A novel approach towards multiattribute decision making using q-rung orthopair fuzzy Dombi-Archimedean aggregation operators.

When dealing with real-life problems, the q-rung orthopair fuzzy set is a core concept because the power of the membership and non-membership degrees is less than or equal to one. The process of selecting and evaluating alternatives based on several criteria or characteristics is known as multi-attribute decision-making (MADM) problems. The overview of the attribute values is a significant problem in MADM. It must be done in an accurate, consistent, and meaningful way. -rung orthopair fuzzy numbers (-ROFNs) are more flexible and powerful for representing uncertain or fuzzy information than other fuzzy number systems such as intuitionistic fuzzy numbers or Pythagorean fuzzy numbers. The paper introduces a new operator within the q-ROF framework or environment. This operator combines the characteristics of Dombi and Archimedean operations. These two operations likely have defined rules and properties within the q-ROF environment. The paper then proceeds to propose some weighted aggregation operators (AOs) based on the Dombi and Archimedean operations under q-ROF. These weighted AOs are likely used for combining or aggregating multiple attributes or criteria in decision-making processes. The paper explores the properties of these operators, which could include aspects like monotonicity, idempotence, or other desirable mathematical properties. Furthermore, the paper emphasizes applying the proposed operators within the q-ROF environment to MADM. This suggests that operators may be used in decision-making scenarios involving multiple attributes or criteria. The paper provides a procedure or methodology for applying the proposed operators in such decision-making processes. Lastly, the paper presents a practical example related to human resource selection. It demonstrates how the suggested strategy can be employed in real-world scenarios with its decision steps and the new operator. The example aims to demonstrate the viability and efficacy of the suggested strategy in solving decision-making problems related to human resource selection.