DocumentCode
2120835
Title
Production logistics scheduling optimization based on Improved Multi-Colony
Author
Liu Beilin ; Ma Li
Author_Institution
Sch. of Manage., Harbin Univ. of Commerce, Harbin, China
fYear
2010
fDate
29-31 July 2010
Firstpage
1753
Lastpage
1756
Abstract
Improved Multi-Colony Diploid Genetic Algorithm (IMCDGA) is studied in order to apply the scheduling theory to the production practice. Aimed at production logistics scheduling optimization for agile manufacturing, a job-shop dynamic scheduling model based on IMCDGA is proposed. Finally, the method mentioned above is applied to solve the scheduling optimization of Shanghai Volksvagen, Automobile Co. Ltd., and the most optimal scheduling can be attained. The simulation results demonstrate that the Improved Multi-Colony Diploid Genetic Algorithm is feasible and effective for production logistics scheduling optimization.
Keywords
genetic algorithms; job shop scheduling; logistics; IMCDGA; improved multicolony diploid genetic algorithm; jobshop dynamic scheduling; production logistics scheduling optimization; Biological cells; Job shop scheduling; Logistics; Optimal scheduling; Genetic Algorithm; Multi-Colony Diploid Genetic Algorithm; Production Logistics Scheduling Optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (CCC), 2010 29th Chinese
Conference_Location
Beijing
Print_ISBN
978-1-4244-6263-6
Type
conf
Filename
5573953
Link To Document