A group decision making approach for trapezoidal fuzzy preference relations with compatibility measure

Zhou, Yuanyuan; Ling-hua, Cheng; Zhou, Ligang; Chen, Huayou; Ge, Jianqiao

doi:10.1007/s00500-015-1975-z

Cited by 21 publications

(7 citation statements)

References 57 publications

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“…In order to obtain a collective decision making matrix, the weights associated to each user need to be determined. In traditional GDM models, the weights of user are usually assumed to be known beforehand [13,16,50,54]. However, this assumption may be unrealistic or improbable in some cases.…”

Section: Consensus Model With Optimal Feedback Mechanism Under Dltfsmentioning

confidence: 99%

“…Another issue to be addressed in GDM is the heterogeneous problem between group users [4,33]. This is usually resolved by assigning importance degrees to users beforehand based on the assumption that these stem from some reliable sources of information [13,16,50,54]. However, in most cases, this assumption may be unrealistic or improbable because there might not exist historical records of interaction between the users in the group.…”

Section: Introductionmentioning

confidence: 99%

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A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust

et al. 2018

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Section: Consensus Model With Optimal Feedback Mechanism Under Dltfsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust

et al. 2018

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“…The consistency index is typically used for measuring the consistency level of an individual IPR; subsequently, an iterative algorithm is established to achieve an acceptable level of consistency, such as in an interval fuzzy preference relation (IFPR) [18,19], interval intuitionistic preference relation [20][21][22], linguistic preference relation [23,24], and hesitant fuzzy preference relation (FPR) [25,26]. The compatibility [27][28][29][30][31][32] is used similar to the consistency index. To ensure the efficiency and consensus [11,[33][34][35], GDM typically aggregates individual preference relations into a collective preference relation, which is often obtained by the weighted averaging operator [36,37], ordered weighted averaging operator [6,10,38], and weighted geometric averaging operator [39,40], followed by a consensus to measure the difference among all individuals.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of Group Consistency for Incomplete Uncertain Preference Relation

Ma¹,

Gong²,

Guo³

2020

IJCIS

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An incomplete uncertain preference relation (UPR) is typical in group decision making (GDM) for decision makers (DMs) to express preference over alternatives because of the information interaction barrier between people and decision making environment. Completing missing values can guarantee individual consistency and consensus level effectively. The operation of traditional interval preference relations (IPRs) is based only on the end point transformation, which may cause interval discretisation and information distortion easily. To overcome these limitations, pairwise comparison of alternatives in an IPR is treated as an uncertain distribution function of the subjective preference of the DM which avoids discretisation operation and handles interval numbers collectively. A belief degree is used to maintain the original information as much as possible. It guarantees the extent how people believe the estimated value is close to the incomplete original value. An uncertain chance constrained programming model is proposed herein to estimate incomplete values based on a belief degree when the preference relation obeys a linear uncertain distribution. A distance measure is defined to compute the consistency index and consensus degree. Subsequently, an iterative algorithm is presented for GDM with linear UPRs, which adjusts inconsistent preference relations and uses an operator to aggregate all individual preference relations. Furthermore, it is proven that the operation of UPRs is an extension of that of traditional IPRs under a certain belief degree.

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“…Different from the majority of researchers [27], [28], this paper defines the comparable degree between the DPMLPRs and utilizes it as the measure to judge the consistency of the DPMLPRs. The reason why we use the comparable degree is that the intrinsic quality between the comparable degree [29]- [31] and the distance measure [32], [33] is same. Moreover, because of the structure of the operator itself, the computation of the comparable degree is also separated into two angles: the membership viewpoint and the non-membership viewpoint.…”

Section: Introductionmentioning

confidence: 99%

Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and its Application on the Cloud Enterprise

2019

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Under the cloud trend of enterprises, how do traditional businesses get on the cloud becomes a worth pondering question. To help those traditional businesses that have no experience to dispel the clouds and see the sun as soon as possible, we are planning to choose one corporation with rich experience to take them into the cloud market. The quintessence of dual probabilistic linguistic term sets (DPLTSs) is that it uses the combination of several linguistic terms and their proportions to reveal decision information by opposite angles. This paper proposes the dual probabilistic multiplicative linguistic preference relations (DPMLPRs) based upon the dual probabilistic multiplicative linguistic term sets (DPMLTSs). Then, it defines the comparable degree between the DPMLPRs and studies the consensus of the group DPMLPR. Moreover, it probes the expanding grey relational analysis (EGRA) under the proposed comparable degree between the DPMLTSs. After that, one example of choosing the experienced cloud cooperative partner is simulated under the dual probabilistic linguistic circumstance. Besides, the comparative analysis is performed by considering the similarity among the EGRA, TODIM, and VIKOR. INDEX TERMS Dual probabilistic multiplicative linguistic preference relations, comparable degree, consensus, expanding grey relational analysis, multi-criteria decision making.

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A group decision making approach for trapezoidal fuzzy preference relations with compatibility measure

Cited by 21 publications

References 57 publications

A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust

A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust

Optimisation of Group Consistency for Incomplete Uncertain Preference Relation

Expanding Grey Relational Analysis With the Comparable Degree for Dual Probabilistic Multiplicative Linguistic Term Sets and its Application on the Cloud Enterprise

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