Self-adapted Hybrid Genetic Algorithm for Job Scheduling of Distributed Manufacturing System

Author

Yang, Jingsong ; Cui, Guangcai ; Hu, Xuedan

Author_Institution

Coll. of Comput. Sci. & Technol., Changchun Univ. of Sci. & Technol., Changchun

fYear

2007

fDate

14-17 Sept. 2007

Firstpage

70

Lastpage

73

Abstract

An approach using the concept of self-adapted hybrid genetic algorithm (AGASA) is proposed as a powerful but simple means to optimize job scheduling in distributed manufacturing system (DMS). A directed graph model is developed to describe the characteristics of DMS. The superiority of the proposed algorithm is illustrated by computing result with pattern of GANTT graph, and the results are compared with methods of genetic algorithm and simulated annealing algorithm.

Keywords

directed graphs; genetic algorithms; manufacturing systems; scheduling; simulated annealing; directed graph model; distributed manufacturing system; job scheduling; self-adapted hybrid genetic algorithm; simulated annealing algorithm; Batch production systems; Control systems; Flexible manufacturing systems; Flow production systems; Genetic algorithms; Job production systems; Job shop scheduling; Manufacturing systems; Processor scheduling; Workstations;

fLanguage

English

Publisher

ieee

Conference_Titel

Bio-Inspired Computing: Theories and Applications, 2007. BIC-TA 2007. Second International Conference on

Conference_Location

Zhengzhou

Print_ISBN

978-1-4244-4105-1

Electronic_ISBN

978-1-4244-4106-8

Type

conf

DOI

10.1109/BICTA.2007.4806421

Filename

4806421