DocumentCode :
3479917
Title :
Hoprield neural network approach for single machine scheduling problem
Author :
Maheswaran, R. ; Ponnambalam, S.G. ; Samuel, D.N. ; Ramkumar, A.S.
Author_Institution :
Dept. of Mech. Eng., Mepco Shlenk Eng. Coll., Sivakasi
Volume :
2
fYear :
2004
fDate :
1-3 Dec. 2004
Firstpage :
850
Lastpage :
854
Abstract :
This paper presents a Hopfield neural network approach for the problem of scheduling n jobs in a single machine to minimize total weighted tardiness. A binary encoding scheme is introduced to represent the solutions, together with a heuristic to decode. A 10-job problem is solved by sequencing the job using different methods viz. weighted shortest processing time (WSPT) rule, earliest due date (EDD) rule, binary representation and Hopfield neural network. The results show that the Hopfield neural network performs better over others
Keywords :
Hopfield neural nets; minimisation; single machine scheduling; Hopfield neural network; binary encoding; binary representation; earliest due date rule; single machine scheduling; total weighted tardiness; weighted shortest processing time rule; Decoding; Encoding; Hopfield neural networks; Job shop scheduling; Mechanical engineering; Neural networks; Production systems; Resource management; Single machine scheduling; Terminology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems, 2004 IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
0-7803-8643-4
Type :
conf
DOI :
10.1109/ICCIS.2004.1460699
Filename :
1460699
Link To Document :
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