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)...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Wiley
2022-09-01
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Series: | IET Collaborative Intelligent Manufacturing |
Subjects: | |
Online Access: | https://doi.org/10.1049/cim2.12060 |