Consensus driven preference analysis model for incomplete group decision making with the multiple criteria hierarchy process. (March 2023) - Qin, Jindong; Liang, Yingying
Highlights: Establish a consensus driven preference analysis model for incomplete GDM. Define a novel group consensus measurement based on norm theory. Propose a filling method for incomplete linguistic assessments. Provide a comparison method for GDM information based on probability theory. Validate the model through case study of sustainable supplier selection. Abstract: Most real-life group decision making issues involve incomplete assessments under hierarchical criteria due to limited knowledge and experience of decision makers, which entail diverse challenges including information filling, consensus reaching and preference fusion. To cope with them, an incomplete information-filling method is first presented based on the objective data and subjective willing. Then to improve the flexibility and compatibility, a novel group consensus measurement is defined based on the norm theory, which considers the influence of label preference ignored by the distance-based consensus measurements. Finally, differently from the existing group decision making methods that infer a consensus result through aggregating all individual preferences, this study develops a consensus driven preference analysis framework to derive the preference relationships among alternatives and further obtain the final ranking result using the representative value function. An application example of a sustainable supplier selection is carried out to illustrate the proposed methodology, and sensitivity and comparative analyses are performed to exhibit its flexibility and effectiveness.