Linear diophantine multi-fuzzy aggregation operators and its application in digital transformation

Abstract

Digital transformation is the significant phenomena in contemporary global environment. All the conventional fuzzy sets are extended by the Linear Diophantine Fuzzy Set (LDFS). LDFS is the most viable adaptable method for decision makers to choose their grade values as it includes reference parameters. The foremost vision is to promote the resilient integration of Linear Diophantine Multi-Fuzzy Set (LDMFS) as a model for constructing decisions in order to identify the appropriate standards for digital transformation. Aggregation Operators are crucial in fuzzy systems for fusing information. To aggregate the LDMF, a number of operators have been devised, such as the Linear Diophantine Multi-Fuzzy Weighted Geometric Operator (LDMFWGO), Linear Diophantine Multi-Fuzzy Ordered Weighted Geometric Operator (LDMFOWGO), Linear Diophantine Multi-Fuzzy Weighted Averaging Operator (LDMFWGO) and Linear Diophantine Multi-Fuzzy Ordered Weighted Averaging Operator (LDMFOWAO). By integrating preferred aggregating operations, a novel method for MCDM with LDMF data is studied. The best option from the current alternatives can be determined using these operators. Moreover, a comparison of LDMF operators is made. Additionally, the idea of a scoring function for LDF is designed to examine the rank of viable alternaties. Additionally, a novel approach to solving LDMF sets is suggested. The annals on organisational digital transformation is presented as the final section to test the supremacy of the theory. Accurate rankings for digital transformation are provided by the outcome. To exhibit the robustness of the MCDM methodology, a prompt comparative analysis is established between the suggested concept and the currently used approaches.