How to match rescue teams to disaster zones under uncertainty? An evidential two-sided matching framework

In emergency situations, achieving efficient matching between the needs of disaster zones and the professional skills of rescue teams is a critical link to enhance rescue efficiency. This study regards the dispatching of rescue personnel as a two-sided matching decision-making process. Based on the uncertainty of the decision-making environment, a new two-sided matching framework is developed. Specifically, an improved evidential linguistic term set is used to express the evaluative information of decision-makers, which more accurately represents real-world conditions and reduces computational redundancy. To address the classical Zadeh paradox problem, a weighted average method based on minimising deviation is proposed to solve the fusion of conflicting evidence. Additionally, the ORESTE method based on the evidential linguistic term set is used to solve for the satisfaction degree of both matching parties, overcoming the problem of insufficient information in Besson ranks. Finally, a bi-objective programming model aimed at maximising overall matching satisfaction is formulated and solved. To validate the effectiveness of the proposed method, the 2025 Tibet earthquake in China is taken as a case study to demonstrate how to achieve accurate matching between disaster zones and rescue teams, offering a solution to the dispatching problem of rescue teams.