Multi-expert multi-criteria decision making based on the likelihoods of interval type-2 trapezoidal fuzzy preference relations

Interval type-2 trapezoidal fuzzy sets, as a particular form of interval type-2 fuzzy sets, can precisely characterize the subjective assessments and qualitative evaluations of a group of experts. In this paper, a novel likelihood-based interval type-2 trapezoidal fuzzy multi-expert multi-criteria decision-making approach is proposed. To do so, the concepts of likelihood-based performance index, likelihood-based comprehensive evaluation value, and signed distance-based evaluation value are adopted. The interval type-2 trapezoidal fuzzy Bonferroni aggregation operator is utilized to construct the likelihood-based interval type-2 trapezoidal fuzzy preference relations. Then, the consistent lower and upper likelihoods are adopted to enhance the efficiency of the group decision making framework. The proposed multi-expert decision making approach works well when there is high degree of fluctuations in the number of criteria and experts. The practicability and feasibility of the proposed approach are validated by applications to four cases. Several comparative analyses are conducted to authenticate the dominancy of the proposed method over conventional interval type-2 trapezoidal fuzzy multi-criteria decision-making approaches.