line and calculate the bottleneck index to identify bottlenecks. Secondly, a data-driven method, auto-regressive moving average (ARMA) model is employed to predict the bottlenecks in the system based on real-time data captured by the Arena software. Finally, the proposed prediction method is verified on real data from the automobile engine remanufacturing production line.