Probabilistic linguistic multi-criteria decision-making based on evidential reasoning and combined ranking methods considering decision-makers' psychological preferences

This study aims to develop an integrated approach for solving probabilistic linguistic multi-criteria decision-making (MCDM) problems. This study first reveals the limitations in the existing methods for probabilistic linguistic term sets (PLTSs). Subsequently, an improved aggregation method and a novel ranking method are developed for addressing PLTSs. The proposed aggregation method is based on the evidence reasoning algorithm and the proposed ranking method that integrates with a three-fold ranking method is based on optimism, neutralism and pessimism decision-making processes. Thus, the proposed approach can straightforwardly and robustly deal with probabilistic linguistic MCDM problems considering decision-makers' psychological preferences. Moreover, to flexibly obtain criteria weights, several models are constructed to adapt to different decision-making situations, in which criteria weight information is incompletely, inconsistently or completely unknown. Finally, a case study on selecting the best investment objective(s) among the member counties of One Belt, One road is conducted to validate the feasibility and effectiveness of the proposed approach, followed by a comparative analysis between the existing methods and the proposed approach.