A Novel Probabilistic Linguistic Two-Sided Matching Decision-Making Method Based on Peer Effect

Considering the peer relationship between subjects, a probabilistic linguistic term sets (PLTSs) two-sided matching decision-making (TSMDM) method based on peer effect is proposed. Firstly, the addition and scalar multiplication operation for PLTSs are improved and the probabilistic linguistic weighted averaging (PLWA) operator is further proposed to aggregate PLTSs. Secondly, two kinds of satisfaction functions are proposed to calculate satisfaction degrees of both sides. Furthermore, the multi-objective optimization model is established and transformed it into the single-objective model, and the satisfactory results are obtained by solving this model. Finally, an example and several comparison analysis and sensitivity analysis are presented to demonstrate the effectiveness and practicability of the proposed TSMDM method.