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Multi-attribute decision-making with (p, q)-rung orthopair fuzzy sets

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Abstract

(p, q)-rung orthopair fuzzy sets can provide more ambiguous scenarios since they can exhibit membership grades across a larger range than Fermatean fuzzy sets, Pythagorean fuzzy sets, and intuitionistic fuzzy sets. In this work, Yager t-norm and t-conorm are used to investigate the correctness of (p, q)-rung orthopair fuzzy numbers. Initially, the Yager operational laws are extended to the (p, q)-rung orthopair fuzzy data. The goal of this study paper is to provide a decision-making strategy related to the great tendencies of the conventional TOPSIS method in the context of (p, q)-rung orthopair fuzzy sets. The TOPSIS method, which employs a system to identify alternatives that acquire beneficial distances from optimal solutions, is recognized as one of the legitimate approaches to multi-attribute decision making. Novel Yager’s operational rules for (p, q)-rung orthopair fuzzy numbers have been developed utilizing Yager norms and the flexibility of (p, q)-rung orthopair fuzzy sets. To further show the viability and use of the created technique, we carry out a case study of the specific carnivorous issue and an application for emergency decision making. The following are this article’s primary contributions: (1) Yager norms have been used to study the aggregation operators for (p, q)-rung orthopair fuzzy numbers and their properties. (2) Under (p, q)-rung orthopair fuzzy sets, the TOPSIS technique is established. An algorithm that explains the suggested method step-by-step is provided. (3) The created technique is then used as a case study of a carnivorous concern. (4) The outcomes have been compared to the rankings attained using different methods that are currently in use.

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Authors and Affiliations

  1. Department of Mathematics, University of Management and Technology, Sialkot Campus, Lahore, Pakistan

    Gulfam Shahzadi & Rana Talha Ahmad

  2. Division of Science and Technology, Department of Mathematics, University of Education, Lahore, Pakistan

    Sundas Shahzadi

  3. Department of Industrial Engineering, Turkish Naval Academy, National Defence University, 34940, Tuzla, Istanbul, Turkey

    Muhammet Deveci

  4. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Muhammet Deveci

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  1. Gulfam Shahzadi
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  2. Sundas Shahzadi
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  3. Rana Talha Ahmad
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  4. Muhammet Deveci
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GS, SS, RTA and MD: concept, design, analysis, writing, or revision of the manuscript.

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Correspondence to Muhammet Deveci.

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Shahzadi, G., Shahzadi, S., Ahmad, R.T. et al. Multi-attribute decision-making with (p, q)-rung orthopair fuzzy sets. Granul. Comput. 9, 18 (2024). https://doi.org/10.1007/s41066-023-00443-5

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