Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions

Li, Cong-Cong; Rodríguez, Rosa M.; Martínez, Luis; Herrera, Francisco

doi:10.1016/j.knosys.2018.01.011

Cited by 157 publications

(46 citation statements)

References 27 publications

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“…It is valuable to develop effective semantic assignment methods as the future research according to practical situations. Some techniques such as granular computing and particle swarm optimization (Cabrerizo, Herrera‐Viedma, & Pedrycz, ) and consistency‐based personalized individual semantics (Li, Dong, & Herrera, ; Li, Rodríguez, et al, ) can be used in the future investigations.…”

Section: Discussionmentioning

confidence: 99%

“…The group consensus is one of the most important issues in GDM, which indicates the agreement among assessments provided by all individual DMs (Li, Rodríguez, et al, ; Li, Wang, & Hu, ; Palomares et al, ; Wu et al, ; Zhang, Dong, Chiclana, & Yu, ). The low group consensus level would result in unreasonable solution (Wan, Wang, & Dong, ).…”

Section: The Proposed Framework For Qfdmentioning

confidence: 99%

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Group multigranular linguistic QFD for prioritizing service designs with combined weighting method

et al. 2019

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Qualification for the importance degree of service designs (SDs) is an essential stage in promoting accommodation performances to satisfy customers and gain market shares. Although various methods have been applied to derive the priorities of SDs, they do not effectively deal with the vagueness of information and the heterogeneity of decision makers (DMs). This study presents an improved quality function deployment (QFD) for prioritizing SDs. Multigranular unbalanced linguistic term sets are used to capture evaluators' ratings to cope with vague information. The multigranular linguistic information is unified by using a novel uniform method. This kind of information then is represented as linguistic distribution assessments using the concept of random preferences based on possibility interpretations of weight information. A consensus‐based mathematical programming model is established to determine the weights of DMs. Moreover, a combined structure that combines maximizing deviation and best worst methods is used to derive basic weights of SDs. Finally, an illustrative example of island accommodation management in Weizhou Island is provided to demonstrate the applicability and advantages of the proposed QFD framework.

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Section: Discussionmentioning

confidence: 99%

Section: The Proposed Framework For Qfdmentioning

confidence: 99%

Group multigranular linguistic QFD for prioritizing service designs with combined weighting method

et al. 2019

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“…Up to now, many approaches have been proposed to discuss the consistency of different kinds of preference relations . For instance, Dong et al proposed a consistency for linguistic preference relations.…”

Section: Introductionmentioning

confidence: 99%

“…Up to now, many approaches have been proposed to discuss the consistency of different kinds of preference relations. 2,[21][22][23][24][25][26][27][28][29][30][31][32] For instance, Dong et al 21 proposed a consistency for linguistic preference relations. Herrera-Viedma et al 19 presented a study of the consistency for fuzzy preference relations (FPRs).…”

Section: Introductionmentioning

confidence: 99%

Analysis of self-confidence indices-based additive consistency for fuzzy preference relations with self-confidence and its application in group decision making

Liu

Montes

et al. 2018

Int J Intell Syst

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Preference relations have been widely used in group decision‐making (GDM) problems. Recently, a new kind of preference relations called fuzzy preference relations with self‐confidence (FPRs‐SC) has been introduced, which allow experts to express multiple self‐confidence levels when providing their preferences. This paper focuses on the analysis of additive consistency for FPRs‐SC and its application in GDM problems. To do that, some operational laws for FPRs‐SC are proposed. Subsequently, an additive consistency index that considers both the fuzzy preference values and self‐confidence is presented to measure the consistency level of an FPR‐SC. Moreover, an iterative algorithm that adjusts both the fuzzy preference values and self‐confidence levels is proposed to repair the inconsistency of FPRs‐SC. When an acceptable additive consistency level for FPRs‐SC is achieved, the collective FPR‐SC can be computed. We aggregate the individual FPRs‐SC using a self‐confidence indices‐based induced ordered weighted averaging operator. The inherent rule for aggregation is to give more importance to the most self‐confident experts. In addition, a self‐confidence score function for FPRs‐SC is designed to obtain the best alternative in GDM with FPRs‐SC. Finally, the feasibility and validity of the research are demonstrated with an illustrative example and some comparative analyses.

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“…Wu and Xu [39] proposed a large-scale group decision-making consensus model with possibility distribution based hesitant fuzzy preference. Li et al [40] personalized individual semantics in group decision-making with hesitant fuzzy linguistic terms sets by consensus model. Zhang et al [41] defined three kinds of additive consistency indices to measure the consistency level of hesitant fuzzy preference relation.…”

Section: Introductionmentioning

confidence: 99%

Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

Guan¹,

Sun²,

Xiao³

et al. 2018

Mathematical Problems in Engineering

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Due to the superiority in expressing the uncertain and vague information, the hesitant fuzzy set (HFS) is regarded as an important tool to deal with multiattribute decision-making (MADM) problems. Quantitative and qualitative fuzzy measures have been proposed to solve such problems from different points. However, most of the existing information measures for HFSs are related to such fuzzy measures as distance, similarity, entropy, and correlation coefficients. The grey relational analysis is omitted. Besides, the existing grey relational analysis for HFSs only considers the range or distance between HFSs data which is only a partial measure of the HFSs. Therefore, in this paper, we improve the grey relational analysis for HFSs and explore a novel slope grey relational degree by considering another factor of HFSs data: the slope. Further, we combine both the distance and slope factors of HFSs data to construct a synthetic grey relational degree that describes the closeness and variation tendency of HFSs simultaneously, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs MADM problems. Finally, combining with two practical MADM examples about energy policy selection and multisensor target recognition, we obtain the most desirable decision results. Compared with the previous methods, the validity, comprehensiveness, and discrimination of the proposed synthetic grey relational degree for HFSs are demonstrated in detail.

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Personalized individual semantics based on consistency in hesitant linguistic group decision making with comparative linguistic expressions

Cited by 157 publications

References 27 publications

Group multigranular linguistic QFD for prioritizing service designs with combined weighting method

Group multigranular linguistic QFD for prioritizing service designs with combined weighting method

Analysis of self-confidence indices-based additive consistency for fuzzy preference relations with self-confidence and its application in group decision making

Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

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