Collaborative Approaches to the Application of Enroute Traffic Flow Management Optimization Models

Recent research has produced stochastic optimization models for adjusting traffic flows in response to predicted congestion in the enroute airspace. These models simultaneously consider a set of options for each flight that includes both the possibility of ground delay and reroutes. They take into account a distribution of possible weather scenarios and their outputs include alternate courses of action based on weather outcomes. The direct application of such models has been challenging because they do not provide the decision making flexibility to flight operators that has now become standard for collaborative air traffic management. In this paper we propose two different recipes for incorporating collaborative features into a meta-framework for the application of one such model. This paradigm for the combined use of optimization and collaboration is specifically geared for use within airspace flow programs, which are now in widespread use within the U.S. We provide computational results that demonstrate the effectiveness of this approach.