Content area

Abstract

In the discrete manufacturing workshop, an unreasonable workshop layout has a significant impact on the production efficiency, which can result in a large distance between operations, low utilization of tooling, and the backlog of products in production process. However, the existing optimization algorithms for workshop layout rarely take into account the real-time feedback of physical information from the workshop such that the layout cannot be self-adjusted to realize the optimum during manufacturing. Thus, this paper focuses on a discrete manufacturing workshop layout optimization based on digital twin, in which the workshop layout problem is solved by twin data fusion, information and physical interaction fusion, and data analysis and optimization. First, a sub-framework of digital twin-based workshop partitioning is established and the workshop partitioning is optimized via simulation analysis. Second, a sub-framework of digital twin-based equipment layout optimization is presented, in which equipment layout decisions are made by real-time data collection and value-added processing of twin data. Then, a sub-framework of digital twin-based distribution route optimization is developed for the workshop. Finally, the proposed method is applied to a welding production workshop and increased the production capacity o by 29.4%.

Details

Title
A digital twin-based layout optimization method for discrete manufacturing workshop
Author
Guo Hongfei 1 ; Zhu, Yingxin 2 ; Zhang, Yu 3 ; Ren Yaping 1 ; Chen Minshi 4 ; Zhang, Rui 5 

 Jinan University, Institute of Physical Internet, Zhuhai, China (GRID:grid.258164.c) (ISNI:0000 0004 1790 3548); Jinan University, School of Intelligent Systems Science and Engineering, Zhuhai, China (GRID:grid.258164.c) (ISNI:0000 0004 1790 3548) 
 Jinan University, School of Intelligent Systems Science and Engineering, Zhuhai, China (GRID:grid.258164.c) (ISNI:0000 0004 1790 3548) 
 Guangzhou CreateView Education Technology Co., Ltd, Guangzhou, China (GRID:grid.258164.c) 
 Sun Yat-sen University, School of Data and Computer Science, Guangzhou, China (GRID:grid.12981.33) (ISNI:0000 0001 2360 039X) 
 Tianjin University of Science and Technology, College of Electronic Information and Automation, Tianjin, China (GRID:grid.413109.e) (ISNI:0000 0000 9735 6249) 
Pages
1307-1318
Publication year
2021
Publication date
Jan 2021
Publisher
Springer Nature B.V.
ISSN
02683768
e-ISSN
14333015
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2477822178
Copyright
© The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021.