Transient chaotic discrete neural network for flexible job-shop scheduling

Authors:
Xinli Xu;Qiu Guan;Wanliang Wang;Shengyong Chen
Affiliations:
Information Engineering Institute, Zhejiang University of Technology, Hangzhou, Zhejiang, China;Information Engineering Institute, Zhejiang University of Technology, Hangzhou, Zhejiang, China;Information Engineering Institute, Zhejiang University of Technology, Hangzhou, Zhejiang, China;Information Engineering Institute, Zhejiang University of Technology, Hangzhou, Zhejiang, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

As an extension of the classical job-shop scheduling problem, the flexible job-shop scheduling problem (FJSP) allows an operation to be performed by one machine out of a set of machines. To solve the problem in real job shops, this paper presents a method of the discrete neural network with transient chaos (TDNN). The method considers various constraints in a FJSP. Furthermore, a new computational energy function for FJSP is proposed. A production scheduling program is developed in this research for validation and implementation of the proposed method in practical engineering situations. The experimental results show that the method can converge to the global optimum or near to the global optimum in reasonable and finite time.