A New Method of Multiattribute Decision‐Making Based on Interval‐Valued Hesitant Fuzzy Soft Sets and Its Application

Yan, Yang; Lei, Lang; Lu, Liuli; Sun, Yangmei

doi:10.1155/2017/9376531

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…Step 3. Let = ( (1) , (2) , … , (t) ) be the ultimate weighting vector of expert, which can be comprehensively determined as follows:…”

Section: The Weighting Vector Of Expert Determined By Expertise Of Exmentioning

confidence: 99%

“…Step 2. Obtain the weighting vector of expert = ( (1) , (2) , ⋯ , (t) ) by using our method proposed in Section 3.…”

Section: Integrated Ivif Group Decision-making Approach With Unknown mentioning

confidence: 99%

“…Decision-making is generally considered a process in which human beings make choices among several alternatives [1]. In real life, due to the increasingly complex socioeconomic environment, it is impossible for a single decision-maker (DM) or expert to consider all relevant aspects of the decision-making problem without difficulty [2][3][4][5][6]. Therefore, many practical decisions are often made by multiple DMs or experts, which leads to abundant research concerning the topic of multi-attribute group decision-making (MAGDM) problems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Cross-Entropy Based on Multi-attribute Group Decision-Making with Unknown Experts' Weights Under Interval-Valued Intuitionistic Fuzzy Environment

Li¹,

Cheng²,

Qiong³

et al. 2020

IJCIS

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This paper studies the multi-attribute group decision-making problems with unknown experts' weights under interval-valued intuitionistic fuzzy environment. First, in order to provide more flexibilities for decision-makers in actual decision-making problems, a novel cross-entropy measure with parameter of interval-valued intuitionistic fuzzy set (IVIFS) based on J-divergence is proposed. The novel cross-entropy measure can obtain more flexible and practical optimal ranking results by adjusting the parameter. Then, by using the designed cross-entropy measure, two models are established to obtain experts' weights, which consider the influence of experts' experience and professional knowledge on experts' weights. Finally, two examples are provided to illustrate the effectiveness and applicability of optimizing the group decision-making approach.

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“…Step 3. Let = ( (1) , (2) , … , (t) ) be the ultimate weighting vector of expert, which can be comprehensively determined as follows:…”

Section: The Weighting Vector Of Expert Determined By Expertise Of Exmentioning

confidence: 99%

“…Step 2. Obtain the weighting vector of expert = ( (1) , (2) , ⋯ , (t) ) by using our method proposed in Section 3.…”

Section: Integrated Ivif Group Decision-making Approach With Unknown mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Novel Cross-Entropy Based on Multi-attribute Group Decision-Making with Unknown Experts' Weights Under Interval-Valued Intuitionistic Fuzzy Environment

Li¹,

Cheng²,

Qiong³

et al. 2020

IJCIS

View full text Add to dashboard Cite

show abstract

“…Liu et al [44] used the continuous entropy weights and improved Hamacher information aggregation operators to aggregate interval-valued hesitant fuzzy information. Yang et al [45] used the possibility degree to present a new comparative law for MADM problems with interval-valued hesitant fuzzy soft sets.…”

Section: Introductionmentioning

confidence: 99%

Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

Guan¹,

Sun²,

Xiao³

et al. 2018

Mathematical Problems in Engineering

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Due to the superiority in expressing the uncertain and vague information, the hesitant fuzzy set (HFS) is regarded as an important tool to deal with multiattribute decision-making (MADM) problems. Quantitative and qualitative fuzzy measures have been proposed to solve such problems from different points. However, most of the existing information measures for HFSs are related to such fuzzy measures as distance, similarity, entropy, and correlation coefficients. The grey relational analysis is omitted. Besides, the existing grey relational analysis for HFSs only considers the range or distance between HFSs data which is only a partial measure of the HFSs. Therefore, in this paper, we improve the grey relational analysis for HFSs and explore a novel slope grey relational degree by considering another factor of HFSs data: the slope. Further, we combine both the distance and slope factors of HFSs data to construct a synthetic grey relational degree that describes the closeness and variation tendency of HFSs simultaneously, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs MADM problems. Finally, combining with two practical MADM examples about energy policy selection and multisensor target recognition, we obtain the most desirable decision results. Compared with the previous methods, the validity, comprehensiveness, and discrimination of the proposed synthetic grey relational degree for HFSs are demonstrated in detail.

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“…Furthermore, with the initiation of interval-valued intuitionistic fuzzy set (IVIFS) by Atanassov [18], MADM based on IVIFS becomes a hot topic for researchers [19][20][21][22][23][24][25][26][27]. The applications of type-2 fuzzy sets [28][29][30], hesitant fuzzy sets [31,32], and dual hesitant fuzzy linguistic term sets [33] were also reported recently. From these applications, we can find that the extension theories provided researchers with more and more profound theoretical models of fuzzy theory to depict and solve the complicated real-life MADM problems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Attribute Expansion Method for Multiple Attribute Decision-Making with Partial Attribute Values and Weights Unknown and Its Applications

2018

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In the real world, there commonly exists types of multiple attribute decision-making (MADM) problems with partial attribute values and weights totally unknown. Symmetry among some attribute information that is already known and unknown, and symmetry between the pure attribute set and fuzzy attribute membership set can be a considerable way to solve this type of MADM problem. In this paper, a fuzzy attribute expansion method is proposed to solve this type of problem based on two key techniques: the spline interpolation technique and the attribute weight reconfiguration technique, which are respectively used for the determination of attribute values and the reconfiguration of attribute weights. The spline interpolation technique to expand attribute values can enhance the performance of some regression methods and clustering methods by the comparisons between the results of these methods dealing with practical cases with and without the application of the technique, which further illustrates the effectiveness of this technique. For MADM problems with partial attribute values and weights totally unknown, compared with traditional fuzzy comprehensive evaluation (FCE), FCE with the application of fuzzy attribute expansion method can obtain results more similar with the ones when all attribute values and weights are known, which is proved by the practical power quality evaluation example.

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A New Method of Multiattribute Decision‐Making Based on Interval‐Valued Hesitant Fuzzy Soft Sets and Its Application

Cited by 8 publications

References 25 publications

Novel Cross-Entropy Based on Multi-attribute Group Decision-Making with Unknown Experts' Weights Under Interval-Valued Intuitionistic Fuzzy Environment

Novel Cross-Entropy Based on Multi-attribute Group Decision-Making with Unknown Experts' Weights Under Interval-Valued Intuitionistic Fuzzy Environment

Grey Relational Analysis for Hesitant Fuzzy Sets and Its Applications to Multiattribute Decision-Making

Fuzzy Attribute Expansion Method for Multiple Attribute Decision-Making with Partial Attribute Values and Weights Unknown and Its Applications

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