Group decision support model based on sequential additive complementary pairwise comparisons

In group decision support systems, it is important on how to process and manage individual decision information. In the paper, a sequential model is proposed to manage individual judgements with additively reciprocal property over paired alternatives. The process of realizing additive complementary pairwise comparisons (ACPCs) is captured. A real-time feedback mechanism is constructed to address the irrational behavior of individuals. An optimization model is established and solved by using the particle swarm optimization (PSO) algorithm, such that the consistency of individual judgements can be improved fast yet effectively. For the aggregation of individual decision information in group decision making (GDM), the weighted averaging operator is used. It is found that when all individual judgements are acceptably additively consistent, the collective matrix is with acceptable additive consistency. Under the control of individual consistency degrees, the approach of reaching consensus in GDM is further proposed. By comparing with some existing models, the observations reveal that the sequential model of originating additive complementary pairwise comparisons possesses the ability to rationally manage individual decision information.

Automated summary

The paper proposes a sequential model for managing individual decision information, including the realization of additive complementary pairwise comparisons and the establishment of a real-time feedback mechanism to address irrational behavior. The weighted averaging operator is used for aggregating individual decision information in group decision making, and a method for reaching consensus in GDM is further proposed under the control of individual consistency degrees. Comparisons with existing models show that the sequential model has the ability to rationally manage individual decision information.