Fusion decision strategies for multiple criterion preferences based on three-way decision

In multi-criteria decision-making (MCDM) problems, with the increasing number of decision makers and candidate objects, some traditional decision-making approaches can no longer be applied to such scenarios, and how to fully ensure the participation of each decision maker as well as reasonably integrate the decision makers' assessment information on the objects becomes a problem. As a risk -averse decision-making method, three-way decision can be applied to MCDM to greatly reduce the decision-making risk. In this paper, combining the decision makers' criterion preferences and the three-way decision model, we propose three novel three-way MCDM models incorporating multiple criterion preferences to solve this problem. Firstly, a novel criterion preference-based decision risk loss measure function is proposed, and a new way of fusing multiple decision risk loss functions is established. Next, the description of the objects is obtained by the affinity propagation clustering algorithm, and three criterion preferences aggregation strategies (optimistic, compromise and pessimistic) are proposed to cope with the demand for group in three scenarios (two extremes and a moderate), respectively, then three conditional probability estimation methods are established. Subsequently, three three-way MCDM models are built to obtain the ranking results of all candidate objects in different group demand scenarios. In addition, three ranking performance testing indices are introduced to evaluate the reasonableness of the ranking results. Finally, a case application, comparative experiments, data set experimental analysis, statistical test analysis and comprehensive discussion are presented to verify the rationality, effectiveness and superiority of the proposed methods.