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Evolving forecast combination structures for airline revenue management

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Journal of Revenue and Pricing Management Aims and scope

Abstract

Forecasting is at the heart of every revenue management system, providing necessary input to capacity control, pricing and overbooking functionalities. For airlines, the key to efficient capacity control is determining the time of when to restrict bookings in a lower-fare class to leave space for later booking high-fare customers. This work presents findings of a collaboration project between Bournemouth University and Lufthansa Systems AG, a company providing revenue management software for airline carriers. The main aim is to increase net booking forecast accuracy by modifying one of its components, the cancellation forecast. Complementing an available set of three traditional individual algorithms, an additional method is presented and added to the method pool. Furthermore, diversification of model parameters and level of learning is discussed to increase the number of individual forecasts even further. Finally, the evolution of forecast combination structures is investigated and shown to be beneficial on an airline data set.

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Authors and Affiliations

  1. Smart Technology Research Centre, School of Design, Engineering and Computing, Bournemouth University, Poole, UK

    Christiane Lemke & Bogdan Gabrys

  2. Lufthansa Systems AG, Berlin, Germany

    Silvia Riedel

Authors
  1. Christiane Lemke
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  2. Silvia Riedel
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  3. Bogdan Gabrys
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Additional information

1received a graduate degree in Computer Science/Intelligent Systems from Brandenburg University of Applied Sciences in Germany in 2004, where she continued working in the area of robotics and image recognition for 2 years. In 2006, she started her PhD studies at Bournemouth University, UK and successfully defended her thesis in March 2010. Her main research interests include the combinations of time-series forecasts and meta-learning.

2works as a Scientific Analyst at Lufthansa Systems AG, Berlin. She received a PhD degree from Bournemouth University in 2008.

3received an MSc degree in Electronics and Telecommunication (Specialization: Computer Control Systems) from the Silesian Technical University, Poland in 1994 and a PhD in Computer Science from the Nottingham Trent University, UK in 1998. After many years of working at different Universities, Professor Gabrys moved to Bournemouth University in January 2003 where he acts as a Director of the Smart Technology Research Centre within the School of Design, Engineering and Computing. His current research interests include a wide range of machine learning and hybrid intelligent techniques encompassing data and information fusion, multiple classifier and prediction systems, processing and modelling of uncertainty in pattern recognition, diagnostic analysis and decision support systems.

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Lemke, C., Riedel, S. & Gabrys, B. Evolving forecast combination structures for airline revenue management. J Revenue Pricing Manag 12, 221–234 (2013). https://doi.org/10.1057/rpm.2012.30

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  • DOI: https://doi.org/10.1057/rpm.2012.30

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