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JACIII Vol.17 No.5 pp. 731-738
doi: 10.20965/jaciii.2013.p0731
(2013)

Paper:

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

Yasumasa Tamura*, Masahito Yamamoto*, Ikuo Suzuki**,
and Masashi Furukawa***

*Graduate School of Information Science and Technology, Hokkaido University, North 14, West 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan

**Department of Computer Science, Kitami Institute of Technology

***Department of System and Informatics, Faculty of Business Administration and Information Science, Hokkaido Information University

Received:
March 20, 2013
Accepted:
June 26, 2013
Published:
September 20, 2013
Creative Commons License
Keywords:
job-shop scheduling problem, dispatching rules, artificial neural network, particle swarm optimization
Abstract
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.
Cite this article as:
Y. Tamura, M. Yamamoto, I. Suzuki, and M. Furukawa, “Acquisition of Dispatching Rules for Job-Shop Scheduling Problem by Artificial Neural Networks Using PSO,” J. Adv. Comput. Intell. Intell. Inform., Vol.17 No.5, pp. 731-738, 2013.
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