Air asset to mission assignment for dynamic high-threat environments in real-time

Abstract

This thesis develops pre-processing algorithms and a mixed integer programming model that solves the route selection and asset-to-mission assignment problem in the presence of threat air-defense systems. Our model and algorithms reduce the planning timeline and coordination burden by handling the heavy computational aspects of the planning process. It takes as input current aircraft, target, and threat information and produces asset-to-mission pairing recommendations that accomplish the mission while providing routes and coordination to reduce the risk from threats by avoiding surface-to-air threats, when possible, or by adding suppression assets, if available. The resulting recommendations are created significantly faster and more reliably than can be done by existing integrated fires methods.