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The Role of Pricing and Revenue Management in a Supply Chain

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... In the static case we discuss two models both of which allow class-dependent cancellations and no-shows. The first model can be seen as a generalization of the single fare class model discussed in Phillips (2005). The second static model aims at determining both the total booking limit and the partitioned allocation of the virtual capacity to each fare class. ...
... Our proposed overbooking model is related to the single fare class model discussed in Section 9.3.2 of (Phillips, 2005). Actually, the optimal booking limit of our model with multiple fare classes is equal to the booking limit obtained by the risk-based overbooking model with a single fare class, where the price is µ 0 /β s , the overbooking cost is θ and the show-up probability is β s . ...
... As mentioned before, our model can be used to provide the overbooking limit to the capacity allocation heuristics like EMSR-a and EMSR-b. Since we allow class-dependent show-up probabilities, our model could perform better than those standard static models that determine the total overbooking limit when the show-up probabilities do not depend on the fare classes (Phillips, 2005). We note that the performance of the proposed model depends on the accuracy of the estimation of the model parameters. ...
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Airline revenue management is concerned with identifying the maximum revenue seat allocation poli-cies. Since a major loss in revenue results from cancellations and no-shows, overbooking has received a significant attention in the literature over the years. In this study, we propose new static and dynamic single-leg overbooking models. In the static case we introduce two models; the first one aims to determine the overbooking limit and the second one is about finding the overbooking limit and the booking limits to allocate the virtual capacity among multiple fare classes. Since the second static model is hard to solve, we also introduce computationally tractable models that give upper and lower bounds on its optimal expected net revenue. In the dynamic case, we propose a dynamic programming model, which is based on two streams of events. The first stream corresponds to the arrival of booking requests and the second one corresponds to the cancellations. We conduct simulation experiments to illustrate the effectiveness of the proposed models.
... Such revenue management techniques are particularly widespread in the airline, hotel, and car rental industries. There is now a large literature on revenue management; see Talluri and van Ryzin (2004b) or Phillips (2005) for an overview. ...
... More details of such a model are provided in Section 2.1, where we follow a development due to Brumelle et al. (1990). Other treatments of buy-up models appear in, for instance, Belobaba (1989) and Pfeifer (1989) as well as in the texts of Talluri and van Ryzin (2004b) and Phillips (2005). Although buy-up models are generally not able to describe the full range of potential customer behaviors, they would appear to offer an improvement over the standard Littlewood rule. ...
... The formula (28) is well-known and can be motivated with an informal marginal analysis; see, e.g., Belobaba (1989), Talluri and van Ryzin (2004b), or Phillips (2005). ...
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We consider settings in which a revenue manager controls bookings over a sequence of flights. The revenue manager uses a buy‐up model to select booking limits and updates estimates of the model parameters as data are accumulated. The buy‐up model we consider is based upon a simple model of customer choice, wherein each low‐fare customer who is not able to purchase a low‐fare ticket will, with a fixed probability, “buy up” to the high fare, independent of everything else. We analyze the evolution of the parameter estimates (e.g., the buy‐up probability) and chosen booking limits in situations where the buy‐up model is misspecified, that is, in situations where there is no setting of its parameters for which its objective function gives an accurate representation of expected revenue as a function of the booking limit. The analysis is motivated by the common situation in which a revenue manager does not know precisely how customers behave but nevertheless uses a parametric model to make decisions. Under some assumptions, we prove that the booking limits and parameter estimates converge and we compare the actual expected revenue at the limiting values with that associated with the booking limits that would be chosen if the revenue manager knew the actual behavior of customers. The analysis shows that the buy‐up model often works reasonably well even when it is misspecified, and also reveals the importance of understanding how parameter estimates of misspecified models vary as functions of decisions.
... Bitran and Caldentey (2003), Elmaghraby and Keskinocak (2003), Talluri and van Ryzin (2004), Chiang et al. (2007), and Chen and Chen (2015) give excellent reviews of the work in this field. Phillips (2005) points out the importance of markdown management and dynamic pricing in the recent successes of revenue management applications. Petruzzi and Dada (1999) extend single-period pricing to the multiple-period dynamic pricing model in the newsvendor setting, and discuss the applicability of the model. ...
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