A data-driven method to predict future bottlenecks in a remanufacturing system with multi-variant uncertainties
A data-driven method to predict future bottlenecks in a remanufacturing system with multi-variant uncertainties
复制标题
一种数据驱动的方法来预测具有多变量不确定性的再制造系统中的未来瓶颈
DOI：
10.1007/s11771-022-4906-z
复制
发表时间：
2022-01
影响因子：
4.4
通讯作者：
Hongchao Zhang
中科院分区：
文献类型：
--
作者：
Zheng Xue;Tao Li;Shitong Peng;Chaoyong Zhang;Hongchao Zhang
再制造系统通过将报废产品恢复到综合性能与新产品一样好的状态，正在重塑制造业。再制造环境的特点是具有更深层次的不确定性。
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.
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影响因子：
9.2
作者：
Lin Li
通讯作者：
Lin Li
影响因子：
5.1
作者：
Chai, T.;Draxler, R. R.
通讯作者：
Draxler, R. R.
影响因子：
9.2
作者：
K. M. Muthiah;Samuel H. Huang
通讯作者：
K. M. Muthiah;Samuel H. Huang
基于混合模型的股指预测
DOI：
10.1016/j.omega.2011.07.008
发表时间：
2012-12-01
影响因子：
6.9
作者：
Wang, Ju-Jie;Wang, Jian-Zhou;Guo, Shu-Po
通讯作者：
Guo, Shu-Po
DOI：
10.1016/j.promfg.2019.03.023
发表时间：
2019
期刊：
Procedia Manufacturing
影响因子：
--
作者：
Constantin Hofmann;T. Staehr;Samuel Cohen;N. Stricker;B. Haefner;G. Lanza
通讯作者：
Constantin Hofmann;T. Staehr;Samuel Cohen;N. Stricker;B. Haefner;G. Lanza