A decision support model for group decision making with hesitant fuzzy preference relations

Zhang, Zhiming; Wang, Chao; Tian, Xuedong

doi:10.1016/j.knosys.2015.05.023

Cited by 85 publications

(46 citation statements)

References 37 publications

(29 reference statements)

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“…In general, the methods of MCDM present a drawback with regard to how to operate nonfuzzy and fuzzy data simultaneously. Similarly, there is weakness in the manipulation of the imprecision and uncertainty involved in measuring the preferences of the decision makers when evaluating criteria and alternatives [35][36][37]. In this sense, the PF-MOORA method introduces two algorithms in order to manipulate fuzzy and nonfuzzy data.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making

et al. 2018

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The multiobjective optimization on the basis of ratio analysis (MOORA) method captures diverse features such as the criteria and alternatives of appraising a multiple criteria decision-making (MCDM) problem. At the same time, the multiple criteria problem includes a set of decision makers with diverse expertise and preferences. In fact, the literature lists numerous approaches to aid in this problematic task of choosing the best alternative. Nevertheless, in the MCDM field, there is a challenge regarding intangible information which is commonly involved in multiple criteria decision-making problem; hence, it is substantial in order to advance beyond the research related to this field. Thus, the objective of this paper is to present a fused method between multiobjective optimization on the basis of ratio analysis and Pythagorean fuzzy sets for the choice of an alternative. Besides, multiobjective optimization on the basis of ratio analysis is utilized to choose the best alternatives. Finally, two decision-making problems are applied to illustrate the feasibility and practicality of the proposed method.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making

et al. 2018

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“…In order to obtain valuable decision from preference relations they should be consistent in a sense that, let us say, 1 is preferable to 2 and 2 is preferable to 3 then 1 must be preferable to 3 . Several authors have pursued consistency issues for preference relations [1,21,32,33,43].…”

Section: (14)mentioning

confidence: 99%

“…A wide number of researchers developed different kinds of preference relations models as fuzzy [10,11,32,40,43], intuitionistic fuzzy [23,26,27], and hesitant fuzzy [6,24,28,33,38]. Of course fuzzy preference relations are subcase of hesitant fuzzy preference relations, as we have discussed the occurrence probability is considered to be the same which is impractical as Table 4 reflects the impact of probabilities on decision making.…”

Section: Comparison With Existing Modelsmentioning

confidence: 99%

“…The HFPR is a special case of HPFPR to provide experimental comparison with existing models of group decision making models with HFPRs [33,38]; we solved their case studies by our proposed model, in a way that preference degrees are the same and their occurrence probabilities are considered to be equal. The comparison is provided in Table 5; clearly our proposed model is quite efficient as it provides the correct results.…”

Section: Comparison With Existing Modelsmentioning

confidence: 99%

“…Consensus measure is used for the mutual understanding of DMs on the finally obtained alternatives [29]. Until now, several researchers of this area successfully made some progress to convert the preference relations as consistent and generate a certain level of consensus in decision making problems [30][31][32][33]. Nowadays, hesitant probabilistic fuzzy sets received good attention in multicriteria decision making.…”

Section: Introductionmentioning

confidence: 99%

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Hesitant Probabilistic Fuzzy Preference Relations in Decision Making

Bashir

Rashid

Iqbal

2018

Mathematical Problems in Engineering

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Preference of an alternative over another alternative is a useful way to express the opinion of decision maker. In the process of group decision making, preference relations are used in preference modelling of the alternatives under given criteria. The probability is an important tool to deal with uncertainty; in many scenarios of decision making probabilities of different events affect the decision making process directly. In order to deal with this issue, in this paper, hesitant probabilistic fuzzy preference relation (HPFPR) is defined. Furthermore, consistency of HPFPR and consensus among decision makers are studied in the hesitant probabilistic fuzzy environment. In this respect, many novel algorithms are developed to achieve consistency of HPFPRs and reasonable consensus between decision makers and a final algorithm is proposed comprehending all other algorithms, presenting a complete decision support model for group decision making. Lastly, we present a case study with complete illustration of the proposed model and discussed the effects of probabilities on decision making validating the importance of the introduction of probability in hesitant fuzzy preference relation.

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A new procedure for hesitant multiplicative preference relations

Meng

Tang

et al. 2018

Int J Intell Syst

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Hesitant information is powerful and flexible to denote decision maker's judgments. Hesitant multiplicative preference relations (HMPRs) own the advantages of preference relations and hesitant fuzzy sets that permit the decision makers (DMs) to compare objects by using several values. Just as other types of preference relations, how to derive the priority weight vector is a crucial step. According to the principle of the consistency concept for multiplicative preference relations, this paper first introduces a new consistency concept for HMPRs, which avoids the disadvantages of the previous ones. Using the new concept, models to judge the consistency of HMPRs are built. Then, a consistency probability‐based method to derive the hesitant fuzzy priority weight vector from HMPRs is offered. Considering the incomplete case, consistency‐based programming models to determine the missing values are constructed. To address group decision making with HMPRs, a distance measure is defined to determine the weights of the DMs, and a consensus index is proposed. Then, a consistency and consensus‐based group decision‐making algorithm is performed. Finally, two practical examples, an investment problem and a water conservancy problem are offered to illustrate the feasibility and efficiency of the new algorithm. Comparison analysis from the numerical and theoretical aspects verifies the potential application of the new procedure.

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A decision support model for group decision making with hesitant fuzzy preference relations

Cited by 85 publications

References 37 publications

MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making

MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making

Hesitant Probabilistic Fuzzy Preference Relations in Decision Making

A new procedure for hesitant multiplicative preference relations

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