Multi-criteria group decision making with incomplete hesitant fuzzy preference relations

Zhang, Zhiming; Wang, Chao; Tian, Xuedong

doi:10.1016/j.asoc.2015.06.047

Cited by 57 publications

(21 citation statements)

References 45 publications

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“…Their main difference is that Zhu et al's method adopts the multiplicative consistency, whereas Zhang et al's method applies the additive consistency. In addition, Zhang, Wang, and Tian () proposed an additive‐consistency‐based method to address incomplete HFPRs that considers the known values to be consistent. Meng and An (2017) developed a multiplicative‐consistency‐and‐ consensus‐based method for GDM with HFPRs that can address incomplete and inconsistent HFPRs.…”

Section: Literature Reviewmentioning

confidence: 99%

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

et al. 2019

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Hesitant fuzzy linguistic preference relations (HFLPRs) can efficiently denote the hesitant qualitative judgments of decision makers. Consistency and consensus are two critical topics in group decision making (GDM) with preference relations. This paper uses the additively consistent concept for linguistic fuzzy preference relations (LFPRs) to give an additive consistency definition for HFLPRs. To judge the additive consistency of HFLPRs, 0‐1 mixed programming models (0‐1‐MPMs) are constructed. Meanwhile, additive‐consistency‐based 0‐1‐MPMs to ascertain missing values in incomplete HFLPRs are established. Following the consistent probability of LFPRs, an algorithm to calculate the linguistic priority weighting vector is presented. In consideration of the consensus of GDM, a consistency‐probability‐distance‐measure‐based consensus index is defined, and an interactive improving consensus method is provided. Finally, a method for GDM with HFLPRs is offered that can address incomplete and inconsistent cases. Meanwhile, numerical examples are offered, and comparative analysis is made.

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Section: Literature Reviewmentioning

confidence: 99%

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

et al. 2019

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“…Because of various kinds of reasons, such as time limitation, lacking information, and the DMs’ subjective preferences, some values in preference relations might be missing, which are the so‐called incomplete preference relations . However, there are fewer studies about incomplete HMPRs.…”

Section: Models To Determine the Missing Values In Incomplete Hmprsmentioning

confidence: 99%

“…Because of various kinds of reasons, such as time limitation, lacking information, and the DMs' subjective preferences, some values in preference relations might be missing, which are the socalled incomplete preference relations. 10,14,32,33,[45][46][47] However, there are fewer studies about incomplete HMPRs. Considering this situation, this section constructs several consistencybased models to determine the missing values in incomplete HMPRs.…”

Section: Incomplete Hmprsmentioning

confidence: 99%

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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“…Xia and Xu 11 first proposed the concept of hesitant fuzzy preference relation (HFPR), which provided a precise description of the decision makers' (DMs') hesitation in providing their preferences. Till now, numerous types of preference relations have been proposed: complete/ incomplete HFPRs 16,17,18,19,20 , hesitant fuzzy linguistic preference relation 21 , and hesitant multiplicative preference relation.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang 16 established two goal programming for deriving the priority weights from incomplete HFPR based on α-normalization and β -normalization respectively. Zhang et al 18 proposed an algorithm to estimate the missing values and solve the multi-criteria GDM problem with incomplete HFPRs. As far as we know, there are only above three papers which concentrate on the incomplete HFPR.…”

Section: Introductionmentioning

confidence: 99%

Missing values estimation and consensus building for incomplete hesitant fuzzy preference relations with multiplicative consistency

Xu¹,

Li²,

Wen³

2018

IJCIS

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This paper proposes a decision support process for incomplete hesitant fuzzy preference relations (HFPRs). First, we present a revised definition of HFPRs, in which the values are not ordered for the hesitant fuzzy element. Second, we propose a method to normalize the HFPRs and estimate the missing elements in incomplete HFPRs based on multiplicative consistency. Based on this, a consensus model with incomplete HFPR is developed. A feedback mechanism is proposed to obtain a best choice with desired consensus level. Multiplicative consistency induced ordered weighted averaging (MC-IOWA) operator is used to aggregate the individual HFPRs into a collective one. A score HFPR is proposed for collective HFPR, and then the hesitant quantifier-guided non-dominance degrees (HQGNDD) of alternatives by using an OWA operator are obtained to rank the alternatives. Finally, a case study for evaluate the qualification of supply chain enterprises is provided to illustrate its application.

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Multi-criteria group decision making with incomplete hesitant fuzzy preference relations

Cited by 57 publications

References 45 publications

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

Qualitative hesitant fuzzy group decision making: An additively consistent probability and consensus‐based perspective

A new procedure for hesitant multiplicative preference relations

Missing values estimation and consensus building for incomplete hesitant fuzzy preference relations with multiplicative consistency

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