A solution of job-shop scheduling problems based on genetic algorithms

Author

Li, Xiu ; Liu, Wenhuang ; Ren, Shouju ; Wang, Xuerui

Author_Institution

Nat. CIMS-ERC, Tsinghua Univ., Beijing, China

Volume

3

fYear

2001

fDate

2001

Firstpage

1823

Abstract

Based on the analysis of various ways of solving job-shop scheduling problems, the mathematical model of job-shop problem is introduced. A kind of genetic algorithm is used to solve it in this paper. Firstly, the chromosomes are encoded, and the population size is premised for the optimized goal of job-shop problem, which are the keys of the method. Secondly, the fitness function is designed. After using selection, crossover and mutation operator, and then elitist strategy to prevent the premature convergence, a best or satisfactory scheduling path can be found. The executing results using the presented algorithms in certain project case are shown in the paper

Keywords

convergence; genetic algorithms; production control; GA; chromosome encoding; crossover operator; elitist strategy; genetic algorithms; job-shop scheduling problems; mutation operator; premature convergence; selection operator; Algorithm design and analysis; Biological cells; Genetic algorithms; Genetic mutations; Job production systems; Job shop scheduling; Mathematical model; Optimization methods; Scheduling algorithm; Simulated annealing;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 2001 IEEE International Conference on

Conference_Location

Tucson, AZ

ISSN

1062-922X

Print_ISBN

0-7803-7087-2

Type

conf

DOI

10.1109/ICSMC.2001.973587

Filename

973587