A Two-stage subgroup Decision-making method for processing Large-scale information

Large-scale group decision-making (LSGDM) problems generally involve a large number of decision-makers (DMs). In many situations, the number of DMs and alternatives simultaneously make traditional techniques inoperable. This paper proposes a two-stage subgroup decision-making (T-SSGDM) method. In contrast to the traditional LSGDM approaches, this framework does not need a clustering process to reduce the size of DMs/ alternatives to a manageable level. Instead, we propose a T-SSGDM process to address inter-group heterogeneity. First, DMs and alternatives are randomly grouped so that the number of alternatives to be assessed by each DM is substantially reduced. Second, as the ratings obtained in the first stage of the decision-making process are incomparable, partial samples are selected for the second stage. The relationships among the ratings of different subgroups are then determined by applying the equivalence test. Additionally, to ensure the robustness of the results, three sampling methods and three kinds of functions for the equivalence test are implemented. Finally, an empirical application is used to verify the effectiveness of the proposed method.

Automated summary

This paper proposes a two-stage subgroup decision-making (T-SSGDM) method to address large-scale group decision-making (LSGDM) problems. The method reduces the number of decision-makers/alternatives through random grouping and equivalence testing, ensuring the robustness of the results.