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Title:

A Deep Reinforcement Learning based Approach for Dynamic Distributed Blocking Flowshop Scheduling with Job Insertions

Document type:
Zeitschriftenaufsatz
Author(s):
Sun, Xueyan; Vogel-Heuser, Birgit; Bi, Fandi; Shen, Weiming
Abstract:
This paper studies the distributed blocking flowshop scheduling problem (DBFSP) with new job insertions. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. This paper proposes a deep reinforcement learning (DRL) algorithm to optimize the job selection model, and makes local modifications on the basis of the original scheduling plan when new jobs arrive. The objective is to minimize the tota...     »
Journal title:
IET Collaborative Intelligent Manufacturing
Year:
2022
Journal volume:
1
Month:
September
Journal issue:
CIM212060
Pages contribution:
15
Fulltext / DOI:
doi: https://doi.org/10.1049/cim2.12060
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