Vector-based uncertain ordered density weighted averaging: a family of incentive-oriented aggregation operators

Incentive is a common phenomenon in the process of decision management. It is important and necessary to integrate incentive requirement into the decision-making process. To this problem, the paper proposed a new family of aggregation operators, denoted the vector-based uncertain ordered density weighted averaging (V-UODWA) operators. The proposed V-UODWA operators first classify the attributes performances into different clusters by considering their development level and trend. Then, the attributes performances are aggregated by the way of local clusters. In this process, it is found the V-UODWA operator can realize the reward or punishment effect by setting appropriate decision-maker’s attitude measurement and V-UODWA weights. The main properties of the V-UODWA operator are discussed, including commutativity, idempotency, boundness, and monotonicity under conditions. The application and the primary characteristics of the V-UODWA operators are illustrated by numerical examples and comparison analysis. The main contribution of the research is that the proposed V-UODWA operators have the effect of incentive management, especially when considering the importance of the attributes, which can be used to guide the sustainable development of alternatives. In addition, the attributes performances are aggregated in the form of vectors, which can greatly improve the aggregation efficiency. The proposed V-UODWA operators have a good application prospect in the fields of incentive management and behavior guidance of organizational personnel.