Paper Info
Title
Transient chaotic discrete neural network for flexible job-shop scheduling
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.
Year
DOI
Venue
2005
10.1007/11427391_122
ISNN (1)
Keywords
Field
DocType
practical engineering situation,transient chaotic discrete neural,discrete neural network,production scheduling program,classical job-shop scheduling problem,real job shop,flexible job-shop scheduling problem,new computational energy function,finite time,job shop scheduling,neural network,production scheduling
Mathematical optimization,Job shop scheduling,Scheduling (computing),Computer science,Job shop,Stochastic neural network,Scheduling (production processes),Time delay neural network,Artificial intelligence,Artificial neural network,Chaotic,Machine learning
Conference
Volume
ISSN
ISBN
3496
0302-9743
3-540-25912-0
Citations 
PageRank 
References 
2
0.48
4
Authors
4
Name
Order
Citations
PageRank
Xinli Xu17910.92
Qiu Guan2439.92
Wan-Liang Wang323539.16
Sheng-Yong Chen41077114.06