Revenue management under customer choice behaviour with cancellations and overbooking

Abstract

In many application areas such as airlines and hotels a large number of bookings are typically cancelled. Explicitly taking into account cancellations creates an opportunity for increasing revenue. Motivated by this we propose a revenue management model based on Talluri and van Ryzin [27] that takes cancellations into account in addition to customer choice behaviour. Moreover, we consider overbooking limits as these are influenced by cancellations. We model the problem as a Markov decision process and propose three dynamic programming formulations to solve the problem, each appropriate in a different setting. We show that in certain settings the problem can be solved exactly using a tractable solution method. For other settings we propose tractable heuristics, since the problem faces the curse of dimensionality. Numerical results show that the heuristics perform almost as good as the exact solution. However, the model without cancellations can lead to a revenue loss of up to 20%. Lastly we provide a parameter estimation method based on Newman et alii [24]. This estimation method is fast and provides good parameter estimates. The combination of the model, the tractable and well-performing solution methods, and the parameter estimation method ensures that the model can efficiently be applied in practice.

A call for exploratory data analysis in revenue management forecasting: a case study of a small and independent hotel in The Netherlands

“…Whereas it is complicated to develop tractable solution methods and accurate parameter estimation methods that perform well on computation time, taking a relevant model extension (based on exploratory data analysis) into account can have substantial impact on revenue, as is reported in literature. For example, the seminal model by Talluri and van Ryzin (2004b) increased revenue up to 12% compared to 1-2% differences in other literature at the time, by incorporating customer choice-behaviour; and Sierag et al (2015) showed that by incorporating cancellations into the Talluri and van Ryzin (2004b) model, a substantial (additional) impact on revenue, up till 20%, could be achieved. The following theoretical proposition is, therefore, formulated.…”

“…As was found in the analysis, on average the closer to the day of arrival, the more clients booked, but this finding did not reveal the nature of the demand distribution. Revenue management literature generally assumes a (nonhomogeneous) Poisson distribution (e.g., McGill and van Ryzin, 1999;Bitran and Caldentey, 2003;Talluri and van Ryzin, 2004b;Sierag et al, 2015). That is, demand per time period is modelled as a homogeneous Poisson process.…”

“…Hotel RMS traditionally assume that demand for each rate class is distinct and independent of the alternative options hotel guests have when booking a room. To challenge this common assumption and to incorporate other important buying behaviour aspects, customer choice models have been proposed in the revenue management literature (e.g., van Ryzin, 2004a, 2004b;Liu and van Ryzin, 2008;Erdelyi and Topaloglu, 2010;Aydin et al, 2012;Meissner and Strauss, 2012;Sierag et al, 2015). When customer choice behaviour is incorporated, data analysis research will be especially important as in order to apply customer choice modelling to hotel revenue management practice successfully the appropriate choice (and estimation) of model parameters is crucial (e.g., van Ryzin, 2005;van Ryzin and Vulcano, 2013;Newman et al, 2014).…”

Optimal online channel strategies for a hotel considering direct booking and cooperation with an online travel agent

“…For example, Koide and Ishii (), Sierag et al. (), and Chua et al. () present models studying hotel room allocations with early‐discount, cancellation, and overbooking facilitated by OTAs.…”

Market structure and the value of overselling under stochastic demands

“…In a related work, Netessine and Shumsky (2005) considered the equilibrium allocation of seat capacity between fare classes and booking limits when firms compete under the same price points. Recently, Sierag et al (2015) propose a revenue management model that takes into account of cancellations and customer choice behaviour. Using models to calibrate the parameters, they design efficient heuristic solution methods to optimize revenue.…”