A Method for Unbalanced Linguistic Term Sets and its Application in Group Decision Making

Abstract

In most decision-making problems where linguistic assessments are used, the assessments are assumed to be based on uniform and symmetrically distributed linguistic term sets. However, there exist decision-making problems whose assessments are unbalanced linguistic term sets. We propose a new linguistic computational model based on symbolic models. Firstly, we develop a representation model of unbalanced linguistic term sets. The values expressed by unbalanced linguistic terms are converted into a special linguistic domain, which is a basic equal-distance linguistic term set. We propose a computational model for group decision making. We then design a consensus model to reach an acceptable consensus level based on our representation model for unbalanced linguistic term sets. Finally, numerical examples are illustrated. The new method enriches the assessment of decision makers and makes computation easier.