Large group decision-making considering multiple classifications for participators: a method based on preference information on multiple elements of alternatives

Abstract

Abstract In large group decision-making, participators with different knowledge structures, backgrounds, and other characteristics are unlikely to accurately evaluate alternatives. For this, it is necessary to decompose alternatives into several elements, and consider the participators’ preferences for elements of alternatives and the multiple classifications for participators according to their characteristics. However, related studies are still scarce. The objective of this paper is to propose a multi-elemental large group decision-making method, in which the desirable alternative(s) are selected from a set of feasible alternatives according to the preference information on multiple elements of alternatives provided by participators from multiple subgroups, and multiple classifications for participators are considered. In the method, according to the strict preference ordering of elements provided by participators, the percentage distributions on preferences of each subgroup concerning each element are firstly presented under each classification for participators. Secondly, the decision weight of each subgroup is determined by three factors, i.e., the consensus of preferences provided by each subgroup, the organizer’s preference for each subgroup, and the number of participators in each subgroup. Then, the comprehensive preference concerning each element is determined by combing the preference information from multiple subgroups and the decision weights of multiple subgroups, the overall preference vector can be obtained under each classification, and the virtual alternatives are determined by normalizing the overall preference vector. Further, considering multiple classifications for participators, the overall dominant degrees of alternatives can be obtained by calculating the similarity degrees between each virtual alternative and each alternative, thus the ranking order of alternatives can be obtained based on the overall dominant degrees of alternatives. Finally, an example is given to confirm the feasibility of the proposed method. The results of the sensitivity and comparative analyses show that the proposed method is applicable and effective. The proposed method can further enrich and improve the theory and approach of large group decision-making with multiple elements considering multiple classifications for participators.

Keywords

• Multi-elemental large group decision-making
• Classification
• Element
• Virtual alternative
• Weight
• Ranking