A two-sided matching decision-making approach based on prospect theory under the probabilistic linguistic environment - Jia, Xiang; Wang, Xin-Fan; Wang, Ying-Ming; Zhou, Lang (Soft computing (Berlin, Germany), 2022)
This paper aims to propose a two-sided matching decision-making approach with the probabilistic linguistic evaluations. Existing two-sided matching decision-making methods rarely consider the psychological behaviors of the subjects, which makes the matching result deviate from the reality. To overcome this drawback, we introduce the prospect theory to describe the perception of the subjects. At first, the probabilistic linguistic evaluations are normalized. Then, the evaluations under the cost criteria are transformed into their benefit types to guarantee the consistency of the computation process. Thereafter, calculate the prospect values for the subjects based on the defined relative normalized Lance distance of the probabilistic linguistic term sets. Afterwards, aggregate the prospect values into the satisfaction degrees. On the basis of this, build the multi-objective two-sided matching decision-making model and further transform it into the single-objective model. To solve the latter is to obtain the optimal matching result. An illustrative example of intelligent technology transfer is presented to validate the proposed approach, and the optimal matching result is obtained for the demanders and the providers. Finally, we demonstrate the advantages of the proposed approach comparing with the existing methods.