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
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;
Conference_Titel :
Control Conference (CCC), 2010 29th Chinese
Conference_Location :
Beijing
Print_ISBN :
978-1-4244-6263-6
