TOPSIS, VIKOR and aggregation operators based on q-rung orthopair fuzzy soft sets and their applications

Riaz, Muhammad; Hamid, Muhammad Tahir; Farid, Hafiz Muhammad Athar; Afzal, Deeba

doi:10.3233/jifs-192175

Cited by 26 publications

(14 citation statements)

References 45 publications

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“…. Riaz et al presented a holistic approach towards q-ROF interactive AOs [66] and AOs related to q-ROF soft set [67]. Ye et al [68] introduced MCDM method based on fuzzy rough sets.…”

Section: Mamps Methodsmentioning

confidence: 99%

Single-valued neutrosophic Einstein interactive aggregation operators with applications for material selection in engineering design: case study of cryogenic storage tank

Farid

Riaz

2022

Complex Intell. Syst.

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Single-valued neutrosophic sets (SVNSs) and their application to material selection in engineering design. Liquid hydrogen is a feasible ingredient for energy storage in a lightweight application due to its high gravimetric power density. Material selection is an essential component in engineering since it meets all of the functional criteria of the object. Materials selection is a time-consuming as well as a critical phase in the design process. Inadequate material(s) selection can have a detrimental impact on a manufacturer’s production, profitability, and credibility. Multi-criteria decision-making (MCDM) is an important tool in the engineering design process that deals with complexities in material selection. However, the existing MCDM techniques often produce conflicting results. To address such problems, an innovative aggregation technique is proposed for material selection in engineering design based on truthness, indeterminacy, and falsity indexes of SVNSs. Taking advantage of SVNSs and smooth approximation with interactive Einstein operations, single-valued neutrosophic Einstein interactive weighted averaging and geometric operators are proposed. Based on proposed AOs, a robust MCDM approach is proposed for material selection in engineering design. A practical application of the proposed MCDM approach for material selection of cryogenic storage containers is developed. Additionally, the authenticity analysis and comparison analysis are designed to discuss the validity and rationality of the optimal decision.

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“…. Riaz et al presented a holistic approach towards q-ROF interactive AOs [66] and AOs related to q-ROF soft set [67]. Ye et al [68] introduced MCDM method based on fuzzy rough sets.…”

Section: Mamps Methodsmentioning

confidence: 99%

Single-valued neutrosophic Einstein interactive aggregation operators with applications for material selection in engineering design: case study of cryogenic storage tank

Farid

Riaz

2022

Complex Intell. Syst.

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“…en, pair (S, A) � (g, S(g))|g ∈ A, S(g) ∈ P(X) 􏼈 􏼉 is called the soft set. Definition 9 (see [49]). Let (X, E) be a soft universe and A ⊆ E. Define a mapping Q: A ⟶ q − ROFS(X); then, pair (Q, A) is called the q-rung orthopair fuzzy soft set (q-ROFSS) over X, where q − ROFS(X) denotes the collection of all q-ROFSs over X. e q-ROFSS (Q, A) can be described as…”

Section: Q-rungmentioning

confidence: 99%

“…Recently, q-rung orthopair fuzzy soft sets (q-ROFSSs) have been introduced by Hamid et al [48]. e model of q-ROFSS is a valuable tool to deal with vagueness by means of the label of parameters along with reliable and unreliable grades in the larger space [49]. Hussain et al [50] presented MCDM techniques using averaging operators on q-ROFSSs.…”

Section: Introductionmentioning

confidence: 99%

Group Generalized q-Rung Orthopair Fuzzy Soft Sets: New Aggregation Operators and Their Applications

Hayat

Shamim

AlSalman

et al. 2021

Mathematical Problems in Engineering

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In recent years, q-rung orthopair fuzzy sets have been appeared to deal with an increase in the value of q > 1 , which allows obtaining membership and nonmembership grades from a larger area. Practically, it covers those membership and nonmembership grades, which are not in the range of intuitionistic fuzzy sets. The hybrid form of q-rung orthopair fuzzy sets with soft sets have emerged as a useful framework in fuzzy mathematics and decision-makings. In this paper, we presented group generalized q-rung orthopair fuzzy soft sets (GGq-ROFSSs) by using the combination of q-rung orthopair fuzzy soft sets and q-rung orthopair fuzzy sets. We investigated some basic operations on GGq-ROFSSs. Notably, we initiated new averaging and geometric aggregation operators on GGq-ROFSSs and investigated their underlying properties. A multicriteria decision-making (MCDM) framework is presented and validated through a numerical example. Finally, we showed the interconnection of our methodology with other existing methods.

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“…New interaction power Bonferroni mean aggregation operators were developed by Wang and Li [33] for MADM of real-world problems. Riaz et al [34][35][36][37] proposed q-rung orthopair fuzzy hybrid, Einstein, prioritized, and Einstein prioritized AOs. Einstein operators, studied by Wang and Liu [38], were based on uncertain intuitionistic fuzzy information.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems

Iampan

Santos‐García

Riaz

et al. 2021

Journal of Mathematics

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The linear Diophantine fuzzy set (LDFS) has been proved to be an efficient tool in expressing decision maker (DM) evaluation values in multicriteria decision-making (MCDM) procedure. To more effectively represent DMs’ evaluation information in complicated MCDM process, this paper proposes a MCDM method based on proposed novel aggregation operators (AOs) under linear Diophantine fuzzy set (LDFS). A q -Rung orthopair fuzzy set ( q -ROFS), Pythagorean fuzzy set (PFS), and intuitionistic fuzzy set (IFS) are rudimentary concepts in computational intelligence, which have diverse applications in modeling uncertainty and MCDM. Unfortunately, these theories have their own limitations related to the membership and nonmembership grades. The linear Diophantine fuzzy set (LDFS) is a new approach towards uncertainty which has the ability to relax the strict constraints of IFS, PFS, and q –ROFS by considering reference/control parameters. LDFS provides an appropriate way to the decision experts (DEs) in order to deal with vague and uncertain information in a comprehensive way. Under these environments, we introduce several AOs named as linear Diophantine fuzzy Einstein weighted averaging (LDFEWA) operator, linear Diophantine fuzzy Einstein ordered weighted averaging (LDFEOWA) operator, linear Diophantine fuzzy Einstein weighted geometric (LDFEWG) operator, and linear Diophantine fuzzy Einstein ordered weighted geometric (LDFEOWG) operator. We investigate certain characteristics and operational laws with some illustrations. Ultimately, an innovative approach for MCDM under the linear Diophantine fuzzy information is examined by implementing suggested aggregation operators. A useful example related to a country’s national health administration (NHA) to create a fully developed postacute care (PAC) model network for the health recovery of patients suffering from cerebrovascular diseases (CVDs) is exhibited to specify the practicability and efficacy of the intended approach.

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TOPSIS, VIKOR and aggregation operators based on q-rung orthopair fuzzy soft sets and their applications

Cited by 26 publications

References 45 publications

Single-valued neutrosophic Einstein interactive aggregation operators with applications for material selection in engineering design: case study of cryogenic storage tank

Single-valued neutrosophic Einstein interactive aggregation operators with applications for material selection in engineering design: case study of cryogenic storage tank

Group Generalized q-Rung Orthopair Fuzzy Soft Sets: New Aggregation Operators and Their Applications

Linear Diophantine Fuzzy Einstein Aggregation Operators for Multi-Criteria Decision-Making Problems

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