A multi-criteria group decision-making method based on OWA aggregation operator and Z-numbers.

Decision making is a universal behavior based on human cognitive information. In the real world, information related to human cognition is characterized by uncertainty and partial reliability, which is challenging to express with traditional and precise concepts. To better describe this type of uncertain information, Z-number is introduced by Zadeh, which contains uncertain information with both probability and fuzziness. Recently, Yager proposed the fusion of multiple multi-criteria aggregate functions, especially the fusion of multiple OWA-type aggregate functions, to deal with the group decision problem. However, such aggregation functions exhibit limitations in facing the uncertainty of expert information in real-world decision-making scenarios. To overcome this shortcoming, this paper extends Yager's aggregation method to the Z-number field and further propose a new group decision-making method based on the OWA aggregation operator and Z-number. The maximum entropy optimization model based on a genetic algorithm is used to determine the hidden probability distribution in Z-numbers to aggregate the Z-numbers, which solves the problem of information loss caused by ignoring the hidden probability distribution in the existing Z-number aggregation methods, and greatly preserves the original meaning of Z-number. Some numerical examples are used to demonstrate the rationality and effectiveness of the proposed method. Finally, a comparative analysis with existing methods expounds on the superiority of the proposed method. [ABSTRACT FROM AUTHOR]