Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation
Joint Authors
Zhao, Yanwei
Wang, Wan Liang
Li, Weikun
Zang, Zelin
Song, Yuhang
Lu, Linyan
Wang, Yule
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-19, 19 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-07-10
Country of Publication
Egypt
No. of Pages
19
Main Subjects
Abstract EN
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 framework is used for solving these subproblems.
HDNNS applies the convolution two-dimensional transformation method (CTDT) to transform irregular scheduling information into regular features so that the convolution operation of deep learning can be introduced into dealing with JSSP.
The simulation experiments designed for testing HDNNS are in the context of JSSPs with different scales of machines and jobs as well as different time distributions for processing procedures.
The results show that the MAKESPAN index of HDNNS is 9% better than that of HNN and the index is also 4% better than that of ANN in ZLP dataset.
With the same neural network structure, the training time of the HDNNS method is obviously shorter than that of the DEEPRM method.
In addition, the scheduler has an excellent generalization performance, which can address large-scale scheduling problems with only small-scale training data.
American Psychological Association (APA)
Zang, Zelin& Wang, Wan Liang& Song, Yuhang& Lu, Linyan& Li, Weikun& Wang, Yule…[et al.]. 2019. Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1129560
Modern Language Association (MLA)
Zang, Zelin…[et al.]. Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-19.
https://search.emarefa.net/detail/BIM-1129560
American Medical Association (AMA)
Zang, Zelin& Wang, Wan Liang& Song, Yuhang& Lu, Linyan& Li, Weikun& Wang, Yule…[et al.]. Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-19.
https://search.emarefa.net/detail/BIM-1129560
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129560