Biobjective Optimization Method for Large-Scale Group Decision Making Based on Hesitant Fuzzy Linguistic Preference Relations With Granularity Levels

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorZheng, Yuanhang
dc.contributor.authorXu, Zeshui
dc.contributor.authorLi, Yufei
dc.contributor.authorPedrycz, Witold
dc.contributor.authorYi, Zhang
dc.date.accessioned2025-04-18T08:13:17Z
dc.date.available2025-04-18T08:13:17Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractLarge-scale group decision making becomes increasingly common with the rapid development of society and the increasing complexity of practical problems. However, it is difficult to distinguish the semantic differences between the same linguistic term, and original linguistic term may not express flexible semantics, so that this will affect the precise of decision-making results. With the help of granular computing, this article adopts a new format of linguistic term, named as hesitant fuzzy linguistic term set with granularity level, to endow preference information with flexibility and at a specific granularity. Then, in this study, we propose a novel intelligent biobjective optimization method for large-scale group decision making, considering group consensus degree and group risk degree in the decision-making process, where group risk degree is measured from the motivation of portfolio risk. Differential evolution is used to handle biobjective optimization method to determine the optimal results. We also introduce an additive consistency measure and develop a method to derive the corresponding threshold values through Monte Carlo simulation. Finally, the case study and comparison results are covered to demonstrate the practicality and superiority of the proposed method. This work has some original points: 1) Hesitant fuzzy linguistic term set with granularity level brings flexibility to the decision-making process. 2) Group consensus degree and group risk degree are involved in biobjective optimization method, where the group risk degree is measured from the motivation of portfolio risk. 3) A novel additive consistency measure is proposed and different threshold values of preference relations in different dimensions are derived.
dc.identifier.citationZheng, Y., Xu, Z., Li, Y., Pedrycz, W., & Yi, Z. (2024). Bi-objective Optimization Method for Large-Scale Group Decision Making based on Hesitant Fuzzy Linguistic Preference Relations with Granularity Levels. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3409720
dc.identifier.endpage4771
dc.identifier.issn10636706
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85196101375
dc.identifier.scopusqualityQ1
dc.identifier.startpage4759
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3409720
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6530
dc.identifier.volume32
dc.identifier.wosWOS:001291157800047
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectLinguistics
dc.subjectDecision Making
dc.subjectAdditives
dc.subjectIndexes
dc.subjectPortfolios
dc.subjectOptimization Methods
dc.subjectSemantics
dc.titleBiobjective Optimization Method for Large-Scale Group Decision Making Based on Hesitant Fuzzy Linguistic Preference Relations With Granularity Levels
dc.typeArticle

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