Data-Driven Systems Design for Last-Mile Emergency Logistics: Drone-Based AED Delivery under Uncertainty

Timely intervention is the critical determinant of survival for out-of-hospital cardiac arrest, yet many emergency medical service (EMS) systems fail to meet life-saving response-time thresholds under limited budgets and uncertain demand. Enabled by Internet-plus technologies, new emergency logistics paradigms are emerging, but their effective design remains an open challenge.

This talk presents an Internet-plus emergency logistics framework for drone-based delivery of disposable automated external defibrillators (AEDs). Shifting away from minimizing average or tail response times, the framework focuses on maximizing the number of patients reached within a critical therapeutic window, where intervention has the greatest impact. We formulate this problem as a modular capacitated maximum covering location model, jointly optimizing drone base locations and AED inventories under severe resource constraints and demand uncertainty. To ensure real-world relevance, the framework integrates distributionally robust optimization, accounts for capacity congestion, and incorporates fairness considerations to mitigate geographic inequities. A large-scale case study using real OHCA data demonstrates substantial gains in timely coverage and equity compared with response-time-based benchmarks. Beyond emergency medicine, the keynote highlights broader lessons for Internet-plus logistics, manufacturing, and service operations, emphasizing threshold-based performance, robust capacity design, and socially responsible resource allocation in digitally enabled service networks.