A Consensus Model for Group Decision-Making Problems With Interval Fuzzy Preference Relations

Tapia, José Miguel Rodríguez; Moral, M. J. del; Martínez, María Angeles Aláez; Liu, Xinyue

doi:10.1142/s0219622012500174

Cited by 42 publications

(15 citation statements)

References 45 publications

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“…It will be an interesting future research to find out possible relationships among preferences, self-confidence assessments and results. Meanwhile, the consensus problem is a hot topic in GDM [2,7,10,17,32,37], and it will be interesting to investigate the consensus reaching model in GDM based on heterogeneous preference relations with self-confidence.…”

Section: Comparative Analysismentioning

confidence: 99%

“…As a result, the decision makers use different preference relations to express their individual preference information. Three kinds of preference relations have been widely investigated: multiplicative preference relations [5,26,33,41], additive preference relations [6,19,31,[34][35][36]41] and linguistic preference relations [8,9,11,16,20,28,37]. Chiclana et al [4,5], Dong et al [12,14], Herrera et al [26], and Herrera-Viedma et al [21] initiated and developed the GDM models with heterogeneous preference relations represented by preference orderings, utility functions, additive preference relations, multiplicative preference relations.…”

Section: Introductionmentioning

confidence: 99%

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Group decision-making based on heterogeneous preference relations with self-confidence

Liu

Dong

Chiclana

et al. 2016

Fuzzy Optim Decis Making

169

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Preference relations are very useful to express decision makers' preferences over alternatives in the process of group decision-making. However, the multiple self-confidence levels are not considered in existing preference relations. In this study, we define the preference relation with self-confidence by taking multiple self-confidence levels into consideration, and we call it the preference relation with self-confidence. Furthermore, we present a two-stage linear programming model for estimating the collective preference vector for the group decision-making based on heterogeneous preference relations with self-confidence. Finally, numerical examples are used to illustrate the two-stage linear programming model, and a comparative analysis is carried out to show how self-confidence levels influence on the group decision-making results.

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Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Group decision-making based on heterogeneous preference relations with self-confidence

Liu

Dong

Chiclana

et al. 2016

Fuzzy Optim Decis Making

169

View full text Add to dashboard Cite

show abstract

“…Furthermore, on the one hand, the chances for reaching such a full agreement are rather low, and on the other hand, unanimity is not necessary or desirable in most real life situations. A second meaning of the concept of consensus refers to the judgement arrived at by 'most of' those concerned, which has led to the definition and use of a new concept of consensus degree referred to as 'soft' consensus degree [20,26,31,[34][35][36][37][38][39][40].…”

Section: Consensus Reaching Processmentioning

confidence: 99%

Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts

Mata

Pérez

Zhou

et al. 2014

Knowledge-Based Systems

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A crucial step in group decision making (GDM) processes is the aggregation of individual opinions with the aim of achieving a "fair" representation of each individual within the group. In multi-granular linguistic contexts where linguistic term sets with common domain but different granularity and/or semantic are used, the methodology widely applied until now requires, prior to the aggregation step, the application of a unification process. The reason for this unification process is the lack of appropriate aggregation operators for directly aggregating uncertain information represented by means of fuzzy sets. With the recent development of the Type-1 Ordered Weighted Averaging (T1OWA) operator, which is able to aggregate fuzzy sets, alternative approaches to multi-granular linguistic GDM problems are possible. Unlike consensus models based on unification processes, this paper presents a new T1OWA based consensus methodology that can directly manage linguistic term sets with different cardinality and/or semantic without the need to perform any transformation to unify the information. Furthermore, the linguistic information could be assumed to be balanced or unbalanced in its mathematical representation, and therefore the new T1OWA approach to consensus is more general in its application than previous consensus reaching processes with muti-granular linguistic information. To test the goodness of the new consensus reaching approach, a comparative study between the T1OWA based consensus model and the unification based consensus model is carried out using six randomly generated GDM problems with balanced multi-granular information. When distance between fuzzy sets used in the T1OWA based approach is defined as the corresponding distance between their centroids, a higher final level of consensus is achieved in four out of the six cases although no significant differences were found between both consensus approaches.

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“…The evaluation of consensus necessarily implies the computation and aggregation of the 'distance' representing disagreement between the opinions (preferences) of each pair of experts on each pair of alternatives [3], [19]. An issue here is that the convergence of the consensus process towards a solution acceptable by most of the experts could be affected by the particular distance function and the aggregation operator used to measure disagreement [1], [4], [20], [21], [24].…”

Section: Introductionmentioning

confidence: 99%

An analysis on consensus measures in group decision making

Moral

Chiclana

Tapia

et al. 2017

2017 4th International Conference on Control, Decision and Information Technologies (CoDIT)

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Abstract-In Group Decision Making (GDM) problems before to obtain a solution a high level of consensus among experts is required. Consensus measures are usually built using similarity functions measuring how close experts' opinions or preferences are. Similarity functions are defined based on the use of a metric describing the distance between experts' opinions or preferences. Different distance functions have been proposed to implement consensus measures. This paper analyzes the effect of the application of different aggregation operators combined with the use of different distance functions for measuring consensus in GDM problems. It is concluded that the application of different aggregation operators together with different distance functions has a significant effect on the speed of achieving consensus. These results are analysed and used to derive decision support rules, based on a convergent criterion, that can be used to control the convergence speed of the consensus process using the compared distance functions.

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A Consensus Model for Group Decision-Making Problems With Interval Fuzzy Preference Relations

Cited by 42 publications

References 45 publications

Group decision-making based on heterogeneous preference relations with self-confidence

Group decision-making based on heterogeneous preference relations with self-confidence

Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts

An analysis on consensus measures in group decision making

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