Volume 24, Issue 3 (11-2013)                   2013, 24(3): 327-338 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Vafaei Jahan M, NosratAbadi M. A Multi-Objective Fuzzy Job Shop Scheduling by Extremal Optimization. Journal title 2013; 24 (3) :327-338
URL: http://ijiepm.iust.ac.ir/article-1-509-en.html
, vafaeija@yahoo.com
Abstract:   (7509 Views)
Job shop scheduling considers jobs distribution between processing machines such that jobs to be done in the minimum time as possible. In this problem such factors as the activities processing time and due date for delivering jobs are defined ambiguously for analyst. In these situations, it seems that it is necessary to use fuzzy parameters and multiple goals on the basis of fuzzy knowledge, which results in fuzzy shop scheduling problem. This problem belongs to non polynomial problems, for this reason it tried to present a method based extremal optimization in which selects and modifies less valuable activities with more probability. It causes the number of less valuable activities decreases and the number of activities with the same value increases in this case any partial change in scheduling makes big changes. Therefore, it leads to escape from local optima and move toward global optima. In this problem, the concentration is on the satisfaction of scheduling. According to the results of simulation on 6×6 and 10×10 experimental data, the proposed method shows appropriate degree of satisfaction of problem's objectives with proper convergent rate in contrast of other methods. Correctness of given results has been proven by means of statistical tests.
Full-Text [PDF 1898 kb]   (4030 Downloads)    
Type of Study: Research | Subject: Optimization Techniques
Received: 2011/02/28 | Accepted: 2014/02/2 | Published: 2014/02/2

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.