Multi-criteria Decision-making Algorithm Based on Linear Diophantine Fuzzy Aggregation Operators
Muhammad Riaz and Hafiz Muhammad Athar Farid
Due to lack of relevant information in many real-life circumstances, it is not acceptable for experts to exactly quantify their judgments with a crisp number. We investigate the aggregation approaches for “linear Diophantine fuzzy numbers” (LDFNs) using Frank operations. We first extend the Frank t-conorm and t-norm (FTcTn ) to linear Diophantine fuzzy environments and offer numerous new LDFN operations, we develop several new linear Diophantine fuzzy aggregation operators (AOs), including the “linear Diophantine fuzzy Frank weighted averaging (LDFFWA) operator and the linear Diophantine fuzzy Frank weighted geometric (LDFFWG) operator”. We also exhibit several properties of these AOs and investigate their relationships. The FTcTn has two major benefits. They work similarly to algebraic, Hamacher and Einstein t-conorms and t-norms (TcTn ). Second, they include a feature that leads to a more adaptive and accurate aggregation process, making them more successful than other broad TcTn techniques. We also employ these operators to provide a strategy for dealing with “multi-criteria decision-making” (MCDM) with LDFNs. Furthermore, using the suggested AOs, a quantitative test case of the new carnivorous (NCOVID19) issue is presented as an implementation for expedient decision-making. The goal of this numerical model is to illustrate the usefulness and feasibility of the AOs provided.
Keywords: Aggregation operators, COVID-19, Frank operations, emergency decision support system