Philipp Bartke's research while affiliated with Freie Universität Berlin and other places
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Publications (5)
The long-distance traffic division of Deutsche Bahn (DB) uses a revenue management system to sell train-tickets to more than 140 million passengers per year. One essential component of a successful Railway Revenue Management system is an accurate forecast of future demand. To benefit from a tighter integration, DB decided in 2017 to develop its own...
In recent years, revenue management research developed increasingly complex demand forecasts to model customer choice. While the resulting systems should easily outperform their predecessors, it appears difficult to achieve substantial improvement in practice. At the same time, interest in robust revenue maximization is growing. From this arises th...
As revenue management research progresses, simplifying assumptions are removed from the underlying mathematical models. In consequence, these models grow, leading to an increase in complexity that may affect both the performance of automated systems and revenue management analysts. In this article, we demonstrate how both hierarchical and dynamic c...
Building on existing fare adjustment methodology for airline revenue management, this article investigates an approach to account for passenger cancel and rebook behavior. Under cancel and rebook assumptions, passengers continuously search for lower priced alternatives for their travel plans (same flights, same travel dates) until departure, and bo...
A condition for airline revenue management is the possibility of identifying and differentiating customer segments (refer to Chiang et al. (2007) for a state of the art). Traditionally, customer differentiation has been realized by the time of request in days before departure as well as by restrictions connected to the tickets sold. Customer segmen...
Citations
... Another example of using simulation to evaluate the performance of forecast components is given in Bartke et al. (2018). Temath et al. (2010) ...
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... Abrate et al. (2019) suggest focusing on the variability and median of the prices during the advance booking to explain how hotels maximize revenues. In addition, a strong dependence structure among different advance bookings can also be observed when hotels 4 try to prevent speculative behaviors of canceling and re-booking (Gorin et al., 2012). Finally, as shown by Mohammed et al. (2021), there is also reason to expect some degree of asymmetry between upward and downward movements (due to unforeseen reservations/cancellations). ...
... This evolution improves the potential for schedule and inventory optimisation, maximising efficiency and expected revenue through complex mathematical methods as introduced, e.g. in Kunnumkal and Talluri (2019). At the same time, extending planning models introduces new challenges: The increased complexity makes it more difficult to forecast demand, evaluate performance, and target manual interventions (Bartke et al. 2013). For instance, to forecast demand for new itineraries, many parameters have to be initialised from scarce data (Lemke et al. 2012). ...
... The authors study multiple-customer cases and numerically show that customers with strategic behavior improve the seller's profit. Other papers that consider customer behavior (without taking into account the reference price) in revenue management are those by Shen and Su (2007), Kunnumkal and Topaloglu (2008), Cleophas and Bartke (2011), Levin et al. (2010), and Mei and Zhan (2013). In the next section, considering the above studies, we develop a mathematical model to enhance the current literature by adding a reference price in the revenue management models. ...
