Novel psychological perception distance measures in a multigranular linguistic term set environment

Abstract Distance measures are widely used in multi-criteria decision-making problems, but classical measures generally rely on transformed objective evaluation values and may not fully reflect decision-makers’ psychological reference points or heterogeneous linguistic expressions. To address this issue, this study proposes psychological perception distance measures in a multigranular linguistic term set environment by combining classical Euclidean and Hamming distance structures with a prospect-theory value function. The proposed measures transform evaluations relative to individual psychological reference points and then calculate perceived distances between decision-makers or alternatives. Numerical examples show that the proposed measures can produce rankings that differ from those obtained using classical Euclidean and Hamming distances when psychological reference points and multigranular linguistic expressions are considered. An improved TOPSIS procedure is further developed and applied to a hospital service-quality evaluation problem. The results illustrate that incorporating psychological expectations into distance measurement can affect alternative ranking and provide an additional perspective for decision-making under linguistic uncertainty.