Acquisition of Dispatching Rules for Job-Shop Scheduling Problem by Artificial Neural Networks Using PSO

被引：5
|
作者：
Tamura, Yasumasa [1 ]
Yamamoto, Masahito [1 ]
Suzuki, Ikuo [2 ]
Furukawa, Masashi [3 ]
机构：
[1] Hokkaido Univ, Grad Sch Informat Sci & Technol, Kita Ku, North 14,West 9, Sapporo, Hokkaido 0600814, Japan
[2] Kitami Inst Technol, Dept Comp Sci, Kitami, Hokkaido, Japan
[3] Hokkaido Informat Univ, Fac Business Adm & Informat Sci, Dept Syst & Informat, Ebetsu, Hokkaido, Japan
来源：
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2013年 / 17卷 / 05期
关键词：
job-shop scheduling problem; dispatching rules; artificial neural network; particle swarm optimization;
D O I：
10.20965/jaciii.2013.p0731
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A Job-shop Scheduling Problem (JSP) constitutes the basic scheduling problem that is observed in manufacturing systems. In conventional JSP, feature values of work and queue times are used to formulate dispatching rules for scheduling. In this paper, an Artificial Neural Network (ANN) is used to create an index for job priority. Furthermore, in order to optimize the output of the ANN, Particle Swarm Optimization (PSO) is used in unsupervised learning of the synaptic weights for the ANN. The functions of the proposed method are discussed in this paper.
引用
收藏
页码：731 / 738
页数：8
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