INCOMPLETE INTERVAL-VALUED HESITANT FUZZY PREFERENCE RELATIONS IN DECISION MAKING

In this article, we propose a method to deal with incomplete interval-valued hesitant fuzzy preference relations. For this purpose, an additive transitivity inspired technique for interval-valued hesitant fuzzy preference relations is formulated which assists in estimating missing preferences. First of all, we introduce a condition for decision makers providing incomplete information. Decision makers expressing incomplete data are expected to abide by the proposed condition. This ensures that the estimated preferences are well-defined intervals which otherwise may not be possible. Additionally, this condition eliminates the problem of outlying estimated preferences. After resolving the issue of incompleteness, this article proposes a ranking rule for reciprocal and non-reciprocal interval-valued hesitant fuzzy preference relations.