Please use this identifier to cite or link to this item:
Title: Applying Ant Colony Optimisation (ACO) algorithm to dynamic job shop scheduling problems
Authors: Zhou, R. 
Lee, H.P. 
Nee, A.Y.C. 
Keywords: ACO
Ant Colony Optimization
Dynamic job shop scheduling
Issue Date: 2008
Citation: Zhou, R.,Lee, H.P.,Nee, A.Y.C. (2008). Applying Ant Colony Optimisation (ACO) algorithm to dynamic job shop scheduling problems. International Journal of Manufacturing Research 3 (3) : 301-320. ScholarBank@NUS Repository.
Abstract: Ant Colony Optimization (ACO) is applied to two dynamic job scheduling problems, which have the same mean total workload but different dynamic levels and disturbing severity. Its performances are statistically analysed and the effects of its adaptation mechanism and parameters such as the minimal number of iterations and the size of searching ants are studied. The results show that ACO can perform effectively in both cases; the adaptation mechanism can significantly improve the performance of ACO when disturbances are not severe; increasing the size of iterations and ants per iteration does not necessarily improve the overall performance of ACO. Copyright © 2008, Inderscience Publishers.
Source Title: International Journal of Manufacturing Research
ISSN: 17500591
DOI: 10.1504/IJMR.2008.019212
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Dec 17, 2018

Page view(s)

checked on Dec 8, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.