A data-driven method to predict future bottlenecks in a remanufacturing system with multi-variant uncertainties基于数据驱动方法的再制造系统瓶颈分析

被引:0
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作者
Zheng Xue
Tao Li
Shi-tong Peng
Chao-yong Zhang
Hong-chao Zhang
机构
[1] Dalian University of Technology,School of Mechanical Engineering
[2] Shantou University,Department of Mechanical Engineering
[3] Huazhong University of Science and Technology,School of Mechanical Science and Engineering
[4] Texas Tech University,Department of Industrial, Manufacturing & Systems Engineering
来源
关键词
bottleneck identification; dynamic bottleneck; remanufacturing system; auto-regressive moving average model; 瓶颈识别; 瓶颈漂移; 再制造; 自回归滑动平均模型;
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学科分类号
摘要
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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页码:129 / 145
页数:16
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