Minimizing completion time variance in a flowshop scheduling problem with a learning effect

dc.contributor.authorEren T.
dc.date.accessioned2020-06-25T15:17:19Z
dc.date.available2020-06-25T15:17:19Z
dc.date.issued2013
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIn this paper, flowshop scheduling problem with a learning effect is considered. The objective function of the problem is minimizing completion times variance. A non-linear programming model is developed for the problem. Also the model is tested on an example. Results of computational tests show that the proposed model is effective in solving problems with up to 30 jobs. The overall average solution error of the heuristic algorithm is 2%. Processing of the 30 jobs case requires only 0.1 s on average to obtain an ultimate or even optimal solution. To solve the large sizes problems up to 500 jobs, heuristics methods were used. The performances of heuristics about the solution error were evaluated with the non-linear programming model results for small size problems and each other for large size problems. According to results, the special heuristic for all number of jobs was the more effective than others. The heuristic scheduling algorithm is more practical to solve real world applications than the non-linear programming model.en_US
dc.identifier.endpage397en_US
dc.identifier.issn13039709
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-84884957965
dc.identifier.scopusqualityQ2
dc.identifier.startpage389en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12587/2300
dc.identifier.volume26en_US
dc.indekslendigikaynakScopus
dc.language.isoen
dc.relation.ispartofGazi University Journal of Science
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCompletion time varianceen_US
dc.subjectFlowshop schedulingen_US
dc.subjectHeuristic methodsen_US
dc.subjectLearning effecten_US
dc.subjectNon-linear programming modelen_US
dc.titleMinimizing completion time variance in a flowshop scheduling problem with a learning effecten_US
dc.typeArticle

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