Scheduling Under Uncertainty: An Analysis of an Air Force Application

摘要

We investigated a new scheduling application under uncertainty: the Eglin AFB phased array radar tracks thousands of objects in space. Because of uncertainty associated with an object's location and radar cross-section, as well as the inherent power limits and weather, scheduling must trade-off maximizing the probability of detection against maximizing the total number of objects scheduled for observation; the system must also dynamically reschedule missed observations and incorporate new requests. The problem of scheduling under uncertainty, and what scheduling methods are robust under uncertainty, is not well understood. We developed and tested algorithms that can handle uncertainty during scheduling. We identified key factors that impact performance and algorithm characteristics that address it.