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作者:

Du, Jiarong (Du, Jiarong.) | Li, Fangyu (Li, Fangyu.) | Han, Honggui (Han, Honggui.)

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EI Scopus

摘要:

The growing complexity of the industrial production environment introduces various uncertain events, resulting in increased process instability and reduced production efficiency. The stable scheduling method is crucial to ensure the continuous and stable operation of the production process. However, the existing dynamic flexible job shop scheduling problem (DFJSP) research lacks stability analysis of the scheduling method. To optimize the production process, we propose a dynamic stability-aware scheduling method based on dueling double deep Q-network (DSAS-D3QN). The method solves DFJSP with multiple uncertain events and reduces the job processing time. Meanwhile, we analyze the stability of the scheduling method by describing the overall distribution of DSAS-D3QN's reward function values. The experiment shows that the proposed method can effectively reduce the processing time while ensuring the stability of the scheduling. Through stability analysis, we verify the adaptability of DSAS-D3QN to unstable environmental changes. © 2024 IEEE.

关键词:

Stability Uncertainty analysis Job shop scheduling Production efficiency Reinforcement learning Deep learning

作者机构:

  • [ 1 ] [Du, Jiarong]Faculty Of Information Technology, Beijing University Of Technology, Beijing; 100124, China
  • [ 2 ] [Du, Jiarong]Beijing Key Laboratory Of Computational Intelligence And Intelligent System, Beijing University Of Technology, Beijing; 100124, China
  • [ 3 ] [Du, Jiarong]Engineering Research Center Of Digital Community Ministry Of Education, Beijing University Of Technology, Beijing; 100124, China
  • [ 4 ] [Du, Jiarong]Beijing Artificial Intelligence Institute, Beijing University Of Technology, Beijing; 100124, China
  • [ 5 ] [Li, Fangyu]Faculty Of Information Technology, Beijing University Of Technology, Beijing; 100124, China
  • [ 6 ] [Li, Fangyu]Beijing Key Laboratory Of Computational Intelligence And Intelligent System, Beijing University Of Technology, Beijing; 100124, China
  • [ 7 ] [Li, Fangyu]Engineering Research Center Of Digital Community Ministry Of Education, Beijing University Of Technology, Beijing; 100124, China
  • [ 8 ] [Li, Fangyu]Beijing Artificial Intelligence Institute, Beijing University Of Technology, Beijing; 100124, China
  • [ 9 ] [Han, Honggui]Faculty Of Information Technology, Beijing University Of Technology, Beijing; 100124, China
  • [ 10 ] [Han, Honggui]Beijing Key Laboratory Of Computational Intelligence And Intelligent System, Beijing University Of Technology, Beijing; 100124, China
  • [ 11 ] [Han, Honggui]Engineering Research Center Of Digital Community Ministry Of Education, Beijing University Of Technology, Beijing; 100124, China
  • [ 12 ] [Han, Honggui]Beijing Artificial Intelligence Institute, Beijing University Of Technology, Beijing; 100124, China

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来源 :

年份: 2024

页码: 5715-5720

语种: 英文

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 2

ESI高被引论文在榜: 0 展开所有

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