A Two-Sided Matching Decision Model Based on Uncertain Preference Sequences

Two-sided matching is a hot issue in the field of operation research and decision analysis. This paper reviews the typical two-sided matching models and their limitations in some specific contexts, and then puts forward a new decision model based on uncertain preference sequences. In this model, we first design a data processing method to get preference ordinal value in uncertain preference sequence, then compute the preference distance of each matching pair based on these certain preference ordinal values, set the optimal objectives as maximizing matching number and minimizing total sum of preference distances of all the matching pairs under the lowest threshold constraint of matching effect, and then solve it with branch-and-bound algorithm. Meanwhile, we take two numeral cases as examples and analyze the different matching solutions with one-norm distance, two-norm distance, and positive-infinity-norm distance, respectively. We also compare our decision model with two other approaches, and summarize their characteristics on two-sided matching.