Minimizing Completion Time Variance in a Flowshop Scheduling Problem with a Learning Effect

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dc.contributor.authorEren, Tamer
dc.date.accessioned2025-01-21T16:42:44Z
dc.date.available2025-01-21T16:42:44Z
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.
dc.identifier.endpage397
dc.identifier.issn2147-1762
dc.identifier.issue3
dc.identifier.scopusqualityQ2
dc.identifier.startpage389
dc.identifier.urihttps://hdl.handle.net/20.500.12587/25114
dc.identifier.volume26
dc.identifier.wosWOS:000421148900004
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherGazi Univ
dc.relation.ispartofGazi University Journal of Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectflowshop scheduling; learning effect; completion time variance; non-linear programming model; heuristic methods
dc.titleMinimizing Completion Time Variance in a Flowshop Scheduling Problem with a Learning Effect
dc.typeArticle

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