Novel Aczel-Alsina operations-based linguistic Z-number aggregation operators and their applications in multi-attribute group decision-making process

Abstract

The change from multi-attribute decision-making (MADM) to multi-attribute group decision-making (MAGDM) has led to a doubling of data volume and a trend toward multi-attribute large group decision-making. At this time, operators need to be used to integrate these data, while also reflecting the psychological impact of decision-makers. Linguistic Z-number (LZN) is widely used for evaluating fuzzy information due to its unique flexibility and accuracy. However, in the LZN environment, aggregation operators, as an important component of decision-making, also have some shortcomings. Some aggregation operators currently used in LZN environments have low flexibility, high computational complexity, and are not convenient. This article cleverly utilizes Aczel Alsina t-norm and t-norm to obtain some aggregation operators with good algebraic properties, and proves these properties, which can also reflect the psychological influence of decision-makers. On this basis, we obtained a MAGDM model and successfully applied it in the following example, comparing it with other operators and verifying its superiority.