نمایش مختصر رکورد

dc.contributor.authorFaezy Razi, Farshaden_US
dc.date.accessioned1399-07-22T18:17:47Zfa_IR
dc.date.accessioned2020-10-13T18:17:48Z
dc.date.available1399-07-22T18:17:47Zfa_IR
dc.date.available2020-10-13T18:17:48Z
dc.date.issued2019-09-01en_US
dc.date.issued1398-06-10fa_IR
dc.date.submitted2020-10-08en_US
dc.date.submitted1399-07-17fa_IR
dc.identifier.citationFaezy Razi, Farshad. (2019). A hybrid DEA-based K-means and invasive weed optimization for facility location problem. Journal of Industrial Engineering, International, 15(3)en_US
dc.identifier.issn1735-5702
dc.identifier.issn2251-712X
dc.identifier.urihttp://jiei.azad.ac.ir/article_676854.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/434637
dc.description.abstractIn this paper, instead of the classical approach to the multi-criteria location selection problem, a new approach was presented based on selecting a portfolio of locations. First, the indices affecting the selection of maintenance stations were collected. The <em>K</em>-means model was used for clustering the maintenance stations. The optimal number of clusters was calculated through the Silhouette index. The efficiency of each cluster of stations was determined using the Charnes, Cooper and Rhodes input-oriented data envelopment analysis model. A bi-objective zero one programming model was used to select a Pareto optimal combination of rank and distance of stations. The Pareto solutions for the presented bi-objective model were determined using the invasive weed optimization method. Although the proposed methodology is meant for the selection of repair and maintenance stations in an oil refinery Company, it can be used in multi-criteria decision-making problems.en_US
dc.format.extent807
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.publisherIslamic Azad University, South Tehran Branchen_US
dc.relation.ispartofJournal of Industrial Engineering, Internationalen_US
dc.subjectFacility location problem . DEAen_US
dc.subjectCCR . Ken_US
dc.subjectmeans algorithm . Invasive weedoptimization . Multipleen_US
dc.subjectcriteria decision analysisen_US
dc.titleA hybrid DEA-based K-means and invasive weed optimization for facility location problemen_US
dc.typeTexten_US
dc.contributor.departmentDepartment of Industrial Management, Semnan Branch, Islamic Azad University, Semnan, Iranen_US
dc.citation.volume15
dc.citation.issue3


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