Paper Info
Title
Hybrid Deep Neural Network Scheduler for Job-Shop Problem Based on Convolution Two-Dimensional Transformation.
Abstract
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.
Year
DOI
Venue
2019
10.1155/2019/7172842
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
DocType
Volume
ISSN
Journal
2019.0
1687-5265
Citations 
PageRank 
References 
1
0.35
0
Authors
7
Name
Order
Citations
PageRank
Zelin Zang110.35
Wan-Liang Wang223539.16
Yuhang Song310.35
Linyan Lu410.35
Weikun Li510.35
Yule Wang611.02
Yanwei Zhao73513.48