Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep...

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Detalles Bibliográficos
Autores principales: Yang, Shengluo, Xu, Zhigang, Wang, Junyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867337/
https://www.ncbi.nlm.nih.gov/pubmed/33540868
http://dx.doi.org/10.3390/s21031019

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7867337/
https://www.ncbi.nlm.nih.gov/pubmed/33540868
http://dx.doi.org/10.3390/s21031019

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