An Improved Genetic Algorithm with Recurrent Search for the Job-Shop Scheduling Problem

Author

Xing, Yingjie ; Wang, Zhuqing ; Sun, Jing ; Wang, Wanlei

Author_Institution

Key Lab. for Precision & Non-traditional Machining Technol. of Minist. of Educ., Dalian Univ. of Technol.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

3386

Lastpage

3390

Abstract

A genetic algorithm with some improvement is proposed to avoid the local optimum for job-shop scheduling problem (JSP). There is recurrent searching process of genetic operation in the improved genetic algorithm. The improved crossover operation can shake current population from local optimum in genetic algorithm. The recurrent crossover operation and mutation operation can inherit excellent characteristics from parent chromosomes and accelerate the diversity of offspring. Both benchmark FT(6times6) and LA1(10times5) job-shop scheduling problems are used to show the effectiveness of the proposed method. Experimental results demonstrate that the proposed genetic algorithm does not get stuck at a local optimum easily, and it is fast in convergence, simple to be implemented

Keywords

convergence; genetic algorithms; job shop scheduling; search problems; genetic algorithm; job-shop scheduling problem; mutation operation; parent chromosomes; recurrent crossover operation; recurrent search; recurrent searching process; Biological cells; Educational technology; Electronic mail; Genetic algorithms; Genetic mutations; Job shop scheduling; Laboratories; Machining; Optimization methods; Sun; Crossover operation; Genetic algorithm; Job-shop; Recurrent search;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712996

Filename

1712996