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Journal of System Simulation

Authors

Yingying Xiao, 1. Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing 100854, China; ;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, China; ;3. Science and Technology on Space System Simulation Laboratory, Beijing Simulation Center, Beijing 100854, China;
Wang Mei, 1. Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing 100854, China; ;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, China; ;
Liqin Guo, 1. Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing 100854, China; ;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, China; ;
Xing Chi, 1. Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing 100854, China; ;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, China; ;3. Science and Technology on Space System Simulation Laboratory, Beijing Simulation Center, Beijing 100854, China;
Changhui Zhuang, 1. Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing 100854, China; ;2. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, China; ;

Abstract

Abstract: For the multi-variety and small-batch manufacturing mode, the planning management system cannot be timely automatically adjusted according to uncertain factors. This paper proposes an intelligent manufacturing plan management system based on factory digital twins. The plan management PDCA business process model is established. In addition, the processing logic model and constraints of the periodic static scheduling model and the dynamic emergency scheduling model are proposed. Based on the planning management case of hybrid assembly of two types of aerospace complex products, it is verified that the management model proposed in this paper can support the intelligent dynamic adjustment of plans for different orders and resource states, and improve the processing efficiency of the workshop to cope with uncertain factors.

First Page

2323

Revised Date

2019-07-24

Last Page

2334

CLC

TP391

Recommended Citation

Xiao Yingying, Wang Mei, Guo Liqin, Xing Chi, Zhuang Changhui. Intelligent Manufacturing Plan Management Based on Digital Twins[J]. Journal of System Simulation, 2019, 31(11): 2323-2334.

DOI

10.16182/j.issn1004731x.joss.19-FZ0375

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