Group decision making with heterogeneous intuitionistic fuzzy preference relations

Meng, Fanyong; Chen, Shyi‐Ming; Yuan, Ruiping

doi:10.1016/j.ins.2020.03.010

Cited by 61 publications

(12 citation statements)

References 49 publications

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“…More recently, Meng et al [34] provided an innovative multiplicative consistency definition of IFPRs based on the quasi-interval-based transitivity equation of IVFPRs and constructed a consensus model to enhance consensus among experts. Based on Krejčí [35] multiplicative consistency definition of IVFPRs, Meng et al [36] redefined the multiplicative consistency of IFPRs, and Wang [37] presented a novel multiplicative consistency definition of IFPRs and developed a representable uninorm based IFAHP.…”

Section: Literature Reviewmentioning

confidence: 99%

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Preference Relations Based on a Generalized Multiplicative Consistent Concept

Lu¹,

Dong²,

Li³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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Based on the analyses of existing preference group decision-making (PGDM) methods with intuitionistic fuzzy preference relations (IFPRs), we present a new PGDM framework with incomplete IFPRs. A generalized multiplicative consistent for IFPRs is defined, and a mathematical programming model is constructed to supplement the missing values in incomplete IFPRs. Moreover, in this study, another mathematical programming model is constructed to improve the consistency level of unacceptably multiplicative consistent IFPRs. For group decisionmaking (GDM) with incomplete IFPRs, three reliable sources influencing the weights of experts are identified. Subsequently, a method for determining the weights of experts is developed by simultaneously considering three reliable sources. Furthermore, a targeted consensus process (CPR) is developed in this study with reference to the actual situation of the consensus level of each IFPR. Meanwhile, in response to the proposed multiplicative consistency definition, a novel method for determining the optimal priority weights of alternatives is redefined. Lastly, based on the above theory, a novel GDM method with incomplete IFPRs is developed, and the comparative and sensitivity analysis results demonstrate the utility and superiority of this work.

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Section: Literature Reviewmentioning

confidence: 99%

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Preference Relations Based on a Generalized Multiplicative Consistent Concept

Lu¹,

Dong²,

Li³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…(Total/Weak/Partial/Quasi (pre)order) R is a total order iif. R ∈ { , ≺}; R is a weak order 3 iif. R ∈ { , ≺, ≈}; R is a partial order iif.…”

Section: Preference Modelling: Backgroundmentioning

confidence: 99%

“…Preference is a traditional topic in human history and its corresponding study is of great interest in various domains, such as sociology [1], economy [2], and more specifically group decision making [3]. As new applications emerge, preference modelling continues to attract the attention of recent research communities in computer science, such as social networks [4], and deep learning communities [5], etc.…”

Section: Introductionmentioning

confidence: 99%

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A distance for evidential preferences with application to group decision making

Zhang

Bouadi

Wang

et al. 2021

Information Sciences

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In this paper, we focus on measuring the dissimilarity between preferences with uncertainty and imprecision, modelled by evidential preferences based on the theory of belief functions. Two issues are targeted: The first concerns the conflicting interpretations of incomparability, leading to a lack of consensus within the preference modelling community. This discord affects the value settings of dissimilarity measures between preference relations. After reviewing the state of the art, we propose to distinguish between two cases: indecisive and undecided, respectively modelled by a binary relation and union of all relations. The second concerns a flaw that becomes apparent when measuring the dissimilarity in the theory of belief functions. Existing dissimilarity functions in the theory of belief functions are not suitable for evidential preferences, because they measure the dissimilarity between preference relations as being identical. This is counter-intuitive and conflicting with almost all the related works. We propose a novel distance named Unequal Singleton Pair (USP) distance, able to discriminate specific singletons from others when measuring the dissimilarity. The advantages of USP distances are illustrated by the evidential preference aggregation and group decision-making applications. The experiments show that USP distance effectively improves the quality of decision results.

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“…It is an extension of the crisp set, where elements have a membership value in the interval [0,1]. Fuzzy sets and fuzzy logic have potential applications in wide-ranging fields, including mathematics, computer science, engineering, statistics, artificial intelligence, decision-making, image analysis, and pattern recognition (Liu et al 2020;Zeng et al 2019;Zhang et al 2020;Zou et al 2020;Meng et al 2020). Atanassov (1983) extended the fuzzy set to a set that gives membership and non-membership grades for each element.…”

Section: Introductionmentioning

confidence: 99%

An approach to decision-making via picture fuzzy soft graphs

2021

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Fuzzy soft graphs are effective mathematical tools that are used to model the vagueness of the real world. A fuzzy soft graph is a fusion of the fuzzy soft set and the graph model and is widely used across different fields. In this current research, the concept of picture fuzzy soft graphs is presented by combining the theory of picture soft sets with graphs. The introduction of this new picture fuzzy soft graphs is an emerging concept that can be rather developed into various graph theoretical concepts. Since soft sets are most usable in real-life applications, the newly combined concepts of the picture and fuzzy soft sets will lead to many possible applications in the fuzzy set theoretical area by adding extra fuzziness in analyzing. The notions of picture soft graphs, strong and complete picture soft fuzzy graphs, a few types of product picture fuzzy soft graphs, and regular, totally regular picture fuzzy soft graphs are discussed and validated using real-world scenarios. In addition, an application of decision-making for medical diagnosis in the current COVID scenario using the picture fuzzy soft graph has been illustrated.

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Group decision making with heterogeneous intuitionistic fuzzy preference relations

Cited by 61 publications

References 49 publications

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Preference Relations Based on a Generalized Multiplicative Consistent Concept

Group Decision-Making Method with Incomplete Intuitionistic Fuzzy Preference Relations Based on a Generalized Multiplicative Consistent Concept

A distance for evidential preferences with application to group decision making

An approach to decision-making via picture fuzzy soft graphs

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