Generalized hesitant fuzzy numbers and their application in solving MADM problems based on TOPSIS method

Keikha, Abazar

doi:10.1007/s00500-022-06995-z

Cited by 15 publications

(5 citation statements)

References 57 publications

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“…Deli and Karaaslan proposed a decision-making method to solve the multi-criteria decision-making problems in which criteria values take the form of generalized trapezoidal hesitant fuzzy information 53 . Keikha generalized hesitant fuzzy numbers and their application in solving multi-attribute decision-making problems 54 . Ranjbar et al developed the hesitant fuzzy arithmetic and ordering method on HFNs, then use them on simple additive weighting (SAW) method based on the extension of Bonissone’s approach in the hesitant fuzzy environment 55 .…”

Section: Introductionmentioning

confidence: 99%

Group decision making in the analytic hierarchy process by hesitant fuzzy numbers

Ranjbar,

Effati

2023

Sci Rep

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Due to the increasing complexity of decision problems, many managers employ multiple experts to reach a good decision in a group decision making. Now, if there is ambiguity in the evaluation of experts, the use of fuzzy numbers can be useful for each expert. In these situations, the use of hesitant fuzzy numbers (HFNs) which consists of several fuzzy numbers with special conditions can be suggested. HFNs are as an extension of the fuzzy numbers to take a better determining the membership functions of the parameters by several experts. Because of simple and fast calculations, in this paper, we use triangular HFNs in the pairwise comparison matrix of analytic hierarchy process by opinions of a group of decision makers in a hesitant fuzzy environment. We define consistency of the hesitant fuzzy pairwise comparison matrix and use the arithmetic operations on the HFNs and a new method of comparing HFNs to get the hesitant fuzzy performance score. By using score function to hesitant fuzzy score we can get a final score for alternatives. Finally, a practical example is provided to show the the effectiveness of this study. The obtained results from this paper show that new method can get a better answer by keeping the experts’ opinions in the process of solving the problem.

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Section: Introductionmentioning

confidence: 99%

Group decision making in the analytic hierarchy process by hesitant fuzzy numbers

Ranjbar,

Effati

2023

Sci Rep

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“…Generalized fuzzy numbers allow for more flexible shapes, which can better capture the uncertainty or imprecision inherent in many real-world situations. Generalized fuzzy numbers have been widely used in fuzzy mathematics [2][3][4], decisionmaking [5,6], and control theory [7], among other fields [2,8,9]. They provide a useful tool for representing uncertain or vague information in a formal way and for making decisions in situations where there is a high degree of uncertainty or ambiguity.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to medical diagnosis, a great deal of study in other areas [14,15] involving hesitant fuzzy sets has been carried out. Additionally, many studies have been conducted on generalized fuzzy numbers, including generalized triangular intuitionistic fuzzy numbers (GTIFN) [16], generalized trapezoidal hesitant fuzzy numbers (GTrHFN) [2], generalized hesitant fuzzy numbers (GHFN) [8], and generalized hexagonal fuzzy numbers (GH χ FN) [17]. However, as far as we know, there is currently no research on the use of a generalized dual hesitant hexagonal fuzzy (GDHH χ F) MCDM technique for disease recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Example 3. Let consider ∆1 , ∆2 , ∆3 and ∆4 are four GDHH χ FNs define as ∆1 =<(5,6,7,8,9,10); {(0.7, 0.2), (0.9, 0.05), (0.85, 0.1)} >, ∆2 =< (0.1, 0.2, 0.3, 0.6, 0.7, 0.8); {(0.8, 0.15), (0.75, 0.2), (0.9, 0)} >, ∆3 =< (0.60, 0.65, 0.75, 0.85, 0.95, 1); {(0.9, 0.05), (0.75, 0.2), (0.85, 0.1)} > and ∆4 =<(9,10,12,14,16,17); {(1, 0), (0.9, 0.05), (0.8, 0.15)} >.…”

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confidence: 99%

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Prediagnosis of Disease Based on Symptoms by Generalized Dual Hesitant Hexagonal Fuzzy Multi-Criteria Decision-Making Techniques

Momena

Mandal

Gazi³

et al. 2023

Systems

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Multi-criteria decision-making (MCDM) is now frequently utilized to solve difficulties in everyday life. It is challenging to rank possibilities from a set of options since this process depends on so many conflicting criteria. The current study focuses on recognizing symptoms of illness and then using an MCDM diagnosis to determine the potential disease. The following symptoms are considered in this study: fever, body aches, fatigue, chills, shortness of breath (SOB), nausea, vomiting, and diarrhea. This study shows how the generalised dual hesitant hexagonal fuzzy number (GDHHχFN) is used to diagnose disease. We also introduce a new de-fuzzification method for GDHHχFN. To diagnose a given condition, GDHHχFN coupled with MCDM tools, such as the fuzzy criteria importance through inter-criteria correlation (FCRITIC) method, is used for finding the weight of criteria. Furthermore, the fuzzy weighted aggregated sum product assessment (FWASPAS) method and a fuzzy combined compromise solution (FCoCoSo) are used to rank the alternatives. The alternative diseases are chosen to be malaria, influenza, typhoid, dengue, monkeypox, ebola, and pneumonia. A sensitivity analysis is carried out on three patients affected by different diseases to assess the validity and reliability of our methodologies.

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“…Liu et al [ 26 ] have proposed an integrated multiattribute group decision-making (MAGDM) framework, which is developed to assess the new energy investment problems. Keikha [ 27 ] utilized GHFNs to model the uncertainty of the assessment values of options against criteria in (MADM) problems. It means that all of the elements of decision matrix are GHFNs.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Fuzzy TOPSIS Method for Investment Decision Optimization of Large-Scale Projects

Cao

2022

Computational Intelligence and Neuroscience

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Investment of large-scale projects must consider various factors, such as economic conditions and investment environment when making decisions. In large-scale project investment problems, almost 90% of them are completed in a multiobjective context. To this end, the authors of this paper have proposed an entropy-based fuzzy TOPSIS synthesis method to assist in the decision-making optimization of large-scale project investment. Firstly, in accordance with the background of the development of multiattribute decision-making method (MADM), closely related to large-scale project investment, the related research works were studied, and the relevant methods are sorted out. Then, the improved vague-entropy-weight based fuzzy TOPSIS (VEWF-TOPSIS) method, entropy and interval language intuitionistic fuzzy sets based TOPSIS (EILIF-TOPSIS) method, and information entropy attribute importance based intuitionistic fuzzy TOPSIS (IEAI-IF-TOPSIS) method were introduced, and a synthesis MADM method that comprehensively combines the above three methods was proposed. Finally, a numerical case was constructed to properly show the effectiveness of the method proposed in the present work, and the conclusions were put forward for providing ideas and methods for large-scale project investment decision-making. It is of great significance that the proposed framework would make large-scale project investment decisions more reasonable and practical, which enriches the research methods of MADM problems to a certain extent and can provide reference for the actual large-scale project investment decision-making problems.

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Generalized hesitant fuzzy numbers and their application in solving MADM problems based on TOPSIS method

Cited by 15 publications

References 57 publications

Group decision making in the analytic hierarchy process by hesitant fuzzy numbers

Group decision making in the analytic hierarchy process by hesitant fuzzy numbers

Prediagnosis of Disease Based on Symptoms by Generalized Dual Hesitant Hexagonal Fuzzy Multi-Criteria Decision-Making Techniques

Entropy-Based Fuzzy TOPSIS Method for Investment Decision Optimization of Large-Scale Projects

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