Group-based generalized q-rung orthopair average aggregation operators and their applications in multi-criteria decision making

Hussain, Azmat; Ali, Muhammad Intizar; Mahmood, Tahir; Munir, Muhammad

doi:10.1007/s40747-020-00176-x

Cited by 10 publications

(10 citation statements)

References 38 publications

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“…3. If we solve with proposed method the example about the disease of Pneumonia proposed [29] The proposed GGq-ROFAAWA by using decision matrix defined by Hussain et al [33], the obtained aggregated q-ROFs that 0.9272, 0.1631 , 0.7323, 0.1637 , 0.7374, 0.3180 , 0.7362, 0.2376 and from here if score values, s(ξ 1 ) = 0.7928, s(ξ 2 ) = 0.3883, s(ξ 3 ) = 0.3689, s(ξ 4 ) = 0.3857. If the ordering, is…”

Section: Comparative and Discussionmentioning

confidence: 99%

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Group based generalized parameters q-rung orthopairfuzzy Aczel- Alsina weighted average operator and anapplication over green industry development

Özlü

2023

Preprint

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The generalized concepts matter in terms of providing more general structures. Therefore,in this paper a wider structure is obtained by being used together q- Rung Orthopair fuzzysets (q- ROFs) and Aczel- Alsina average operators. Q- ROFs are superior than several clusters as intuitionistic fuzzy sets (IFSs) and the Pythagorean fuzzy sets (PFSs) because of hostvariableness. Aczel- Alsina t- norm (AA-TN) and t- conorm (AA-TCN) structures were putforward by Aczel and Alsina in 1982. These operators have very useful structure due to having changeability parameters. The objective of this paper is to introduce q-rung orthopair fuzzy Aczel- Alsina weighted average operator (q-ROFAAWA) by using Aczel- Alsina Average opera-tors (AAAOs). Then, we give generalized q-rung orthopair fuzzy Aczel- Alsina weighted averageoperator (Gq-ROFAAWA) and introduce Group generalized based Q-rung orthopair fuzzy Aczel-Alsina weighted average operator (GGq-ROFAAWA) with helping to (Gq-ROFAAWA). Generalized parameter is essential to induce error margin while decision makers decide final options about a subject. Moreover, we order an algorithm of presented approach and give an example toindicate flexible construction of GGq-ROFAAWA. Immediately after, we introduce a comparative analysis with different methods which indicate the superiority of proposed concept thanexisting methods.

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Section: Comparative and Discussionmentioning

confidence: 99%

“…Thus, two different matrices and weight vectors are revealed and these matrices are converted to generalized assessment matrix by combining two matrices. The several works have been made about this subject, see [30,31,32,33].…”

Section: Introductionmentioning

confidence: 99%

Group based generalized parameters q-rung orthopairfuzzy Aczel- Alsina weighted average operator and anapplication over green industry development

Özlü

2023

Preprint

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“…Hussain et al [35] also proposed and discussed the hesitant q-ROFWA and hesitant q-ROFWG operators, as well as their desirable properties. Hussain et al [36] defined the generalized and group generalized averaging operation based on q-ROF information. Wang et al [37] defined the decision-making based on q-rung orthopair fuzzy soft rough sets.…”

Section: Covid-19 Pandemic In Pakistanmentioning

confidence: 99%

Decision Support System Based on CoCoSo Method with the Picture Fuzzy Information

Qiyas

Naeem

Khan

et al. 2022

Journal of Mathematics

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Coronavirus disease 2019 (COVID-19) is currently threatening the entire world, and a novel coronavirus is a virus from the corona family that has spread a new infection. The number of instances of this disease is increasing at an exponential, but there are now commercially accessible COVID-19 vaccines. The weak symptoms of COVID-19 disease, on the other hand, are treated with a variety of antiviral treatments. It is still choosing the optimal antiviral medicine to manage COVID-19s. It is a challenging and difficult alternative to reduce the risk of infection. In this study, an improved combined compromise solution (CoCoSo) method is proposed to identify the ranking of alternatives. The introduction of a logarithmic picture fuzzy set is a more effective technique for representing variance, represented by three memberships (positive, neutral, and negative membership) degrees. This work introduces a fresh logarithmic picture fuzzy score function, to deal with the problem of comparison. The CoCoSo method-based logarithmic picture fuzzy decision-making algorithm is given. To achieve so, a new divergence measure for the logarithmic picture fuzzy number is introduced. To demonstrate the viability and efficacy of the established approach in real-world applications, a case study of COVID-19 disease drug selection is discussed.

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“…Recently, q-ROFSs have been studied by many researchers. [16][17][18] Further applications of q-ROFSs can be seen in [19][20][21][22][23][24].…”

Section: A′mentioning

confidence: 99%

Why do we need q‐rung orthopair fuzzy sets? Some evidence established via mass assignment

2021

Self Cite

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Intuitionistic fuzzy sets (IFSs) have advantage over fuzzy sets and made it possible to describe imprecise information considering its positive and negative aspects simultaneously. In an information system mass assignment and possibility theory are very useful to assign membership grades to elements in a fuzzy set.Unfortunately the situation differs for IFSs in assigning membership function (MF) and nonmembership function (NMF). In this paper, it is shown that the above-mentioned theories fail to produce the MF and NMF for IFSs. Aim of this paper is to present an alternate algorithm to generate these grading functions based on q-rung orthopair fuzzy set. Consequently, it will be extremely convenient to model imprecise and vague information using this approach. K E Y W O R D Sintuitionistic fuzzy sets, mass assignment theory, possibility theory, q-rung orthopair fuzzy sets | INTRODUCTIONBefore the advent of fuzzy set theory, probability theory was the only scientific way to deal with uncertainty. The inception of fuzzy set theory, 1 was a paradigm shift, which was followed by possibility theory, 2 the theory of evidence, 3,4 rough set theory, and soft set theory. These theories changed the perspective of the scientific community to see vague and uncertain phenomena. So new lines of research emerged, which deal with vague, uncertain, or incomplete information systems.How to cite this article: Shaheen T, Ali MI, Toor H. Why do we need q-rung orthopair fuzzy sets? Some evidence established via mass assignment.

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Group-based generalized q-rung orthopair average aggregation operators and their applications in multi-criteria decision making

Cited by 10 publications

References 38 publications

Group based generalized parameters q-rung orthopairfuzzy Aczel- Alsina weighted average operator and anapplication over green industry development

Group based generalized parameters q-rung orthopairfuzzy Aczel- Alsina weighted average operator and anapplication over green industry development

Decision Support System Based on CoCoSo Method with the Picture Fuzzy Information

Why do we need q‐rung orthopair fuzzy sets? Some evidence established via mass assignment

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