Maximum expert consensus models with linear cost function and aggregation operators

• In this paper, we develop the maximum expert consensus models with aggregation operators.
• We incorporate OWA operators into the minimum cost consensus models.
• All the consensus models in this paper are equivalently transformed into mixed 0–1 linear programming problems.

In group decision making problems, consensus is a very important issue for the aggregation of individual opinions. Based on the concept of maximum expert consensus model (MECM), this paper incorporates aggregation operators into the MECM, and proposes a novel framework of MECM. When the aggregation operator is set to be the weighted averaging operator or the ordered weighed averaging (OWA) operator, this paper equivalently transforms the MECM into mixed 0–1 linear programming problems. Additionally, this paper also shows that the minimum cost consensus model under the OWA operator with any weights can be similarly transformed into a mixed 0–1 linear programming problem. Numerical examples and a comparison analysis are used to demonstrate the validity of the proposed model.