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dc.contributor.authorAmiri, Maghsoud
dc.contributor.authorHashemi-Tabatabaei, Mohammad
dc.contributor.authorGhahremanloo, Mohammad
dc.contributor.authorKeshavarz-Ghorabaee, Mehdi
dc.contributor.authorZavadskas, Edmundas Kazimieras
dc.contributor.authorAntuchevičienė, Jurgita
dc.date.accessioned2023-09-18T20:43:47Z
dc.date.available2023-09-18T20:43:47Z
dc.date.issued2021
dc.identifier.issn0360-8352
dc.identifier.other(SCIDIR_EID)1-s2.0-S0360835221001911
dc.identifier.urihttps://etalpykla.vilniustech.lt/handle/123456789/152119
dc.description.abstractThe best-worst method (BWM) is one of the most important methods for determining the weights of criteria or options in multi-criteria decision-making (MCDM) and has attracted the attention of many researchers due to its advantages such as fewer numbers of comparisons and higher consistency rate. Given that real-world decision making is often associated with uncertainty, in this study several fuzzy linear programming models have been developed using trapezoidal fuzzy numbers for BWM that can calculate the optimal weights of criteria. The proposed models are based on three measures of possibility, necessity, and credibility, which are parts of possibilistic chance-constrained programming (PCCP). Development of BWM based on possibilistic distribution allows the decision-maker (DM) to take into account uncertainties in the calculation of weights as well as include his optimistic, pessimistic, and intermediate attitudes in determining the weight of decision criteria. The possibility approach reflects the DM's optimistic view of the issue. The necessity approach is used in situations where the DM prefers a pessimistic view, and the credibility approach indicates that DM has an intermediate view between optimistic and pessimistic views, or in other words, considers an intermediate approach between possibility and necessity approaches. Finally, the feasibility and effectiveness of the proposed approaches were tested using two numerical examples and the sensitivity of the results was analyzed for different values of uncertainty (alpha parameter). Also, by analyzing the coefficient of variation, it was found that the results of the proposed models had very little dispersion for different values of uncertainty, and this confirmed the validity of the results. Examining the results of the proposed models revealed that the possibility approach provides more robust results than other approaches when the levels of confidence of the decision-makers are changed.eng
dc.formatPDF
dc.format.extentp. 1-16
dc.format.mediumtekstas / txt
dc.language.isoeng
dc.relation.isreferencedbyScopus
dc.relation.isreferencedbyScienceDirect
dc.relation.isreferencedbyScience Citation Index Expanded (Web of Science)
dc.source.urihttps://doi.org/10.1016/j.cie.2021.107287
dc.titleA novel model for multi-criteria assessment based on BWM and possibilistic chance-constrained programming
dc.typeStraipsnis Web of Science DB / Article in Web of Science DB
dcterms.references86
dc.type.pubtypeS1 - Straipsnis Web of Science DB / Web of Science DB article
dc.contributor.institutionAllameh Tabataba’i University, Tehran
dc.contributor.institutionShahrood University of Technology
dc.contributor.institutionGonbad Kavous University
dc.contributor.institutionVilniaus Gedimino technikos universitetas
dc.contributor.facultyStatybos fakultetas / Faculty of Civil Engineering
dc.subject.researchfieldT 002 - Statybos inžinerija / Construction and engineering
dc.subject.researchfieldT 007 - Informatikos inžinerija / Informatics engineering
dc.subject.vgtuprioritizedfieldsFM0101 - Fizinių, technologinių ir ekonominių procesų matematiniai modeliai / Mathematical models of physical, technological and economic processes
dc.subject.ltspecializationsL104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies
dc.subject.enmulti-criteria decision-making
dc.subject.enpossibilistic programming
dc.subject.enbest-worst method
dc.subject.enpairwise comparison
dc.subject.enfuzzy linear programming
dcterms.sourcetitleComputers & industrial engineering
dc.description.volumevol. 156
dc.publisher.nameElsevier
dc.publisher.cityOxford
dc.identifier.doi1-s2.0-S0360835221001911
dc.identifier.doiS0360-8352(21)00191-1
dc.identifier.doi85104805758
dc.identifier.doi2-s2.0-85104805758
dc.identifier.doi0
dc.identifier.doi10.1016/j.cie.2021.107287
dc.identifier.elaba92936466


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