DocumentCode :
441987
Title :
Solving the job shop scheduling problem by an immune algorithm
Author :
Zuo, Xing-quan ; Fan, Yu-shun
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Volume :
6
fYear :
2005
fDate :
18-21 Aug. 2005
Firstpage :
3282
Abstract :
An immune algorithm is presented for solving the job shop scheduling problem. In the algorithm, the niche technology is used to keep the diversity of the population and chaos variables are employed to perform antibody mutation. The code of an antibody is based on random keys, and a heuristic process is given to decode the antibody into a parameterized active schedule to reduce the solution space. Experimental results demonstrate the algorithm is effective for solving job shop problems.
Keywords :
evolutionary computation; job shop scheduling; optimisation; evolutionary algorithm; heuristic process; immune algorithm; job shop scheduling problem; optimization computation; random key; Artificial neural networks; Computer networks; Delay effects; Genetic mutations; Immune system; Job shop scheduling; Optimal scheduling; Processor scheduling; Scheduling algorithm; Space technology; Job shop scheduling; evolutionary algorithm; immune algorithm; optimization computation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
Type :
conf
DOI :
10.1109/ICMLC.2005.1527509
Filename :
1527509
Link To Document :
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