Potential of integrated flight scheduling and rotation planning considering aerodynamic-, engine- and mass-related aircraft deterioration

  • M. Lindner
  • J. Rosenow
  • S. Förster
  • H. Fricke
Original Paper


Aircraft are subject to steady deterioration effects in flight operations resulting in increased fuel consumption due to decreased engine efficiency, increased airframe surface roughness and increased operating empty mass. These effects are taken into account in operational flight planning (e.g. fuel calculations) but long-term efficiency loss is rarely respected in flight and rotation scheduling. In most cases, each aircraft type per fleet series is treated homogeneously and scheduled considering maintenance events, equipment and other restrictions. This work investigates fuel saving potentials by additionally considering the aircraft tail sign-specific efficiency during aircraft to flight assignment. For this purpose, we calculate additional fuel consumption caused by deterioration effects, expressed as performance factor and mass deviation. By using an integrated flight scheduling and aircraft rotation model, aircraft will be reassigned to flights from given flight schedules by minimizing operating costs or fuel consumption. Our research yields in a saving of 22,400 kg fuel for 15 long-haul aircraft (150 kg per reassigned flight on average) based on a flight schedule for a typical week of an air cargo operator. The saving potential depends on the number of flight legs, their distance and the number of available aircraft, their performance variation and loading factor. Although relative savings are of theoretical nature and marginal to total fuel burn (0.2%), we propose an inexpensive approach to decrease total fuel consumption. Furthermore, we show that the usual approach of assigning less efficient aircraft to short flight legs is applicable but does not always provide in the cost minimum solution because low cruising altitudes of short-haul flights adversely affect fuel consumption.


Flight schedule optimization Aircraft rotation assignment Aircraft deterioration Performance deviation factors 



This work has been done in the framework of the research project MEFUL, financed by the Federal Ministry for Economic Affairs and Energy.


  1. 1.
    Peeters, P.M., Middel, J., Hoolhorst, A.: Fuel Efficiency of Commercial Aircraft: an Overview of Historical and Future Trends. National Aerospace Laboratory NLR, Amsterdam (2005)Google Scholar
  2. 2.
    Airbus: Getting hands-on experience with aerodynamic deterioration (2), Blagnac (2002)Google Scholar
  3. 3.
    Airbus: Getting to grips with aircraft performance monitoring, Blagnac (2002)Google Scholar
  4. 4.
    Boeing: Fuel conservation - Flight operations engineering (2004)Google Scholar
  5. 5.
    Lapp, M., Wikenhauser, F.: Incorporating aircraft efficiency measures into the tail assignment problem. J. Air Transp. Manag. 19, 25–30 (2012)CrossRefGoogle Scholar
  6. 6.
    Solomon, M.M., Desrosiers, J.: Survey paper—time window constrained routing and scheduling problems. Transp. Sci. 22(1), 1–13 (1988)CrossRefGoogle Scholar
  7. 7.
    Clark, P.: Buying the big jets: fleet planning for airlines. 2nd edn. Ashgate Pub, Hants, England; Burlington, VT (2007)Google Scholar
  8. 8.
    Sherali, H., Bish, E., Zhu, X.: Airline fleet assignment concepts, models, and algorithms. Eur. J. Oper. Res. 172, 1–30 (2006)CrossRefGoogle Scholar
  9. 9.
    Lohatepanont, M., Barnhart, C.: Airline schedule planning: integrated models and algorithms for schedule design and fleet assignment. Transp. Sci. 38(1), 19–32 (2004)CrossRefGoogle Scholar
  10. 10.
    Salhi, S., Sari, M.: A multi-level composite heuristic for the multi-depot vehicle fleet mix problem. Eur. J. Oper. Res. 103, 95–112 (1997)CrossRefGoogle Scholar
  11. 11.
    Koç, Ç, Bektaş, T., Jabali, O., Laporte, G.: The fleet size and mix location-routing problem with time windows: formulations and a heuristic algorithm. Eur. J. Oper. Res. 248(1), 33–51 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Grönkvist, M.: The tail assignment problem. PhD Thesis, Göteburg (2005)Google Scholar
  13. 13.
    Borndörfer, R., Dovica, I., Nowak, I., Schickinger, T.: Robust tail assignment. ZIB Report, 10–08 (2010)Google Scholar
  14. 14.
    Froyland, G., Maher, S., Wu, C.-L.: The recoverable robust tail assignment problem. Transp. Sci. 48(3), 351–372 (2013)CrossRefGoogle Scholar
  15. 15.
    Maher, S., Desaulniers, G., Soumis, F.: The tail assignment problem with look-ahead maintenance constraints. ZIB Report (2015)Google Scholar
  16. 16.
    Hottenrott, A.: An adaptive large neighborhood search algorithm for the tail assignment problem of airlines, PhD Thesis, Aachen (2015)Google Scholar
  17. 17.
    National Research Council. Assessment of Wingtip Modifications to Increase the Fuel Efficiency of Air Force Aircraft. The National Academies Press, Washington, DC (2007). CrossRefGoogle Scholar
  18. 18.
    Barker, S.: Aircraft performance monitoring presentation, Airbus China (2013)Google Scholar
  19. 19.
    Martin, P., Mykoniatis, G.: Study of data to aid trajectory prediction calculation. Eurocontrol, Brussels (1998)Google Scholar
  20. 20.
    Airbus: ICAO WG3 AEM-TG meeting #9, 21 March 2002Google Scholar
  21. 21.
    Meher-Homji, C.B., Chaker, M., Motiwalla, H.: Gas turbine performance, Turbomachinery Laboratory, Texas A&M University (2001)Google Scholar
  22. 22.
    Krajcek, K., Nikolic, D., Domitrovic, A.: Aircraft performance monitoring from flight data. Tech. Gaz. 2, 1337–1344 (2015)Google Scholar
  23. 23.
    Air Berlin: Annual report 2013. p. 45 (2013)Google Scholar
  24. 24.
    Gheysens, F., Golden, B., Assad, A.: A comparison of techniques for solving the fleet size mix and vehicle routing. Oper. Res. Spektrum 6, 207–2016 (1984)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Munkres, J.: Algorithms for the assignment and transportation problems. J. Soc. Ind. Appl. Math. 5(1), 32–38 (1957)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Rosenow, J., Förster, S., Lindner, M., Fricke, H.: Multi-objective trajectory optimization. Int. Transp. 68, 40–43 (2016)Google Scholar
  27. 27.
    Förster, S., Rosenow, J., Lindner, M., Fricke, H.: A toolchain for optimizing trajectories under real weather conditions and realistic flight performance. In: Greener Aviation, Brüssel (2016)Google Scholar
  28. 28.
    Rosenow, J., Fricke, H.: Flight performance modeling to optimize trajectories. In: Deutscher Kongress für Luft- und Raumfahrt, Braunschweig (2016)Google Scholar
  29. 29.
    Airbus: A320, A350 Aircraft characteristics for airport and maintenance planning (2015). Accessed 07 Sept 2015
  30. 30.
    Rosenow, J., Lindner, M., Fricke, H.: Assessment of air traffic networks considering multi criteria targets in network and trajectory optimization. CEAS Aeronaut. J. 8(2), 371–384 (2017)CrossRefGoogle Scholar
  31. 31.
    Poles, D., Nuic, A., Mouillet, V.: Advanced aircraft performance modelling for ATM: analysis of BADA model capabilities. In: 29th Digital Avionics Systems Conference, Salt Lake City, UT, USA. Eurocontrol, Brétigny-sur-Orge (2010)Google Scholar

Copyright information

© Deutsches Zentrum für Luft- und Raumfahrt e.V. 2018

Authors and Affiliations

  1. 1.Institute of Logistics and Aviation Chair of Air Transport Technology and Logistics, TU DresdenDresdenGermany

Personalised recommendations