A distance-based decision model in interval-valued hesitant fuzzy setting for industrial selection problems

In a classical group decision-making (gdm) analysis, the ratings of potential alternatives and the weights of criteria or decision makers (dms) are known precisely. however, for dealing with uncertain situations, the dms can dene their opinions in linguistic variables based on fuzzy sets in industrial selection problems. in this respect, an interval-valued hesitant fuzzy set (ivhfs) is the suitable and capable theory that could help the dms with assigning some interval-valued membership degrees to a candidate or option under a set. this paper introduces a novel interval-valued hesitant fuzzy distance-based group decision (ivhf-dbgd) model by a group of dms, in which the best potential alternative can be appraised and selected among the conicting criteria. in the proposed ivhf-dbgd model, the weight of each criterion is determined by extended ivhf-entropy method along with the dms' opinions about the criteria's weights. also, the weight of each dm is computed by a new ivhf-order preference method with the relative closeness. moreover, this paper introduces a new ivhf-collective index to discriminate among potential alternatives in the selection process. finally, the computational results with a robot selection from the literature indicate that the proposed ivhf-dbgd model is the suitable group decision-making tool for the industrial selection problems.