Benchmarking filter-based demand estimates for airline revenue management

  • Philipp Bartke
  • Natalia Kliewer
  • Catherine Cleophas
Research Paper

Abstract

In recent years, revenue management research developed increasingly complex demand forecasts to model customer choice. While the resulting systems should easily outperform their predecessors, it appears difficult to achieve substantial improvement in practice. At the same time, interest in robust revenue maximization is growing. From this arises the challenge of creating versatile and computationally efficient approaches to estimate demand and quantify demand uncertainty. Motivated by this challenge, this paper introduces and benchmarks two filter-based demand estimators: the unscented Kalman filter and the particle filter. It documents a computational study, which is set in the airline industry and compares the estimators’ efficiency to that of sequential estimation and maximum-likelihood estimation. We quantify estimator efficiency through the posterior Cramér–Rao bound and compare revenue performance to the revenue opportunity. Both indicate that unscented Kalman filter and maximum-likelihood estimation outperform the alternatives. In addition, the Kalman filter requires comparatively little computational effort to update and quantifies demand uncertainty.

Keywords

Revenue management Demand estimation Uncertainty Kalman filter Particle filter Simulation 

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Copyright information

© Springer-Verlag Berlin Heidelberg and EURO - The Association of European Operational Research Societies 2017

Authors and Affiliations

  • Philipp Bartke
    • 1
  • Natalia Kliewer
    • 2
  • Catherine Cleophas
    • 3
  1. 1.Information Systems DepartmentFreie Universität BerlinBerlinGermany
  2. 2.Information Systems DepartmentFreie Universität BerlinBerlinGermany
  3. 3.School of Business and EconomicsRWTH Aachen UniversityAachenGermany

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