A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem

Abstract This paper studies the flexible double shop scheduling problem (FDSSP) that considers simultaneously job shop and assembly shop. It brings about the problem of scheduling association of the related tasks. To this end, a reinforcement learning algorithm with a deep temporal difference networ...

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Bibliographic Details
Main Authors: Xiao Wang, Peisi Zhong, Mei Liu, Chao Zhang, Shihao Yang
Format: Article
Language:English
Published: Nature Portfolio 2024-04-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-59414-8

Internet

https://doi.org/10.1038/s41598-024-59414-8

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