An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation

Shen, Feng; Ma, Xinsong; Li, Zhiyong; Zhang, Xu; Cai, Dongliang

doi:10.1016/j.ins.2017.10.045

Cited by 216 publications

(85 citation statements)

References 30 publications

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“…Zhang et al [20] propounded an extended TODIM approach combined with Choquet integral to sort products with online reviews. Shen et al [21] developed an extended TOPSIS approach with a novel distance measure to do with the credit risk evaluation issue. Liu et al [22] propounded several intuitionistic fuzzy Dombi Bonferroni mean operators, which can take into account the correlation of different attributes.…”

Section: Introductionmentioning

confidence: 99%

Linguistic Pythagorean Einstein Operators and Their Application to Decision Making

2020

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Linguistic Pythagorean fuzzy (LPF) set is an efficacious technique to comprehensively represent uncertain assessment information by combining the Pythagorean fuzzy numbers and linguistic variables. In this paper, we define several novel essential operations of LPF numbers based upon Einstein operations and discuss several relations between these operations. For solving the LPF numbers fusion problem, several LPF aggregation operators, including LPF Einstein weighted averaging (LPFEWA) operator, LPF Einstein weighted geometric (LPFEWG) operator and LPF Einstein hybrid operator, are propounded; the prominent characteristics of these operators are investigated as well. Furthermore, a multi-attribute group decision making (MAGDM) approach is presented on the basis of the developed operators under an LPF environment. Ultimately, two application cases are utilized to demonstrate the practicality and feasibility of the developed decision approach and the comparison analysis is provided to manifest the merits of it.

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Section: Introductionmentioning

confidence: 99%

Linguistic Pythagorean Einstein Operators and Their Application to Decision Making

2020

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“…e essence of MADM is the process of sorting a limited number of alternatives under given attributes and selecting the optimal one through the evaluation information provided by experts. It has been widely applied in multitudinous aspects [1][2][3][4][5][6][7][8][9]. Owing to the complicacy and nondeterminacy of the decision-making (DM) context, one of the most arduous challenges in course of MADM is how to represent the evaluation information of attributes in fuzzy and uncertain circumstances accurately.…”

Section: Introductionmentioning

confidence: 99%

Hesitant Fuzzy Linguistic Hamy Mean Aggregation Operators and Their Application to Linguistic Multiple Attribute Decision-Making

Run-zhang

Pei

Liu

2020

Mathematical Problems in Engineering

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Linguistic aggregation operator is a paramount appliance to fix linguistic multiple attribute decision-making (MADM) issues. In the article, the Hamy mean (HM) operator is utilized to fuse hesitant fuzzy linguistic (HFL) information and several novel HFL aggregation operators including the hesitant fuzzy linguistic Hamy mean (HFLHM) operator, weighted hesitant fuzzy linguistic Hamy mean (WHFLHM) operator, hesitant fuzzy linguistic dual Hamy mean (HFLDHM) operator, and weighted hesitant fuzzy linguistic dual Hamy mean (WHFLDHM) operator are proposed. Besides, several paramount theorems and particular cases of these aggregation operators are investigated in detail, and then a novel MADM approach is presented by using the proposed aggregation operators. Ultimately, a practical example is utilized to manifest the effectiveness and practicability of the propounded method.

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“…For instance, Szmidt presented distance and similarity measures for IFSs and applied them to various DM problems. Shen et al proposed a novel distance measure and discussed its various properties and further developed a technique for order preference by similarity to ideal solution (TOPSIS) based on the proposed measure. Ye put forward two similarity measures based on cosine functions and illustrated their effectiveness by comparing them with existing trigonometric similarity measures.…”

Section: Introductionmentioning

confidence: 99%

Some results on information measures for complex intuitionistic fuzzy sets

Garg

Rani

2019

Int J Intell Syst

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Complex intuitionistic fuzzy sets (CIFSs), modeled by complex‐valued membership and nonmembership functions with codomain the unit disc in a complex plane, handle two‐dimensional information in a single set. Under this environment, the primary objective of the present study is to introduce some novel formulae of information measures (similarity measures, distance measures, entropies, and inclusion measures) and discuss the transformation relationships among them. To demonstrate the efficiency of the proposed similarity measures, we apply it to pattern recognition problem and a detailed comparative analysis is conducted with some of the existing measures. Further, algorithms based on proposed measures are developed for handing multicriteria decision‐making problems and their working is illustrated with the help of an example. Besides this, the practicality of the proposed similarity measure is demonstrated by developing a clustering algorithm under CIFS environment.

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An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation

Cited by 216 publications

References 30 publications

Linguistic Pythagorean Einstein Operators and Their Application to Decision Making

Linguistic Pythagorean Einstein Operators and Their Application to Decision Making

Hesitant Fuzzy Linguistic Hamy Mean Aggregation Operators and Their Application to Linguistic Multiple Attribute Decision-Making

Some results on information measures for complex intuitionistic fuzzy sets

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