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  • Kourentzes_2017_Unconstraining

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1057/s41272-017-0117-x

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Unconstraining methods for revenue management systems under small demand

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>14/02/2019
<mark>Journal</mark>Journal of Revenue and Pricing Management
Issue number1
Number of pages15
Pages (from-to)27-41
Publication StatusPublished
Early online date25/09/17
<mark>Original language</mark>English


Sales data often only represents a part of the demand for a service product owing to constraints such as capacity or booking limits. Unconstraining methods are concerned with estimating the true demand from such constrained sales data. This paper addresses the frequently encountered situation of observing only a few sales events at the individual product level and proposes variants of small demand forecasting methods to be used for unconstraining. The usual procedure is to aggregate data; however, in that case we lose information on when restrictions were imposed or lifted within a given booking profile. Our proposed methods exploit this information and are able to approximate convex, concave or homogeneous booking curves. Furthermore, they are numerically robust due to our proposed group-based parameter optimization. Empirical results on accuracy and revenue performance based on data from a major car rental company indicate revenue improvements over a best practice benchmark by statistically significant 0.5%-1.4% in typical scenarios.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1057/s41272-017-0117-x