An interactive method for the solution of fully Z-number linear programming models

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dc.authoridAllahviranloo, Tofigh/0000-0002-6673-3560
dc.authoridAkram, Muhammad/0000-0001-7217-7962
dc.authorwosidAllahviranloo, Tofigh/V-4843-2019
dc.authorwosidAkram, Muhammad/N-3369-2014
dc.contributor.authorAkram, Muhammad
dc.contributor.authorUllah, Inayat
dc.contributor.authorAllahviranloo, Tofigh
dc.date.accessioned2024-05-19T14:43:02Z
dc.date.available2024-05-19T14:43:02Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractLinear programming is a technique widely used in decision-making nowadays. Linear programming in a fuzzy environment makes it even more interesting due to the vagueness and uncertainty of the available resources and variables. Since the market price and profit of certain goods are not known exactly, considering fuzzy variables and parameters in the linear programming makes it more closer to the real-life situation; therefore, it becomes more attractive for the decision-makers. In a fuzzy environment, there is only one information and that is the possibility of the variable. In many real-world problems, we need the reliability of the information along with its possibility. Zadeh suggested a Z-number Z = (A; B) with two components, A carrying the information of possibility of the variable, and B carrying the information about reliability of the first component A. Linear programming with its parameters and variables carrying the information in the form of Z number is even more exciting for the decision-makers. Because every decision-maker demands information that is more reliable, linear programming in a Z-number environment with both its components taken as fuzzy numbers is a very attractive problem. In this paper, we present linear programming problems with the parameters and variables taken as Z number having triangular fuzzy numbers as possibility and reliability. We also suggest an interactive method to solve Z number linear programming problems by converting Z-numbers into conventional fuzzy numbers and then using the ranking of fuzzy numbers. We also present applications of the proposed models by solving numerical examples. We also test the authenticity of the proposed method by comparing the results with the existing techniques.en_US
dc.identifier.doi10.1007/s41066-023-00402-0
dc.identifier.endpage1227en_US
dc.identifier.issn2364-4966
dc.identifier.issn2364-4974
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85168566363en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1205en_US
dc.identifier.urihttps://doi.org10.1007/s41066-023-00402-0
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5314
dc.identifier.volume8en_US
dc.identifier.wosWOS:001063513700002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringernatureen_US
dc.relation.ispartofGranular Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectLinear Programmingen_US
dc.subjectZ-Numberen_US
dc.subjectRankingen_US
dc.subjectZ-Number Linear Programming Problemen_US
dc.titleAn interactive method for the solution of fully Z-number linear programming modelsen_US
dc.typeReview Articleen_US

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