ABSTRACT
The comprehensive minimum cost consensus (CMCC) models for group decision-making (GDM) problems provide a new perspective on cost control in consensus reaching processes (CRPs). However, these models are not suitable for multi-criteria group decision-making (MCGDM) problems because they neglect the agreement on the final decision that is made according to the importance of the criteria. Therefore, this chapter aims to propose new CMCC models for MCGDM problems, in which an additional constraint is considered to guarantee the agreement on the evaluation of the alternatives under the criteria. In addition, in real-world decision-making problems, the unit adjustment cost of experts often presents uncertainty. Considering that the disturbance of uncertain data may reduce the quality of the optimal solution, this chapter uses the robust optimization (RO) method to establish an R-MC-CMCC model to provide uncertainty-stable solutions. Afterward, the implementation of the proposed framework is shown in an illustrative example related to the selection of an Internet of Things (IoT) platform. Finally, a sensitivity analysis regarding the involved parameters is provided.
