Collaborative emergency decision-making for public health events: An integrated BWM-TODIM approach with multi-granularity extended probabilistic linguistic term sets

Abstract

Carrying out emergency rescue in coordination with multiple organizations, is of great significance for the effectiveness and timeliness of emergency response. Therefore, this study aims to extend a novel practical tool for distinguishing the optimal combination of different emergency plans in multiple organizations. In a multi-granularity extended probabilistic linguistic term sets (MGEPLTSs) environment, we propose a new collaborative emergency decision-making (CEDM) approach in the inspiration of the best–worst method (BWM) and TOmada de Decisão Iterativa Multicritério (TODIM) method. Firstly, a combined multi-granularity and extended probabilistic linguistic term sets, namely MGEPLTSs, are proposed to quantify the preferences given by decision makers (DMs) to address the issues on potential ambiguity and uncertainty in actual CEDM. Then, the BWM is introduced to the MGEPLTSs environment to compute the index weights of the individual and collaborative performance evaluation for multi-plan combinations, by building the fuzzy mathematical programming model respectively. Finally, we develop the multi-granularity extended probabilistic linguistic TODIM method to calculate the overall dominance of indexes considering the psychological behavior of DMs, thereby achieving the ranking of multi-plan combinations. A CEDM case on COVID-19 epidemic is used to illustrate the feasibility of the proposed approach, and the sensitivity analysis and comparative analysis with other similar approaches are presented to demonstrate its effectiveness and superiority.