DC FieldValueLanguage
dc.contributor.authorStanojević, Bogdanaen_US
dc.contributor.authorPantelić, O.en_US
dc.contributor.authorDzitac, S.en_US
dc.date.accessioned2024-05-07T09:13:25Z-
dc.date.available2024-05-07T09:13:25Z-
dc.date.issued2024-01-01-
dc.identifier.issn18419836-
dc.identifier.issn1841-9836-
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/5289-
dc.description.abstractOptimization involving fuzzy numbers generally, and intuitionistic fuzzy numbers particularly is more and more an essential part of any computational intelligence method; and might be of interest in modeling fuzzy control systems, or carrying out a fuzzy sensitivity analysis. The recent literature includes many research papers related to both theoretical modelling, and practical implementation of fuzzy decision support systems. Fuzzy optimization is one of the main part in such decisionmaking tools. A realistic solution to a fuzzy optimization problem is always desired, and similarly, a Pareto optimal solution to a multiple objective optimization problem is always a need. In this paper we analyze the shortcomings; and eliminate the weaknesses of a solution approach from the literature proposed by Moges et al. in 2023. Firstly, Moges et al. used an accuracy function to de-fuzzify the original intuitionistic fuzzy optimization problem; then linearized the obtained crisp problem; and finally involved fuzzy goals to solve the derived multiple objective linear problem. We present two faulty key points of their approach, namely the de-fuzzification and linearization steps; prove the inappropriateness of their results; and propose some improvements. The final aim in addressing the first key point is to clarify the border between defuzzifications made in accordance to true theoretical statements, and those made for modeling reasons. The methodological improvement we propose is related to the second key point and it assures that Pareto optimal solutions - highly required in multiple objective optimization - are obtained. To illustrate our point of view, we use a numerical example from the literature, and report better numerical results derived by the improved methodology.en_US
dc.publisherUniversity of Oradea & Research & Development Center of Agora University, Romaniaen_US
dc.relation.ispartofInternational Journal of Computers, Communications and Controlen_US
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectfuzzy mathematical programming | intuitionistic fuzzy numbers | linear fractional programming | multi-objective optimizationen_US
dc.titleEnhanced Solutions to Intuitionistic Fuzzy Multiobjective Linear Fractional Optimization Problems via Lexicographic Methoden_US
dc.typeArticleen_US
dc.identifier.doi10.15837/ijccc.2024.1.6333-
dc.identifier.scopus2-s2.0-85187282458-
dc.contributor.affiliationComputer Scienceen_US
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Artsen_US
dc.relation.firstpage6333-
dc.relation.issue1-
dc.relation.volume19-
dc.description.rank~M22-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.orcid0000-0003-4524-5354-
Files in This Item:
File Description SizeFormat
StanojevicB.pdf966.8 kBAdobe PDFView/Open
Show simple item record

Page view(s)

3
checked on May 9, 2024

Download(s)

1
checked on May 9, 2024

Google ScholarTM

Check

Altmetric

Altmetric


This item is licensed under a Creative Commons License Creative Commons