A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups

Li, Cong-Cong; Herrera, Francisco

doi:10.1109/tfuzz.2018.2857720

Cited by 238 publications

(48 citation statements)

References 49 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%

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%

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

et al. 2019

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“…With the rapid developments and applications of science and technology, more and more experts are involved in GDM problems. It makes large‐scale group decision‐making (LSGDM) problems becoming a hotspot . In addition, due to time pressure, lack of knowledge, and limited experience, experts may overestimate their judgments, that is, experts may show overconfidence behaviors in decision‐making processes .…”

Section: Discussionmentioning

confidence: 99%

“…It makes large-scale group decision-making (LSGDM) problems becoming a hotspot. [45][46][47][48][49][50][51][52] In addition, due to time pressure, lack of knowledge, and limited experience, experts may overestimate their judgments, that is, experts may show overconfidence behaviors in decision-making processes. 53 Thus, in future work, we will address to extend FPRs-SC to LSGDM problems and discuss the influence of overconfidence behaviors on decision making.…”

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confidence: 99%

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

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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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“…As a part of the future scope, we plan the following research directions: (i) to present new methods for ranking under pair-wise comparison ideas; (ii) to enhance the consistency of the PLPRs under both additive and multiplicative context; (iii) to develop methods for consensus reaching by gaining motivation from [38,39] and strategic weight calculation inspired by [40,41].…”

Section: Discussionmentioning

confidence: 99%

Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making

et al. 2018

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With trending competition in decision-making process, linguistic decision-making is gaining attractive attention. Previous studies on linguistic decision-making have neglected the occurring probability (relative importance) of each linguistic term which causes unreasonable ranking of objects. Further, decision-makers' (DMs) often face difficulties in providing apt preference information for evaluation. Motivated by these challenges, in this paper, we set our proposal on probabilistic linguistic preference relation (PLPR)-based decision framework. The framework consists of two phases viz., (a) missing value entry phase and (b) ranking phase. In phase (a), the missing values of PLPR are filled using a newly proposed automatic procedure and consistency of PLPR is ensured using a consistency check and repair mechanism. Following this, in phase (b), objects are ranked using newly proposed analytic hierarchy process (AHP) method under PLPR context. The practicality of the proposal is validated by using two numerical examples viz., green supplier selection problem for healthcare and the automobile industry. Finally, the strength and weakness of the proposal are discussed by comparing with similar methods. Symmetry 2019, 11, 2 2 of 18 weighted geometric aggregation operator for a linguistic domain. Further, He et al. [8] put forward a new entropy measure for linguistic-based group decision-making.Though linguistic decision-making is an attractive concept, DMs still face difficulties in rationally rating the objects. The main reason for this difficulty is the cognitive behavior of the human mind, which encourages pair-wise comparative analysis rather than a standalone rating [9]. Motivated by the power of pair-wise comparison and linguistic term set (LTS), Herrera et al. [10,11] proposed the linguistic preference relation (LPR) concept for group decision-making and investigated some choice functions for the same. Following this, Xu [12] put forward some deviation measures for decision-making process under LPR context. Recently, Molinera et al. [13] developed new fuzzy ontologies for linguistic preference information and applied the same for decision-making. Wang and Xu [14] put forward an interactive algorithm for filling the missing LPR values using consistency measures and repaired the consistency of the same using the repairing mechanism.Inspired by the power of linguistic information and its substantial use in decision-making, Rodriguez [15] proposed the hesitant fuzzy linguistic term set (HFLTS) which is an extension to LTS under hesitant fuzzy environment [16]. The HFLTS allowed DMs to give different choices of preference for the same instance, which managed uncertainty to some extent. Motivated by the power of HFLTS, Zhu and Xu [17] put forward the hesitant fuzzy linguistic preference relation (HFLPR), which is an extension to preference relation under HFLTS context. They also investigated some consistency measures for the same. Following this, Wang and Xu [18] presented the concept of extended hesitant fuzzy prefe...

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A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups

Cited by 238 publications

References 49 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

Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making

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