An integrated hesitant fuzzy linguistic model for multiple attribute group decision-making for health management center selection

Abstract

Eliciting information in multiple attribute group decision-making (MAGDM) is a key for obtaining more reliable results in decisions defined under uncertain domains. In this case, the use of a hesitant fuzzy linguistic term set (HFLTS) as a flexible model to express decision-makers’ (DMs’) qualitative and hesitant decision-making information has been successfully applied to multiple decision situations. However, in many decision situations, DMs’ and attributes’ weights are still subjectively provided. These actions may cause randomness of decision results. Therefore, to solve the two issues, this paper aims to develop a group consensus optimization model and DEA (data envelopment analysis) model to derive DMs’ weights and attributes’ weights, respectively, based on HFLTS. Hence, a new hesitant fuzzy linguistic distance measure is introduced first, and some properties are discussed. The individual consensus measure and the group consensus measure are then defined based on this hesitant fuzzy linguistic distance measure. Subsequently, a new group consensus optimization model is introduced and included in the DEA-based TODIM method for MAGDM with HFLTSs. This integrated approach is applied in health management center site selection. Finally, a sensitivity and a comparative analysis with other methods such as hesitant fuzzy linguistic VIKOR and hesitant fuzzy linguistic TOPSIS are performed to show the validity of our proposed approach.