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Author:

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

Indexed by:

CPCI-S EI Scopus

Abstract:

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 Qnetwork (DSAS-D(3)QN). 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-D(3)QN'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-D(3)QN to unstable environmental changes.

Keyword:

Random job arrival Deep reinforcement learning Dynamic flexible job shop scheduling Urgent job insertion

Author Community:

  • [ 1 ] [Du, Jiarong]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Li, Fangyu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Han, Honggui]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Du, Jiarong]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 5 ] [Li, Fangyu]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 6 ] [Han, Honggui]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 7 ] [Du, Jiarong]Beijing Univ Technol, Engn Res Ctr Digital Community, Minist Educ, Beijing 100124, Peoples R China
  • [ 8 ] [Li, Fangyu]Beijing Univ Technol, Engn Res Ctr Digital Community, Minist Educ, Beijing 100124, Peoples R China
  • [ 9 ] [Han, Honggui]Beijing Univ Technol, Engn Res Ctr Digital Community, Minist Educ, Beijing 100124, Peoples R China
  • [ 10 ] [Du, Jiarong]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China
  • [ 11 ] [Li, Fangyu]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China
  • [ 12 ] [Han, Honggui]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Du, Jiarong]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Du, Jiarong]Beijing Univ Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China;;[Du, Jiarong]Beijing Univ Technol, Engn Res Ctr Digital Community, Minist Educ, Beijing 100124, Peoples R China;;[Du, Jiarong]Beijing Univ Technol, Beijing Artificial Intelligence Inst, Beijing 100124, Peoples R China

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Source :

PROCEEDINGS OF THE 36TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC 2024

ISSN: 1948-9439

Year: 2024

Page: 5715-5720

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

Affiliated Colleges:

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