An End-to-End Deep Learning Method for Dynamic Job Shop Scheduling Problem

An End-to-End Deep Learning Method for Dynamic Job Shop Scheduling Problem

Job shop scheduling problem (JSSP) is essential in the production, which can significantly improve production efficiency. Dynamic events such as machine breakdown and job rework frequently occur in smart manufacturing, making the dynamic job shop scheduling problem (DJSSP) methods urgently needed. E...

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Bibliographic Details
Main Authors: Shifan Chen, Zuyi Huang, Hongfei Guo
Format: Article
Language:English
Published: MDPI AG 2022-07-01
Series:Machines
Subjects:
smart manufacturing
dynamic job shop scheduling problem
deep learning
transformer
spatial pyramid pooling
generalization
Online Access:https://www.mdpi.com/2075-1702/10/7/573

Internet

https://www.mdpi.com/2075-1702/10/7/573

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