The history of unconstraining models in revenue management

Weatherford, Larry

doi:10.1057/rpm.2016.10

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…In what follows, we categorise and give a brief overview of the main single-class unconstraining methods which have been proposed and compared since then. For more general reviews of existing unconstraining research, see [10,32].…”

Section: Literature Reviewmentioning

confidence: 99%

Gaussian processes for unconstraining demand

Price

Fowkes

Hopman

2019

European Journal of Operational Research

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Section: Literature Reviewmentioning

confidence: 99%

Gaussian processes for unconstraining demand

Price

Fowkes

Hopman

2019

European Journal of Operational Research

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“…Other detruncation algorithms include booking curve (BC) and projection detruncation (PD) Skwarek, 10 nearest research by Zeni. 11 Weatherford 12 shows that EM is the method of mean absolution deviation minimization. In this paper, we use the widely accepted EM method to detruncate the booking data before demand forecasting.…”

Section: Introductionmentioning

confidence: 99%

Support vector regression model for flight demand forecasting

Fan

Xiang

Shi

et al. 2023

International Journal of Engineering Business Management

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Flight demand forecasting is a particularly critical component for airline revenue management because of the direct influence on the booking limits that determine airline profits. The traditional flight demand forecasting models generally only take day of the week (DOW) and the current data collection point (DCP) adds up bookings as the model input and uses linear regression, exponential smoothing, pick-up as well as other models to predict the final bookings of flights. These models can be regarded as time series flight demand forecasting models based on the interval between the current date and departure date. They fail to consider the early bookings change features in the specific flight pre-sale period, and have weak generalization ability, at last, they will lead to poor adaptability to the random changes of flight bookings. The support vector regression (SVR) model, which is derived from machine learning, has strong adaptability to nonlinear random changes of data and can adaptively learn the random disturbances of flight bookings. In this paper, flight bookings are automatically divided into peak, medium, and off (PMO) according to the season attribute. The SVR model is trained by using the vector composed of historical flight bookings and adding up bookings of DCP in the early stage of the flight pre-sale period. Compared with the traditional models, the priori information of flight is increased. We collect 2 years of domestic route bookings data of an airline in China before COVID-19 as the training and testing datasets, and divide these data into three categories: tourism, business, and general, the numerical results show that the SVR model significantly improves the forecasting accuracy and reduces RMSE compared with the traditional models. Therefore, this study provides a better choice for flight demand forecasting.

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“…Guo surveyed the history of research on unconstraining methods by reviewing over 130 references [17]. Weatherford provided two reviews of unconstrained estimation and forecast methods broadly used in different industries up to 2016 [18,19]. Recent discussions include forecast multipliers and hybrid forecasting [9], the effect of customers' reference price on demand [20], Gaussian processes for unconstraining demand [21], and demand forecast accuracy [22].…”

Section: Introductionmentioning

confidence: 99%

Unconstrained Estimation of Multitype Car Rental Demand

et al. 2021

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The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.

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The history of unconstraining models in revenue management

Cited by 12 publications

References 30 publications

Gaussian processes for unconstraining demand

Gaussian processes for unconstraining demand

Support vector regression model for flight demand forecasting

Unconstrained Estimation of Multitype Car Rental Demand

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