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Load Balancing of Two-Sided Assembly Line Based on Deep Reinforcement Learning.

Authors :
Jia, Guangpeng
Zhang, Yahui
Shen, Shuqi
Liu, Bozu
Hu, Xiaofeng
Wu, Chuanxun
Source :
Applied Sciences (2076-3417); Jul2023, Vol. 13 Issue 13, p7439, 24p
Publication Year :
2023

Abstract

In the complex and ever-changing manufacturing environment, maintaining the long-term steady and efficient work of the assembly line is the ultimate goal pursued by relevant enterprises, the foundation of which is a balanced load. Therefore, this paper carries out research on the two-sided assembly line balance problem (TALBP) for load balancing. At first, a mathematical programming model is established with the objectives of optimizing the line efficiency, smoothness index, and completion time smoothness index of the two-sided assembly line (TAL). Secondly, a deep reinforcement learning algorithm combining distributed proximal policy optimization (DPPO) and the convolutional neural network (CNN) is proposed. Based on the distributed reinforcement learning agent structure assisted by the marker layer, the task assignment states of the two-sided assembly and decisions of selecting tasks are defined. Task assignment logic and reward function are designed according to the optimization objectives to guide task selection and assignment. Finally, the performance of the proposed algorithm is verified on the benchmark problem. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
13
Issue :
13
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
164921163
Full Text :
https://doi.org/10.3390/app13137439