Configuration Resilience of Emergency Medical Rescue Networks Under Coupled Disruptions Induced by Extreme Disasters: An Active-Learning-Assisted Optimization Approach

This study investigates configuration resilience in emergency medical rescue networks under disaster-induced compound disruptions. Major disasters can simultaneously intensify casualty severity, disrupt road networks, restrict response access, and increase hospital surge pressure, turning emergency preparedness into a location–capacity–transfer planning problem under uncertainty. We formulate a two-stage scenario-based mixed-integer programming model that determines the locations and capacity levels of temporary rescue sites and emergency medical facilities and then optimizes scenario-adaptive casualty transfers among affected areas, rescue sites, emergency facilities, and hospitals. The model considers 81 compound disruption scenarios defined by casualty severity, road-network disruption, response-access constraints, and hospital surge pressure. To solve large-scale instances, we develop a small-sample active-learning-assisted variable neighborhood search algorithm (AL-VNS), which combines a committee random forest surrogate with a progressive active-verification strategy to prioritize limited exact CPLEX evaluations, while keeping all accepted and reported solutions exactly evaluated. Numerical experiments show that AL-VNS achieves near-optimal performance in small- and medium-scale instances. Based on three independent runs for each large-scale instance, AL-VNS reduces average runtime by 34.71–63.73% relative to baseline variable neighborhood search (BVNS), while maintaining average objective-value differences of only 0.01–0.12% and requiring approximately 217 exact evaluations per instance. In the Ya’an case and the tested sensitivity settings, temporary rescue sites provide a relatively stable spatial triage-and-transfer backbone, whereas emergency medical facilities offer flexible surge capacity for relieving hospital pressure. The findings support resilience-oriented location and capacity planning for emergency medical rescue networks under uncertain compound disruptions.