Uncertainty Analysis in Group Decisions through Interval Ordinal Priority Approach

In multiple criteria decision-making (MCDM) problems, ranking alternatives is usually the end. However, in real life, it is rarely an end in itself and serves as a means to an end. Further, in real-life problems, the selection of alternatives is usually made with the aid of unique experts, and thus minimizing the influence of the uncertainty of their subjective judgements on the optimality of the decisions is a critical issue. The current study proposes a novel Interval Ordinal Priority Approach to objectively solve these and other issues by allowing uncertainty analysis and quantification. The study argues that when the model’s input contains uncertainty (even if represented by crisp numbers), expecting the output to be free from uncertainty is an unrealistic conjecture. Therefore, unlike the conventional MCDM models producing crisp weights, the proposed approach yields interval weights with the length of the interval representing the uncertainty (inconsistency among the experts’ judgements). Also, instead of resorting to the subjective measurement of thresholds to qualify or disqualify a set of inputs based on the degree of uncertainty, a novel objective measure of threshold is put forward. The validity of the proposed method is demonstrated through illustrative examples and comparative analysis. Later, the study is concluded with the implications for real-world decision-making.