Robust airline schedule design in a dynamic scheduling environment

In the past decade, major airlines in the US have moved from banked hub‐and‐spoke operations to de‐banked hub‐and‐spoke operations in order to lower operating costs. In Jiang and Barnhart (2009) [1], it is shown that dynamic airline scheduling, an approach that makes minor adjustments to flight schedules in the booking period by re‐fleeting and re‐timing flight legs, can significantly improve utilization of capacity and hence increase profit. In this paper, we develop robust schedule design models and algorithms to generate schedules that facilitate the application of dynamic scheduling in de‐banked hub‐and‐spoke operations. Such schedule design approaches are robust in the sense that the schedules produced can more easily be manipulated in response to demand variability when embedded in a dynamic scheduling environment. In our robust schedule design model, we maximize the number of potentially connecting itineraries weighted by their respective revenues. We provide two equivalent formulations of the robust schedule design model and develop a decomposition‐based solution approach involving a variable reduction technique and a variant of column generation. We demonstrate, through experiments using data from a major U.S. airline that the schedule generated can improve profitability when dynamic scheduling is applied. It is also observed that the greater the demand variability, the more profit our robust schedules achieve when compared to existing ones.