An integrated probabilistic linguistic multi‐criteria sorting framework for assessing city resilience

Abstract The corona virus disease 2019 (COVID‐19) pandemic has significantly impacted the normal socioeconomic development of cities. The strength of a city's resilience determines the speed of its socioeconomic recovery. However, the assessment of city resilience relies on multiple criteria, and challenges arise as decision‐makers (DMs) often use linguistic terms to express the assessment information of city resilience that may be highly conflicting. To address this, this study proposes an integrated multi‐criteria decision‐making model based on probabilistic linguistic term sets (PLTSs), which can characterize uncertainty in resilience assessment issues and support multi‐criteria sorting with highly conflicting information. First, to facilitate DMs in expressing their opinions under different criteria, PLTS is adopted to characterize DMs’ evaluations. Second, a comprehensive criterion weight determining method is proposed, integrating subjective weights from the best–worst method and objective weights from the entropy weight method. Third, regarding the highly conflicting PLTSs provided by DMs, the evidence reasoning method is introduced. Conflicts are resolved via assignment and combination rules of evidence credibility. Subsequently, the limiting profiles are constructed, and the grading of city resilience is realized based on the matching of overall values with curve thresholds. Finally, the effectiveness of the proposed framework is verified through an illustrative example, supplemented by sensitivity analysis and comparative analysis.