Please use this identifier to cite or link to this item:
Title: Dynamic scheduling of manufacturing job shops using genetic algorithms
Authors: Chryssolouris, G.
Subramaniam, V. 
Keywords: Genetic algorithms
Job shop
Issue Date: Jun-2001
Citation: Chryssolouris, G., Subramaniam, V. (2001-06). Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of Intelligent Manufacturing 12 (3) : 281-293. ScholarBank@NUS Repository.
Abstract: Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date. A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.
Source Title: Journal of Intelligent Manufacturing
ISSN: 09565515
DOI: 10.1023/A:1011253011638
Appears in Collections:Staff Publications

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

Google ScholarTM



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