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Development of Archimedean power Heronian mean operators for aggregating linguistic q-rung orthopair fuzzy information and its application to financial strategy making

  • Fuzzy systems and their mathematics
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Abstract

The linguistic \(q\)-rung orthopair fuzzy (L\(q\)-ROF) set (L\(q\)-ROFS) is an important implement in the research area in modelling vague decision information by incorporating the advantages of \(q\)-rung orthopair fuzzy sets with linguistic variables. To effectively fuse the L\(q\)-ROF information, this paper introduces a series of L\(q\)-ROF power Heronian mean operators with their weighted variants based on Archimedean operations. The defined operators meet certain important characteristics such as they can provide generality and flexibility in the aggregation process, model uncertainty of decision-making with interrelated attributes, and the influence of unreasonable attribute values can also be reduced by them. A generalized distance measure and an entropy measure were defined for L\(q\)-ROFSs, subsequently. Afterwards, a multi-attribute group decision-making approach under L\(q\)-ROF context, where the information about decision-makers’ and attribute weights might be known or unknown, is designed utilizing individual expressions of the proposed operators. The developed approach includes a TOPSIS‐based algorithm and an entropy-based algorithm in order to compute the weight of decision-makers’ and attributes, respectively. Finally, the proposed method’s feasibility is validated by solving a financial strategy-making problem, and its superiority is demonstrated via some detailed comparisons.

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Authors and Affiliations

  1. Department of Mathematics, University of Kalyani, Kalyani, 741235, India

    Nayana Deb & Animesh Biswas

  2. Department of Mathematics, Heramba Chandra College, Kolkata, 700029, India

    Arun Sarkar

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  1. Nayana Deb
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Deb, N., Sarkar, A. & Biswas, A. Development of Archimedean power Heronian mean operators for aggregating linguistic q-rung orthopair fuzzy information and its application to financial strategy making. Soft Comput 27, 11985–12020 (2023). https://doi.org/10.1007/s00500-023-08015-0

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