Reducing Criteria in Multicriteria Group Decision-Making Methods Using Hierarchical Clustering Methods and Fuzzy Ontologies

Multicriteria group decision-making environments that have a high number of criterion values can be difficult for the experts to handle. This is due to the fact that the experts have to take too much information into account. Thus, they get lost among all the possibilities and have difficulties making the right decision. In order to solve this problem, we present a novel multicriteria group decision-making method that reduces the initial set of criterion values in an organized way. Hierarchical clustering methods are used in order to generate a new reduced criteria set that can be handled by the experts. Fuzzy ontologies are used as an aid system that stores how much each alternative fulfills each criterion. The presented method makes it possible for the experts to carry out the group decision-making process by focusing on ranking the reduced set of criterion values. As a result, a comfortable decision environment is generated, in which the experts can make decisions by managing a fair amount of information. The aid provided by fuzzy ontologies allows the experts to focus on establishing the importance of the criterion values, leaving the rest to the computational system.

Automated summary

This article introduces a new method for multicriteria group decision-making, which reduces the initial set of criterion values using hierarchical clustering methods and utilizes fuzzy ontologies as an aid system. This method allows experts to focus on ranking the reduced set of criterion values and manage a fair amount of information in the decision-making process.