Abstract
One of the key challenges in revenue management is unconstraining demand data. Existing state of the art single-class unconstraining methods make restrictive assumptions about the form of the underlying demand and can perform poorly when applied to data which breaks these assumptions. In this paper, we propose an unconstraining method that uses Gaussian process (GP) regression. We develop a novel GP model by constructing and implementing a new non-stationary covariance function for the GP which enables it to learn and extrapolate the underlying demand trend. We show that this method can cope with important features of realistic demand data, including nonlinear demand trends, variations in total demand, lengthy periods of constraining, non-exponential inter-arrival times, and discontinuities/changepoints in demand data. In all such circumstances, our results indicate that GPs outperform existing single-class unconstraining methods.
| Original language | English |
|---|---|
| Pages (from-to) | 621-634 |
| Number of pages | 14 |
| Journal | European Journal of Operational Research |
| Volume | 275 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 1 Jun 2019 |
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Keywords
- Demand unconstraining
- Gaussian process regression
- OR in airlines
- Revenue management
Cite this
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Gaussian processes for unconstraining demand. / Price, Ilan; Fowkes, Jaroslav; Hopman, Daniel.
In: European Journal of Operational Research, Vol. 275, No. 2, 01.06.2019, p. 621-634.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - Gaussian processes for unconstraining demand
AU - Price, Ilan
AU - Fowkes, Jaroslav
AU - Hopman, Daniel
PY - 2019/6/1
Y1 - 2019/6/1
N2 - One of the key challenges in revenue management is unconstraining demand data. Existing state of the art single-class unconstraining methods make restrictive assumptions about the form of the underlying demand and can perform poorly when applied to data which breaks these assumptions. In this paper, we propose an unconstraining method that uses Gaussian process (GP) regression. We develop a novel GP model by constructing and implementing a new non-stationary covariance function for the GP which enables it to learn and extrapolate the underlying demand trend. We show that this method can cope with important features of realistic demand data, including nonlinear demand trends, variations in total demand, lengthy periods of constraining, non-exponential inter-arrival times, and discontinuities/changepoints in demand data. In all such circumstances, our results indicate that GPs outperform existing single-class unconstraining methods.
AB - One of the key challenges in revenue management is unconstraining demand data. Existing state of the art single-class unconstraining methods make restrictive assumptions about the form of the underlying demand and can perform poorly when applied to data which breaks these assumptions. In this paper, we propose an unconstraining method that uses Gaussian process (GP) regression. We develop a novel GP model by constructing and implementing a new non-stationary covariance function for the GP which enables it to learn and extrapolate the underlying demand trend. We show that this method can cope with important features of realistic demand data, including nonlinear demand trends, variations in total demand, lengthy periods of constraining, non-exponential inter-arrival times, and discontinuities/changepoints in demand data. In all such circumstances, our results indicate that GPs outperform existing single-class unconstraining methods.
KW - Demand unconstraining
KW - Gaussian process regression
KW - OR in airlines
KW - Revenue management
UR - http://www.scopus.com/inward/record.url?scp=85059756209&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059756209&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2018.11.065
DO - 10.1016/j.ejor.2018.11.065
M3 - Article
VL - 275
SP - 621
EP - 634
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 2
ER -