Main Article Content
This research is to develop an approximate solution for a flow shop scheduling problem under the effects of fuzzy learning and deterioration with a common fuzzy due date by applying genetic algorithm technique. Real life is complex and filled with ambiguity and uncertainty. Due dates may not be always determined by a decision maker because of their biased approach and past experiences. Therefore, due dates may be defined in forms of any fuzzy set to encode decision maker’s biased approaches and satisfaction levels for completion times of jobs. The objective function of the problem in this research is to maximise decision maker’s sum of satisfaction levels with respect to completion times of jobs on a flow shop scheduling environment by applying genetic algorithm technique. Keywords: Flow shop, fuzzy due date, genetic algorithm, learning effect, deterioration effect.
Download data is not yet available.
How to Cite
TOKSARI, M. Duran. Genetic algorithm applied to the flow shop scheduling problem under effects of fuzzy learning and deterioration with a common fuzzy due date. New Trends and Issues Proceedings on Humanities and Social Sciences, [S.l.], v. 4, n. 10, p. 306-316, jan. 2018. ISSN 2547-8818. Available at: <https://sproc.org/ojs/index.php/pntsbs/article/view/3105>. Date accessed: 22 jan. 2018. doi: https://doi.org/10.18844/prosoc.v4i10.3105.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).