Solve Job-shop Scheduling Problem Based on Cooperative Optimization

Author

Jian-Jia, He ; Chun-Ming, Ye ; Fu-Yuan, Xu ; Lin, Ye ; He, Huang

Author_Institution

Bus. Sch., Univ. of Shanghai for Sci. & Technol., Shanghai, China

fYear

2010

fDate

7-9 May 2010

Firstpage

2599

Lastpage

2602

Abstract

Coping with such disadvantages of particle swarm optimization algorithm and GA as being easy to run into local optima, the method of cooperative optimization is proposed to solve the job shop scheduling problem by combing the quantum-behaved particle swarm optimization and GA. The algorithm applied the parallel hybrid architecture of collaborative quantum particle swarm and GA, in which a kind migration operator was designed to associate all population, and the result shows that this algorithm has better answers and more rapid convergence.

Keywords

genetic algorithms; job shop scheduling; particle swarm optimisation; GA; cooperative optimization; genetic algorithm; job shop scheduling problem; local optima; migration operator; parallel hybrid architecture; quantum behaved particle swarm optimization; Algorithm design and analysis; Gallium; Job shop scheduling; Optimal scheduling; Particle swarm optimization; GA; cooperative optimization; job-shop scheduling problem; quantum particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

E-Business and E-Government (ICEE), 2010 International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-0-7695-3997-3

Type

conf

DOI

10.1109/ICEE.2010.657

Filename

5593113