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Multi-attribute decision making using q-rung orthopair fuzzy weighted fairly aggregation operators

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Abstract

In this study, in view of expressing the uncertain information more elegantly, we shall enlighten the q-rung orthopair fuzzy sets (q-ROFSs) and the q-rung orthopair fuzzy numbers (q-ROFNs) which are considered to be superior of the intuitionistic fuzzy sets and the Pythagorean fuzzy sets, respectively. Here our aim is towards the development of some new operational laws and their corresponding weighted aggregation operators under the q-rung orthopair fuzzy environment. In this regard, at the very beginning, we define some new neutral or fair operational laws that include the concept of proportional distribution to achieve a neutral or fair treatment to the membership and non-membership functions of q-ROFN. Subsequently, with these operations, we develop q-rung orthopair fuzzy weighted fairly aggregation operator (qROFWFA) and q-rung orthopair fuzzy ordered weighted fairly aggregation operator (qROFOWFA) which can neutrally or fairly serve the membership and non-membership degrees. We observe the noteworthy features of these proposed aggregation operators. Furthermore, we exercise also an MADM (multi-attribute decision-making) approach with multiple decision makers and partial weight information in the framework of q-rung orthopair fuzzy sets. At the end of this study, we provide an illustrative example to highlight the feasibility and a practical look of the approach proposed herein.

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Authors and Affiliations

  1. Faculty of Mathematics, Techno College of Engineering Agartala, Tripura, 799004, India

    Abhijit Saha & Priyanka Majumder

  2. Faculty of Basic and Applied Sciences, NIT Arunachal Pradesh, Yupia, Arunachal Pradesh, 791112, India

    Debjit Dutta

  3. Faculty of Applied Sciences, Tezpur University, Tezpur, Assam, 784028, India

    Bijoy Krishna Debnath

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  1. Abhijit Saha
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  2. Priyanka Majumder
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  4. Bijoy Krishna Debnath
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Saha, A., Majumder, P., Dutta, D. et al. Multi-attribute decision making using q-rung orthopair fuzzy weighted fairly aggregation operators. J Ambient Intell Human Comput 12, 8149–8171 (2021). https://doi.org/10.1007/s12652-020-02551-5

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