Selection of high-arm fire trucks for urban emergency preparedness based on evidential linguistic CRITIC-BWM approach

Abstract

This study discusses the selection problem of high-arm fire trucks in urban emergency preparedness and proposes a multi-criteria decision-making (MCDM) model based on the Criteria importance through intercriteria correlation and best worst method (CRITIC-BWM) methods guided by the evidence linguistic term sets (ELTS). The model aims to help fire departments select appropriate high-arm fire trucks to deal with high-rise building fires and improve the city’s fire emergency response capabilities. The MCDM model handles the linguistic preference problem by combining the evidence linguistic term sets and uses the CRITIC-BWM combined weighting method to determine the weight of the decision criteria, thereby reducing subjective bias while comprehensively considering multiple criteria. The effectiveness of the model is verified through specific case analysis. The research results show that the model can not only effectively solve the selection problem of high-arm fire trucks, but also provide guidance for the future performance optimization of high-arm fire trucks. Nevertheless, there are still some limitations in this study, such as the evidence linguistic term sets method needs to be further improved and the universality of the model needs to be verified in more fields. Future research will continue to optimize the model, expand its scope of application, and further verify its reliability and effectiveness in actual decision-making.