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An Enhanced NSGA-II Driven by Deep Reinforcement Learning to Mixed Flow Assembly Workshop Scheduling System with Constraints of Continuous Processing and Mold Changing

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成果类型:
期刊论文
作者:
Yang, Bihao;Chen, Jie;Xiao, Xiongxin;Li, Sidi;Ren, Teng
通讯作者:
Ren, T
作者机构:
[Xiao, Xiongxin; Ren, Teng; Yang, Bihao; Chen, Jie] Cent South Univ Forestry & Technol, Sch Econ & Management, Changsha 410004, Peoples R China.
[Li, Sidi] Cent South Univ Forestry & Technol, Sch Foreign Languages, Changsha 410004, Peoples R China.
通讯机构:
[Ren, T ] C
Cent South Univ Forestry & Technol, Sch Econ & Management, Changsha 410004, Peoples R China.
语种:
英文
关键词:
workshop scheduling system;mixed-flow assembly line;deep reinforcement learning;improved NSGA-II;multi-objective optimization
期刊:
Systems
ISSN:
2079-8954
年:
2025
卷:
13
期:
8
页码:
659-
基金类别:
This work was supported by the National Social Science Fund of China [22BJL114].
机构署名:
本校为第一且通讯机构
院系归属:
外国语学院
摘要:
Mixed-flow assembly lines are widely employed in industrial manufacturing to handle diverse production tasks. For mixed flow assembly lines that involve mold changes and greater processing difficulties, there are currently two approaches: batch production and production according to order sequence. The first approach struggles to meet the processing constraints of workpieces with higher production difficulty, while the second approach requires the development of suitable scheduling schemes to balance mold changes and continuous processing. Therefore, under the second approach, developing an ex...

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