Managing flexible linguistic expression and ordinal classification-based consensus in large-scale multi-attribute group decision making

The consensus reaching process (CRP) is often utilized to mitigate conflicts among decision makers and help them reach an agreement. Almost all existing CRP models focused on obtaining a consensual complete ranking of alternatives. However, in many situations, it may be sufficient to classify alternatives into a number of ordinal classes rather than a complete ranking. To this end, this study proposes an ordinal classification-based CRP (OCCRP) with the aim to generate consensual ordinal classes of alternatives. Further, this study investigates the OCCRP in the large-scale multi-attribute group decision making context, in which flexible linguistic expression is used to model uncertain opinions of decision makers. In the OCCRP, an approach is proposed to manage flexible linguistic expression by transforming it into linguistic distribution assessment. Then, an opinion clustering and aggregation approach is used to deal with the assessment information of the large group. Next, an approach-based technique for order performance by similarity to ideal solution is used to obtain the ordinal classification of alternatives. Following this, a feedback adjustment mechanism is presented to help decision makers improve their consensus level on the ordinal classification of alternatives. Finally, a numerical example about grape wine grading problem and a comparative analysis are presented to demonstrate the validity of the proposal.