摘要：Emergency decision making and disposal are significant challenges faced by the international commu-nity. To minimize the emergency casualties, and reduce probable secondary disasters, it is necessary to immediately dispatch rescuers for emergency rescue in calamity prone areas. The abruptness, destructiveness, and uncertainty of emergencies, the rescue team often faces challenges of pressing time, scattered calamity locations, and diverse tasks. This necessitates the effective organization of rescuers, for their swift dispatch to the areas requiring rescue. A valuable research problem is how to group and dispatch rescuers reasonably and effectively according to the actual needs of the emergency rescue task and the situation to achieve the best rescue effect. This study establishes a dispatch model for rescuers in multiple disaster areas and rescue points. First, this paper combines the Dempster- Shafer theory (DST) and linguistic term set, to propose the concept of an evidential linguistic term set (ELTS), that can flexibly and accurately describe the subjective judgment of emergency decision -makers. It not only lays a theoretical foundation for establishing the rescuers' dispatch model, but also aids in expressing information in uncertain linguistic environments of decision-making and evaluation. Second, to determine the weight of ability-based rescuer evaluation criteria, this study adopted the evidential best-worst method, combining it with DST to compensate for the limitations of the traditional weightage calculation method in expressing uncertainty. Third, to effectively dispatch rescuers to multiple disaster areas, modeling is carried out based on the above methods to maximize the competence of rescuers and the satisfaction of rescue time, and the best scheme for the allocation of rescuers is determined by solving the model. Finally, the advantages of the constructed model in emergency multitasking group decision-making are demonstrated through an empirical analysis. (c) 2022 Elsevier B.V. All rights reserved.
原文刊载于：KNOWLEDGE-BASED SYSTEMS ,NOV 14 2022