Novel MCGDM with q-rung orthopair fuzzy soft sets and TOPSIS approach under q-Rung orthopair fuzzy soft topology

Hamid, Muhammad Tahir; Riaz, Muhammad; Afzal, Deeba

doi:10.3233/jifs-192195

Cited by 23 publications

(11 citation statements)

References 51 publications

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“…Recently, q-rung orthopair fuzzy SSs (q-ROFSSs) and q-ROFSSs based MCDM methods are proposed by Riaz et al [19]. The q-ROFSS is a classical settings of q-ROFS to deal with uncertainties in the framework and descriptions of parameters in contrast of reliable and un-reliable informations in the larger space [38].…”

Section: Introductionmentioning

confidence: 99%

Aggregation and Interaction Aggregation Soft Operators on Interval-Valued q-Rung Orthopair Fuzzy Soft Environment and Application in Automation Company Evaluation

et al. 2022

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In several practical decision procedures, it is not accessible to denote assessments by a single crisp number due to a lack of information. However, representing information by an interval number within [0, 1] is a more credible approach. In multi-criteria decision making (MCDM) such an interval number can significantly catch information. In addition, the combination of soft sets with intervalvalued q-rung orthopair fuzzy sets can be viewed as interval-valued q-rung orthopair fuzzy soft sets (IVq-ROFSs). It can be a reliable tool to cope with uncertainties. Usually, aggregation operators are functional in MCDM techniques; therefore, aggregation operators on IVq-ROFSs can significantly aggregate pieces of information in intervals with IVq-ROFSs. In this paper, we investigated some crucial properties of interval valued q-rung orthopair fuzzy soft sets (IVq-ROFSSs) and expressed a different representation of IVq-ROFSS in the form of IVq-ROFS number. Based on this representation, we investigated IVq-ROF weighted averaging, IVq-ROF weighted geometric operators and given their basic properties. Moreover, we consider interactions between non-memberships and memberships of different interval-valued q-rung orthopair fuzzy values and defined IVq-ROF weighted interaction averaging, IVq-ROF weighted interaction geometric aggregation operators in IVq-ROFS environments. A decision-making process is given, and an illustration is provided by tackling application in automation company evaluation.

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Section: Introductionmentioning

confidence: 99%

Aggregation and Interaction Aggregation Soft Operators on Interval-Valued q-Rung Orthopair Fuzzy Soft Environment and Application in Automation Company Evaluation

et al. 2022

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“…For instance, Hussain et al [12] proposed the q-rung orthopair fuzzy soft aggregation operators and discussed their multi-criteria decision-making applications. The q-ROFS model was found to be more effective when extended to range of parameterizations and used in different domains [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Since then, a host of research was published based on different aspects of FSSs and IFSSs and these sets were effectively used in decision-making for real-life problems [22][23][24]. Recently, Hamid et al [13] proposed a new model called q-rung orthopair fuzzy soft sets (q-ROFSSs) by combining q-ROFSs with SSs. [2], PFSs [3], FFSs [11], and q-ROFSs [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel MADM Framework under q-Rung Orthopair Fuzzy Bipolar Soft Sets

et al. 2021

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In many real-life problems, decision-making is reckoned as a powerful tool to manipulate the data involving imprecise and vague information. To fix the mathematical problems containing more generalized datasets, an emerging model called q-rung orthopair fuzzy soft sets offers a comprehensive framework for a number of multi-attribute decision-making (MADM) situations but this model is not capable to deal effectively with situations having bipolar soft data. In this research study, a novel hybrid model under the name of q-rung orthopair fuzzy bipolar soft set (q-ROFBSS, henceforth), an efficient bipolar soft generalization of q-rung orthopair fuzzy set model, is introduced and illustrated by an example. The proposed model is successfully tested for several significant operations like subset, complement, extended union and intersection, restricted union and intersection, the ‘AND’ operation and the ‘OR’ operation. The De Morgan’s laws are also verified for q-ROFBSSs regarding above-mentioned operations. Ultimately, two applications are investigated by using the proposed framework. In first real-life application, the selection of land for cropping the carrots and the lettuces is studied, while in second practical application, the selection of an eligible student for a scholarship is discussed. At last, a comparison of the initiated model with certain existing models, including Pythagorean and Fermatean fuzzy bipolar soft set models is provided.

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“…An extended form of fuzzy soft sets, known as intuitionistic fuzzy soft sets (IFSSs), was initiated by Maji et al [47]. 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].…”

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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Novel MCGDM with q-rung orthopair fuzzy soft sets and TOPSIS approach under q-Rung orthopair fuzzy soft topology

Cited by 23 publications

References 51 publications

Aggregation and Interaction Aggregation Soft Operators on Interval-Valued q-Rung Orthopair Fuzzy Soft Environment and Application in Automation Company Evaluation

Aggregation and Interaction Aggregation Soft Operators on Interval-Valued q-Rung Orthopair Fuzzy Soft Environment and Application in Automation Company Evaluation

A Novel MADM Framework under q-Rung Orthopair Fuzzy Bipolar Soft Sets

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

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