Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation

In this paper, a hybrid deep neural network scheduler (HDNNS) is proposed to solve job-shop scheduling problems (JSSPs). In order to mine the state information of schedule processing, a job-shop scheduling problem is divided into several classification-based subproblems. And a deep learning framewor...

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Detalles Bibliográficos
Autores principales: Zang, Zelin, Wang, Wanliang, Song, Yuhang, Lu, Linyan, Li, Weikun, Wang, Yule, Zhao, Yanwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6652087/
https://www.ncbi.nlm.nih.gov/pubmed/31379935
http://dx.doi.org/10.1155/2019/7172842

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6652087/
https://www.ncbi.nlm.nih.gov/pubmed/31379935
http://dx.doi.org/10.1155/2019/7172842

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