A large-scale group decision-making method based on group-oriented rough dominance relation in scenic spot service improvement.

In today's age of big data and information, large-scale group decision-making has become an essential aspect of modern economy, science, and technology. This paper proposes a large-scale group decision-making method that leverages group-oriented rough dominance relation to identify the worst group when addressing complex issues that involve a large number of decision-makers. The proposed method entails building a set-valued ordered information system that utilizes clustering learning to reduce data dimensions and reduce the dimensions of decision space, thereby improving the efficiency of the decision-making process. Additionally, it proposes a novel group-oriented rough dominance relation based on dominance-based rough set theory. By clarifying this advantage relationship, more targeted focus is placed on the group that needs improvement, thereby improving decision-making effectiveness. The proposed method calculates the advantage degree of alternative group plans to select the worst group. The main purpose is to compare the advantages and disadvantages of different groups by defining a metric that quantifies the rough dominance relation between groups, thereby improving the objectivity and repeatability of the decision-making process. Finally, the study applies the proposed method to a case study aimed at improving various types of services in European scenic spots, and the benefits of the method are discussed. Experimental analysis shows that the method in this study can screen the groups that should be improved most in this case, showing the benefits and applicability of the proposed method, and providing valuable insights for complex decision-making problems involving multiple decision-makers. • The clustering learning is used for dimensionality reduction. • Using the group-oriented rough dominance relation for decision-making aggregation. • A new method called LSGDM is designed. [ABSTRACT FROM AUTHOR]