Multi-attribute group decision making based on cubic bipolar fuzzy information using averaging aggregation operators

Riaz, Muhammad; Tehrim, Syeda Tayyba

doi:10.3233/jifs-182751

Cited by 51 publications

(14 citation statements)

References 48 publications

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“…Riaz et al [18,19] introduced the soft rough topology including its applications to group decision making. Riaz and Tehrim [20][21][22] originated the notions of the bipolar fuzzy soft topology, cubic bipolar fuzzy sets, and operators. By using diverse algorithms, they solved some new and challenging decision making applications.…”

Section: Literature Reviewmentioning

confidence: 99%

Linear Diophantine Fuzzy Soft Rough Sets for the Selection of Sustainable Material Handling Equipment

et al. 2020

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The concept of linear Diophantine fuzzy sets (LDFSs) is a new approach for modeling uncertainties in decision analysis. Due to the addition of reference or control parameters with membership and non-membership grades, LDFS is more flexible and reliable than existing concepts of intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (q-ROFSs). In this paper, the notions of linear Diophantine fuzzy soft rough sets (LDFSRSs) and soft rough linear Diophantine fuzzy sets (SRLDFSs) are proposed as new hybrid models of soft sets, rough sets, and LDFS. The suggested models of LDFSRSs and SRLDFSs are more flexible to discuss fuzziness and roughness in terms of upper and lower approximation operators. Certain operations on LDFSRSs and SRLDFSs have been established to discuss robust multi-criteria decision making (MCDM) for the selection of sustainable material handling equipment. For these objectives, some algorithms are developed for the ranking of feasible alternatives and deriving an optimal decision. Meanwhile, the ideas of the upper reduct, lower reduct, and core set are defined as key factors in the proposed MCDM technique. An application of MCDM is illustrated by a numerical example, and the final ranking in the selection of sustainable material handling equipment is computed by the proposed algorithms. Finally, a comparison analysis is given to justify the feasibility, reliability, and superiority of the proposed models.

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Section: Literature Reviewmentioning

confidence: 99%

Linear Diophantine Fuzzy Soft Rough Sets for the Selection of Sustainable Material Handling Equipment

et al. 2020

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“…In 2020, Yiarayong 25 applied the theory of intervalvalued fuzzy soft sets (IVFSSs) to semigroups and introduced the notion of interval-valued fuzzy soft sets (IVFSSs), which is a generalization of fuzzy soft sets (FSSs). After fuzzy set theory, many set theories have been developed interval-valued fuzzy set theory and BFS theory, [26][27][28][29][30][31][32][33][34][35][36] and so forth.…”

Section: Introductionmentioning

confidence: 99%

A new approach of bipolar valued fuzzy set theory applied on semigroups

Yiarayong

2021

Int J Intell Syst

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In this paper, we generalize the concept of interval‐valued bipolar fuzzy sets (IVBFSs) and define interval‐valued bipolar fuzzy subsemigroups (IVBF‐subsemigroup) and interval‐valued bipolar fuzzy left (right, two‐sided) ideals (IVBF‐left [right, two‐sided] ideals) over semigroups, which is a generalization of the concept of an bipolar valued fuzzy set (BVFS) in a semigroup. The purpose of this paper is to deal with the algebraic structure of semigroups by applying IVBFS theory. We give characterizations of different classes of (intra‐regular, left [right] regular, regular, semisimple) semigroups by the properties of their IVBF‐ideals. We also characterize these classes in terms of IVBF‐left ideals, IVBF‐right ideals, and IVBF‐two‐sided ideals. In this respect, we prove that a semigroup is regular if and only if for every IVBF‐right ideal true A ˜ = )( true μ A P ˜ , true μ A N ˜ and every IVBF‐left ideal true B ˜ = )( true μ B P ˜ , true μ B N ˜ over S , we have true A ˜ ∩ true B ˜ = true A ˜ ⊙ true B ˜ . Further, we characterize intra‐regular and regular semigroups and prove that a semigroup is intra‐regular and regular if and only if for every IVBF‐left ideal true A ˜ = )( true μ A P ˜ , true μ A N ˜ and every IVBF‐right ideal true B ˜ = )( true μ B P ˜ , true μ B N ˜ over S we have true A ˜ ∩ true B ˜ 0.25em ≼ 0.25em true A ˜ ⊙ true B ˜ .

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“…According to the overall criteria values of alternatives, all the alternatives can be ranked and then the optimal one can be obtained (Mi et al, 2019). To improve the ranking results of MCDM problems, a variety of aggregation operators have been proposed (Kang et al, 2018;Liu et al, 2020;Riaz & Tehrim, 2019;, such as weighted averaging operator, weighted geometric operator, and ordered weighted averaging (OWA) operator (Yager, 1988;. The OWA operator was proposed by Yager (1988).…”

Section: Introductionmentioning

confidence: 99%

Determine OWA operator weights using kernel density estimation

Lin

et al. 2020

Economic Research-Ekonomska Istraživanja

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Some subjective methods should divide input values into local clusters before determining the ordered weighted averaging (OWA) operator weights based on the data distribution characteristics of input values. However, the process of clustering input values is complex. In this paper, a novel probability density based OWA (PDOWA) operator is put forward based on the data distribution characteristics of input values. To capture the local cluster structures of input values, the kernel density estimation (KDE) is used to estimate the probability density function (PDF), which fits to the input values. The derived PDF contains the density information of input values, which reflects the importance of input values. Therefore, the input values with high probability densities (PDs) should be assigned with large weights, while the ones with low PDs should be assigned with small weights. Afterwards, the desirable properties of the proposed PDOWA operator are investigated. Finally, the proposed PDOWA operator is applied to handle the multicriteria decision making problem concerning the evaluation of smart phones and it is compared with some existing OWA operators. The comparative analysis shows that the proposed PDOWA operator is simpler and more efficient than the existing OWA operators.

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Multi-attribute group decision making based on cubic bipolar fuzzy information using averaging aggregation operators

Cited by 51 publications

References 48 publications

Linear Diophantine Fuzzy Soft Rough Sets for the Selection of Sustainable Material Handling Equipment

Linear Diophantine Fuzzy Soft Rough Sets for the Selection of Sustainable Material Handling Equipment

A new approach of bipolar valued fuzzy set theory applied on semigroups

Determine OWA operator weights using kernel density estimation

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