A deep reinforcement learning based approach for dynamic distributed blocking flowshop scheduling with job insertions

A deep reinforcement learning based approach for dynamic distributed blocking flowshop scheduling with job insertions

Abstract The distributed blocking flowshop scheduling problem (DBFSP) with new job insertions is studied. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. A deep reinforcement learning (DRL)...

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Bibliographic Details
Main Authors: Xueyan Sun, Birgit Vogel‐Heuser, Fandi Bi, Weiming Shen
Format: Article
Language:English
Published: Wiley 2022-09-01
Series:IET Collaborative Intelligent Manufacturing
Subjects:
deep reinforcement learning
distributed blocking flowshop scheduling problem
dynamic scheduling
job insertions
multi‐agent deep deterministic policy gradient
Online Access:https://doi.org/10.1049/cim2.12060

Internet

https://doi.org/10.1049/cim2.12060

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