solving robot selection problem by a new interval-valued hesitant fuzzy multi-attributes group decision method

‎selecting the most suitable robot among their wide range of specifications and capabilities is an important issue to perform the hazardous and repetitive jobs‎. ‎companies should take into consideration powerful group decision-making (gdm) methods to evaluate the candidates or potential robots versus the selected attributes (criteria)‎. ‎in this study‎, ‎a new gdm method is proposed by utilizing the complex proportional assessment method under interval-valued hesitant fuzzy (ivhf)-environment‎. ‎in the proposed method‎, ‎a group of experts is established to evaluate the candidates or alternatives among the conflicted attributes‎. ‎in addition‎, ‎experts assign their preferences and judgments about the rating of alternatives and the relative importance of each attribute by linguistic terms which are converted to interval-valued hesitant fuzzy elements (ivhfes)‎. ‎also‎, ‎the attributes’ weights and experts’ weights are applied in procedure of the proposed interval-valued hesitant fuzzy group decision-making (ivhf-gdm) method‎. ‎hence‎, ‎the experts’ opinions about the relative importance of each attribute are considered in determination of attributes’ weights‎. ‎thus‎, ‎we propose a hybrid maximizing deviation method under uncertainty‎. ‎finally‎, ‎an illustrative example is presented to show the feasibility of the proposed ivhf-gdm method and also the obtained ranking results are compared with a recent method from the literature‎.