A two-sided matching method considering the lowest value of acceptability with regret theory for probabilistic linguistic term sets

In order to solve matching problems for probabilistic linguistic information, a novel two-sided matching decision method for the probabilistic linguistic term sets (PLTSs) based on the regret theory considering the lowest value of acceptability is proposed. First, we propose a new utility function to transform the PLTSs to utility values, which can be conveniently applied to two-sided matching models. Then, to reflect the bounded rationality of expert and make the decision result close to real decision process, we put forward a novel regret-based model to obtain regret-rejoice by setting the lowest value of acceptability based on the utility function. Furthermore, we presented a new type of two-sided matching method considering constraint condition based on the lowest value of acceptability. Finally, we apply our method to a real case and make comparisons with two traditional two-sided methods.