In responses to large-scale emergencies, emergency rescuers often face inadequate professional competence and critical personnel shortages caused by decentralized management and insufficient specialized training, which compromise self-protection and rescue performance. The current literature largely treats training and dispatching as isolated processes, overemphasizes personnel allocation while underrating training evaluation, and commonly assumes sufficient qualified rescuers, thus failing to resolve capability gaps and multi-scenario shortages. To bridge these research gaps, this paper develops a multi-scenario integrated approach for emergency rescuer training and dispatching with knowledge accumulation. The methodology integrates centralized pre-dispatch training and dynamic multi-scenario dispatching, establishes a training evaluation model based on knowledge accumulation and capability utility functions, adopts time-dependent task penalty variables to assess shortage impacts, and employs the SEVIR model for emergency medical demand prediction. A multi-objective optimization model is formulated and solved by particle swarm optimization (PSO) and the greedy algorithm for comparison. The contributions are threefold: (1) proposing a training–dispatching integration framework to break traditional separation; (2) realizing quantifiable training evaluation via knowledge accumulation; (3) validating the approach through emergency medical missions, showing that PSO achieves lower penalties and higher utility. This integrated method effectively boosts rescue capacity, mitigates shortage risks, and improves emergency response efficiency.
Wang et al. (Mon,) studied this question.