New hybrid EC-PROMETHEE method with multiple iterations of random weight ranges: step-by-step application in Python

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dc.contributor.authorBasilio, Marcio Pereira
dc.contributor.authorPereira, Valdecy
dc.contributor.authorYiğit, Fatih
dc.date.accessioned2024-08-21T13:17:36Z
dc.date.available2024-08-21T13:17:36Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractThe decision-making process consists of finding the best solution to an analyzed problem. This search is carried out in the face of countless interactions when analyzing an alternative criterion by criterion, under which weights are assigned that distinguish the degree of importance they have for the decision-makers. The definition of weight for each criterion gives rise to three lines of thought on the subject. There are objective, subjective, and hybrid methods. This discussion concerns the degree to which experts define the criteria weights. Based on this discussion, we developed a hybrid method to integrate the Entropy and CRITIC methods with the PROMETHEE method, called EC-PROMETHEE. The innovation of this method is that the combination of the Entropy and CRITIC methods does not result in a single set of weights. In reality, the weights generated by each method are used to define each criterion's upper and lower limits. The range of weights generated for each criterion is emulated "n" times and builds a set of weights that are applied to the ranking definition process. The model generates "n" rankings, defining a single ranking. In this article, we demonstrate a step-by-step application of a tool developed in Python called EC-PROMETHEE and use it as an example of the problem of choosing rotary-wing airplanes for application in the military police service. ➢ The method reduces discretion in determining the weights of the criteria; ➢ The innovation lies in the use of a range of weights for criteria; ➢ Consistency in defining the final ranking.en_US
dc.identifier.citationBasilio, M. P., Pereira, V., Yiğit, F. (2024). New hybrid EC-PROMETHEE method with multiple iterations of random weight ranges: step-by-step application in Python. MethodsX, 13. 10.1016/j.mex.2024.102890en_US
dc.identifier.issn2215-0161
dc.identifier.scopus2-s2.0-85201102083
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4809
dc.identifier.volume13en_US
dc.identifier.wosWOS:001296805700001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorYiğit, Fatih
dc.language.isoen
dc.publisherElsevier B.V.en_US
dc.relation.ispartofMethodsX
dc.relation.isversionof10.1016/j.mex.2024.102890en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCriticen_US
dc.subjectDecision makeren_US
dc.subjectEntropyen_US
dc.subjectMcdaen_US
dc.subjectOperations researchen_US
dc.subjectPrometheeen_US
dc.titleNew hybrid EC-PROMETHEE method with multiple iterations of random weight ranges: step-by-step application in Python
dc.typeArticle

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