A Synchronized Decision-Making Method for Highly Dynamic Production System Based on Digital-twin

doi:10.3969/j.issn.1007-7375.2021.02.008

Abstract

Abstract: The random and highly dynamic interference caused by individual requirements makes it more difficult for the production system to operate efficiently. How to build a multi-unit online synchronized decision-making method for high dynamic interference to support the efficient and collaborative operation of each production unit has become a key challenge. Taking the multi-unit synchronized operation of modular production as the research object, a multi-unit synchronized decision-making architecture, mechanism, and model based on digital twins are built. Through real-time accurate mapping and dynamic virtual simulation, online collaborative operation and autonomous synchronized decision-making of highly dynamic production systems are realized. Finally, the multi-unit synchronized operation of a large chemical company is taken as an example to verify that the method proposed can achieve the coordinated operation of complex multi-unit production system under high dynamic interference.

Key words: digital-twin, collaborative optimization, synchronized decision-making, customized production

CLC Number:

TP39
TH16

JIANG Hongfei, QU Ting, ZHANG Kai, HUANG George Q. A Synchronized Decision-Making Method for Highly Dynamic Production System Based on Digital-twin[J]. Industrial Engineering Journal, 2021, 24(2): 57-67.

References

[1] 李帆, 高东, 张玉良. 射频识别技术在定制化产品柔性制造系统中的设计与应用[J]. 组合机床与自动化加工技术, 2017(12): 113-116
LI Fan, GAO Dong, ZHANG Yuliang. The design and application of RFID in the FMS of product customization[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2017(12): 113-116
[2] 牟小俐, 邓毅, 汪洋. 面向21世纪的柔性制造模式—模块化生产[J]. 科技管理研究, 2007, 27(9): 190-192
MOU Xiaoli, DENG Yi, WANG Yang. Flexible manufacturing model for the 21st century-modular production[J]. Science and Technology Management Research, 2007, 27(9): 190-192
[3] 李浩, 祁国宁, 纪杨建, 等. 面向服务的产品模块化设计方法及其展望[J]. 中国机械工程, 2013, 24(12): 1687-1695
LI Hao, QI Guoning, JI Yangjian, et al. Service oriented product modular design method and its prospects[J]. China Mechanical Engineering, 2013, 24(12): 1687-1695
[4] 刘志, 李帮义, 程晋石, 等. 基于模块化设计的制造/再制造生产决策[J]. 计算机集成制造系统, 2016, 22(4): 935-944
LIU Zhi, LI Bangyi, CHENG Jinshi, et al. Production decision of manufacturing/remanufacturing based on modularity design[J]. Computer Integrated Manufacturing System, 2016, 22(4): 935-944
[5] 王海军, 徐伟, 宋红英, 等. 从内部优化到全局协同: 模块化生产网络构建策略[J]. 科技管理研究, 2017(1): 124-127
WANG Haijun, XU Wei, SONG Hongying, et al. From internal optimization to global collaboration: research on the strategies of building modular production networks[J]. Science and Technology Management Research, 2017(1): 124-127
[6] 米雷雨. 模块化生产在复杂系统制造过程的运用研究[J]. 工业工程, 2018, 21(4): 34-42
MI Leiyu. An application research of modular production in complex system manufacturing process[J]. Industrial Engineering Journal, 2018, 21(4): 34-42
[7] 屈挺, 张凯, 李从东, 等. 物联网环境下面向高动态性生产系统优态运行的联动决策与控制方法[J]. 机械工程学报, 2018, 54(16): 24-33
QU Ting, ZHANG Kai, LI Congdong, et al. Synchronization decision-making and control method for the high-dynamic production system operation in opti-stage under environment of Internet of things[J]. Journal of Mechanical Engineering, 2018, 54(16): 24-33
[8] 陶飞, 张萌, 程江峰, 等. 数字孪生车间: 一种未来车间运行新模式[J]. 计算机集成制造系统, 2017, 23(1): 1-9
TAO Fei, ZHANG Meng, CHENG Jiangfeng, et al. Digital twin workshop: a new paradigm for future workshop[J]. Computer Integrated Manufacturing System, 2017, 23(1): 1-9
[9] 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 4-21
TAO Fei, LIU Weiran, LIU Jianhua, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 4-21
[10] CAI Y, STARLY B, COHEN P, et al. Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing[J]. Procedia Manufacturing, 2017, 10: 1031-1042
[11] UHLEMANN T H J, SCHOCK C, LEHMANN C, et al. The digital twin: demonstrating the potential of real time data acquisition in production systems[J]. Procedia Manufacturing, 2017, 9: 113-120
[12] BAZILEVS Y, DENG X, KOROBENKO A, et al. Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor data[J]. Journal of Applied Mechanics, 2015, 82(9): 091008
[13] QU Ting, PAN Yanghua, LIU Xuan, et al. Internet of things-based real-time production logistics synchronization mechanism and method toward customer order dynamics[J]. Transactions of the Institute of Measurement and Control, 2017, 39(4): 429-445
[14] 周达坚, 屈挺, 张凯, 等. 数字孪生驱动的工业园区“产-运-存”联动决策与控制方法[J]. 计算机集成制造., 2019, 25(6): 1576-1590
ZHOU Dajian, QU Ting, ZHANG Kai, et al. Digital twin-drived synchronized decision-making and control method of production-transportation-storage of industrial park[J]. Computer Intergrated Manufacturing Systems, 2019, 25(6): 1576-1590
[15] KIM H M, MICHELENA N F, PAPALAMBROS P Y, et al. Target cascading in optimal system design[J]. Journal of Mechanical Design, 2003, 125(3): 474-480
[16] KANG N, KOKKOLARAS M, PAPALAMBROS P, et al. Optimal design of commercial vehicle systems using analytical target cascading[J]. Structural & Multidisciplinary Optimization, 2014, 50(6): 1103-1114
[17] QU T, NIE D X, CHEN X, et al. Optimal configuration of cluster supply chains with augmented Lagrange coordination[J]. Computers & Industrial Engineering, 2015, 84(C): 43-55
[18] ZHANG Y, ZHANG G, QU T, et al. Analytical target cascading for optimal configuration of cloud manufacturing services[J]. Journal of Cleaner Production, 2017, 151: 330-343