Emergency Drone Deployment and Disposable Defibrillator Allocation: A Modular Capacitated Maximum Covering Location Model

Problem Definition. Rapid medical response is critical for out-of-hospital cardiac arrest (OHCA) cases. Using drones to deliver Automated External Defibrillators (AEDs) can significantly enhance the chances of survival by reducing delivery time. This paper aims to optimize the strategic deployment of drones and disposable AEDs within a budget-constrained Emergency Medical Services (EMS) system, using incomplete OHCA data. Unlike previous research, we focus on maximizing the number of timely AED deliveries within a critical window, rather than improving average or tail delivery time metrics. Methodology/Results. We frame this problem as a Modular Capacitated Maximal Covering Location Problem (MC-MCLP), incorporating a time constraint for AED delivery within a narrow therapeutic window. Our model can help alleviate resource imbalances across diverse service regions. We address demand variability using a distributionally robust optimization approach, which enhances decision resilience amid real-world uncertainties. Extensive testing reveals the impact of key parameters on the model, highlighting trade-offs between operational efficiency and both reliability and fairness. A case study of OHCA incidents in Virginia Beach demonstrates our model’s effectiveness in significantly increasing the number of patients reached within the critical time period. Managerial Implications. Our framework ensures prompt assistance to OHCA cases within the vital intervention window while promoting equitable resource allocation across regions. This approach addresses the primary challenges in EMS planning by improving response times within the crucial timeframe and establishing backup emergency resources. Our proposed methodology will enhance OHCA survival rates and optimize EMS resource distribution.