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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

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Unconstraining methods for revenue management systems under small demand. / Kourentzes, Nikolaos; Dong, Li; Strauss, Arne.

In: Journal of Revenue and Pricing Management, Vol. 18, No. 1, 01.02.2019, p. 27-41.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Kourentzes, N, Dong, L & Strauss, A 2019, 'Unconstraining methods for revenue management systems under small demand', Journal of Revenue and Pricing Management, vol. 18, no. 1, pp. 27-41. https://doi.org/10.1057/s41272-017-0117-x

APA

Kourentzes, N., Dong, L., & Strauss, A. (2019). Unconstraining methods for revenue management systems under small demand. Journal of Revenue and Pricing Management, 18(1), 27-41. https://doi.org/10.1057/s41272-017-0117-x

Vancouver

Kourentzes N, Dong L, Strauss A. Unconstraining methods for revenue management systems under small demand. Journal of Revenue and Pricing Management. 2019 Feb 1;18(1):27-41. https://doi.org/10.1057/s41272-017-0117-x

Author

Kourentzes, Nikolaos ; Dong, Li ; Strauss, Arne. / Unconstraining methods for revenue management systems under small demand. In: Journal of Revenue and Pricing Management. 2019 ; Vol. 18, No. 1. pp. 27-41.

Bibtex

@article{6a92c52058af43cebcf7cc23939a3e99,
title = "Unconstraining methods for revenue management systems under small demand",
abstract = "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.",
keywords = "Demand unconstraining, Forecasting , Small demand , Revenue management ",
author = "Nikolaos Kourentzes and Li Dong and Arne Strauss",
note = "The final publication is available at Springer via http://dx.doi.org/10.1057/s41272-017-0117-x",
year = "2019",
month = feb,
day = "1",
doi = "10.1057/s41272-017-0117-x",
language = "English",
volume = "18",
pages = "27--41",
journal = "Journal of Revenue and Pricing Management",
issn = "1476-6930",
publisher = "Palgrave Macmillan Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - Unconstraining methods for revenue management systems under small demand

AU - Kourentzes, Nikolaos

AU - Dong, Li

AU - Strauss, Arne

N1 - The final publication is available at Springer via http://dx.doi.org/10.1057/s41272-017-0117-x

PY - 2019/2/1

Y1 - 2019/2/1

N2 - 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.

AB - 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.

KW - Demand unconstraining

KW - Forecasting

KW - Small demand

KW - Revenue management

U2 - 10.1057/s41272-017-0117-x

DO - 10.1057/s41272-017-0117-x

M3 - Journal article

VL - 18

SP - 27

EP - 41

JO - Journal of Revenue and Pricing Management

JF - Journal of Revenue and Pricing Management

SN - 1476-6930

IS - 1

ER -