Research of an Improved Genetic Algorithm for Job Shop Scheduling

Author

Wu Jinghua ; Chen Mianzhou

Author_Institution

Huangshi Inst. of Technol., Huangshi, China

Volume

2

fYear

2010

fDate

13-14 March 2010

Firstpage

1076

Lastpage

1078

Abstract

Job shop scheduling is one of the most difficult NP-hard combinatorial optimize problems, in order to solve this problem, an improved Genetic Algorithm with three-dimensional coded model was put forward in this paper. In this model, the gene was coded with 3-D space, and self-adapting plot was drawn into conventional GA, then the probability of crossover and mutation can automatic adjust by fit degree. The instance shows that this algorithmic is effective to solve job shop scheduling problem.

Keywords

computational complexity; genetic algorithms; job shop scheduling; 3D coded model; 3D space; NP-hard combinatorial optimization problem; improved genetic algorithm; job shop scheduling; self-adapting plot; Automation; Biological cells; Encoding; Equations; Genetic algorithms; Genetic mutations; Job shop scheduling; Mechatronics; Scheduling algorithm; Time measurement; Genetic Algorithm; job shop scheduling; self-adapting formatting; three-dimensional coded model;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on

Conference_Location

Changsha City

Print_ISBN

978-1-4244-5001-5

Electronic_ISBN

978-1-4244-5739-7

Type

conf

DOI

10.1109/ICMTMA.2010.737

Filename

5460201