Sequential Traffic Flow Optimization with Tactical Flight Control Heuristics

A sequential optimization method is applied to manage air traffic flow under uncertainty in airspace capacity and demand. To support its testing, a decision support system is developed by integrating a deterministic integer programming model for assigning delays to aircraft under en route capacity constraints with a fast-time simulation environment to reactively account for system uncertainties. To reduce computational complexity, the model assigns only departure controls, and a tactical control loop consisting of a shortest-path routing algorithm and an airbornedelay algorithm refines the strategic plan to keep flights from deviating into capacity-constrained airspace. This integrated approach was used to conduct 32 six-hour fast-time simulation experiments to explore variations in the number and severity of departure controls, tactical reroutes, and airborne-delay controls. These experiments highlighted a fundamental difference in the manner in which weather translation is performed at the strategic and tactical levels. Although tactical weather translation explicitly accounts for the direct impact of weather on individual flights, strategic weather translation, which is accomplished by calculating a reduced capacity fora region of airspace such as a sector, typically fails to account for these flow-based impacts. Finally, an initial validation of the rerouting algorithm, which was the dominant control strategy for avoiding en route weather in the experiments, indicates that the algorithm is able to generate reroutes that are, on average, shorter than operationally flown weather-avoidance routes, when regions of airspace that flights are likely to avoid are identified using the convective-weather-avoidance model.