Stochastic network interdiction for optimizing defensive counter air operations planning

Abstract

This thesis describes a stochastic, network interdiction optimization model to guide defensive, counter-air (DCA) operations planning. We model a layered, integrated air-defense system, which consists of fighter and missile engagement zones. We extend an existing two-stage, stochastic, generalized-network interdiction model by Pan, Charlton and Morton, and adapt it to DCA operations planning. The extension allows us to handle multiple-type interdiction assets, and constrain the attacker's flight path by the maximum allowable traveled distance. The defender selects the locations to install multiple interceptor types, with uncertainty in the attacker's origin and destination, in order to minimize the probability of evasion, or the expected target value collected by the evader. Then, the attacker reveals an origin-destination pair (independent of the defender's decision), and sends a strike package along a path (through the interdicted network) that maximizes his probability of evasion. By adding a small persistence penalty we ensure the plans are consistent in presence of minor variations in the number of interceptors. We present computational results for several instances of a test case consisting of the airspace over a 360-by-360 nautical miles area. The computational time ranges from some seconds to ten minutes, which is acceptable for operational use of this model.