An Optimization Approach to Airline Integrated Recovery

  • Authors:

  • Jon D. Petersen;Gustaf Sölveling;John-Paul Clarke;Ellis L. Johnson;Sergey Shebalov

  • Affiliations:

  • H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332;H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332;The Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332;H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332;Sabre Holdings Research, Southlake, Texas 76092

  • Venue:
  • Transportation Science
  • Year:
  • 2012

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Abstract

Although the airline industry has benefited from advancements made in computational and operational research methods, most implementations arise from the frictionless environment of the planning stage. Because 22% of all flights have been delayed and 3% have been cancelled in the United States since 2001, schedule perturbations are inevitable. The complexity of the operational environment is exacerbated by the need for obtaining a solution in as close to real-time as possible. Given some time horizon, the recovery process seeks to repair the flight schedule, aircraft rotations, crew schedule, and passenger itineraries in a tractable manner. Each component individually can be difficult to solve, so early research on irregular operations has studied these problems in isolation, leading to a sequential process by which the recovery process is conducted. Recent work has integrated a subset of these four components, usually abstracting from crew recovery. We present an optimization-based approach to solve the fully integrated airline recovery problem. After our solution methodology is presented, it is tested using data from an actual U.S. carrier with a dense hub-and-spoke network using a single-day horizon. It is shown that in several instances an integrated solution is delivered in a reasonable runtime. Moreover, we show the integrated approach can substantially improve the solution quality over the incumbent sequential approach. To the best of our knowledge, we are the first to present computational results on the fully integrated problem.