CASUALTY EVACUATION OPTIMIZATION IN A CONFLICTED ENVIRONMENT

Abstract

Servicemembers who are injured, particularly in combat, often require rapid evacuation and transport through contested environments. Using unmanned autonomous vehicles (UAV) may help reduce the personnel required to move patients to points of care, thereby reducing the potential for further casualties. However, the UAV and the original patient may still be subject to detection by enemy agents in the area. Safely transporting a casualty in as little time as possible greatly improves survivability. Current treatment of the problem of moving casualties involves manned medical evacuation (MEDEVAC) missions, often with armed escorts. Autonomous evacuation will likely involve simple shortest path solutions to move from one point to another; however, this will not help protect from adversaries. Our model uses network flow optimization to best determine a safe path for autonomous casualty evacuation to follow, while avoiding adversaries and their attacks, and delivering a patient in a timely fashion. This model synchronizes departure and travel times of two echelons of vehicles to effect patient transfer for extraction to definitive care. With two scenarios, our results prove the concept of this model, successfully delivering patients with synchronized efforts, within time limits, and solving the problem in little computational time.