Research on Group Decision-Making Method under Incomplete Information and Its Application in Service Quality Evaluation of Nursing Homes

With the increasing complexity of decision-making problems, group decision-making problems that consider the similarity of opinions and the lack of evaluation information among DMs have become increasingly important. An interesting phenomenon is that decision makers often fail to provide complete evaluation information. Therefore, the research focus of this article is the group decision-making method under complete information. First of all, this paper proposes a new method for calculating the similarity of decision-maker's opinions based on the Jaccard coefficient, and on this basis, a method for constructing a decision-maker's opinion similarity network is proposed. Secondly, this paper proposes a decision-maker clustering method based on Louvain algorithm. In addition, an evaluation information completion method is proposed based on the KNN interpolation method, which can complete the missing parts of the evaluation information provided by the decision-maker. Then, a new two-stage feedback adjustment mechanism is constructed to guide the consensus reaching process. Based on this, this paper proposes a group decision-making method under incomplete information based on Louvain algorithm. Finally, the effectiveness of the method proposed in this paper is verified through a case study of service quality evaluation in nursing homes that includes comparative analysis.