Airline Schedule Recovery in Flow Management: An Application for Departure Re-Routing

Abstract

This paper presents a flexible modeling methodology that is designed to assess a range of operational concepts for collaborative flow management. The particular focus is on the problem of airline schedule recovery in conditions where airspace sectors are capacity limited due to convective weather events. This model is embedded in a dynamic simulation environment, the Boeing national flow model (NFM), representing the US National Airspace System (NAS). The airline schedule recovery model is based on an optimization formulation that allows a representation of adaptive airline behavior in current and future operations. The schedule recovery options considered include ground delay, flight cancellation, and pre-departure re-routing. The paper presents simulation results based upon a convective weather scenario in the Houston TRACON area. The primary objective of this study was to illustrate, in a simple scenario, how sophisticated planning algorithms can re-plan traffic flows around a network of traffic constraints, and to quantify the potential benefits of automation-supported planning. Comparisons are made concerning the effectiveness of differing levels of automation in re-planning departure schedules using ground delays, flight cancellations, and pre-departure re-routing using published coded departure routes (CDR). An idealized convective weather disruption was designed to generate significant airspace capacity outages over a period of several hours to illustrate the difference between current and more advanced planning techniques. Although the results presented in this paper are specialized to the case involving only departures from a single airport, the methodology is sufficiently general to be applied to NAS-wide airline schedules. Additionally, the method can address both airport and airspace capacities and can account for delay associated with tail-routed schedules. The paper shows that significant delay reduction benefits can be achieved if more automation support is available to support traffic flow management during convective weather events