A novel approach to the tail assignment problem in airline planning

• Manuel Fuentes ^[4] ; Luis Cadarso ^[1] ; Vikrant Vaze ^[2] ; Cynthia Barnhart ^[3]
  1. [1] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     
  2. [2] Dartmouth College

     Dartmouth College

     Town of Hanover, Estados Unidos

     
  3. [3] Massachusetts Institute of Technology

     Massachusetts Institute of Technology

     City of Cambridge, Estados Unidos

     
  4. [4] Cisneria Engineering (Alcalá de Henares)

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• Localización: R-evolucionando el transporte [Recurso electrónico]: XIV Congreso de Ingeniería del Transporte. Universidad de Burgos 6, 7 y 8 de julio 2021 / Hernán Gonzalo Orden (ed. lit.), Marta Rojo Arce (ed. lit.), 2021, ISBN 978-84-18465-12-3, págs. 2309-2320
• Idioma: inglés
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• Resumen

  • Combinatorial optimization problems abound in the field of airline planning. Aircraft and passengers fly on networks made up of flights and airports. To schedule aircraft, assignments of fleet types to flights and of aircraft to routes must be determined. The former is known as the fleet assignment problem while the latter is known as the aircraft routing problem in the literature. Aircraft routing is typically addressed as a feasibility problem, the solution to which is required for the construction of crew schedules. All these issues are typically resolved 4 to 6 months before the day of operations. As a result, there is little information available about each aircraft's operational status when making such decisions. The tail assignment problem, which has received little attention in the literature, is solved when additional information about operational conditions is revealed, with the goal of determining each aircraft's route for the day of operations while accounting for the originally planned aircraft routes and crew schedules. As a result, it is a problem that must be resolved closer to the day of operations. We propose a mathematical programming approach based on sequencing that captures all operational constraints and maintenance requirements while minimizing operational costs and schedule changes relative to original plans. The computational experiments are based on realistic cases drawn from a Spanish airline with over 1000 flights and over 100 aircraft.