A data-driven method to predict future bottlenecks in a remanufacturing system with multi-variant uncertainties

作     者：XUE Zheng LI Tao PENG Shi-tong ZHANG Chao-yong ZHANG Hong-chao 薛政;李涛;彭世通;张超勇;张洪潮

作者机构：School of Mechanical EngineeringDalian University of TechnologyDalian 116024China Department of Mechanical EngineeringShantou UniversityShantou 515000China School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan 430000China Department of IndustrialManufacturing&Systems EngineeringTexas Tech UniversityLubbockTX 79409 USA 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2022年第29卷第1期

页      面：129-145页

核心收录：

学科分类：082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：Projects(51975099 51775086)supported by the Natural Science Foundation of China 

主　　题：bottleneck identification dynamic bottleneck remanufacturing system auto-regressive moving average model 

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 *** remanufacturing environment is featured by a far deeper level of uncertainty than new manufacturing,such as probabilistic routing files,and highly variable processing *** stochastic disturbances result in the production bottlenecks,which constrain the productivity of the job *** uncertainties in the remanufacturing process cause the bottlenecks to shift when the workshop is *** this outstanding problem,many researchers try to optimize the production process to mitigate dynamic bottlenecks toward a balanced *** paper proposes a data-driven method to predict bottlenecks in the remanufacturing system with multi-variant ***,discrete event simulation technology is applied to establish a simulation model of the remanufacturing production line and calculate the bottleneck index to identify ***,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 ***,the proposed prediction method is verified on real data from the automobile engine remanufacturing production line.