DocumentCode :
2232489
Title :
Solving the flexible job-shop scheduling problem by immune genetic algorithm
Author :
Ma, Jia ; Zhu, Yunlong ; Shi, Gang
Author_Institution :
Shenyang Inst. of Autom., Chinese Acad. of Sci., Shenyang, China
Volume :
4
fYear :
2010
fDate :
20-22 Aug. 2010
Abstract :
Analyzing the model of the flexible job-shop scheduling problem(FJSP),an immune genetic algorithm(IGA) is proposed to solve the problem. This algorithm preserves the random global search ability of simple genetic algorithm(SGA), and introduces the immune mechanism by which the necessary vaccine may be extracted with the scheduling vaccinated so as to improve efficiently SGA´s low ability for global search because of immature convergence and low local search ability. Thus, the IGA proposed can provide such ability and convergence rate that will implement the global optimum solution. Experimental results showed that the IGA can solve the FJSP effectively.
Keywords :
genetic algorithms; job shop scheduling; flexible job-shop scheduling problem; immune genetic algorithm; random global search ability; simple genetic algorithm; Convergence; Gallium nitride; Vaccines; flexibility; immune genetic algorithm; job-shop scheduling; resource constrained; vaccine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on
Conference_Location :
Chengdu
ISSN :
2154-7491
Print_ISBN :
978-1-4244-6539-2
Type :
conf
DOI :
10.1109/ICACTE.2010.5579716
Filename :
5579716
Link To Document :
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