Regret in Overbooking and Fare-Class Allocation for Single Leg

Lan, Yingjie; Ball, Michael O.; Karaesmen, Itır Z.

doi:10.1287/msom.1100.0316

Cited by 34 publications

(17 citation statements)

References 28 publications

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“…For example, Lim and Shanthikumar (2007) consider dynamic pricing problems in which the decision maker knows only that the true model is within some specified distance, as measured by relative entropy, from a nominal model. Lan et al (2011) consider overbooking and allocation decisions with limited information using ideas from competitive analysis of algorithms, and focus on obtaining policies that yield the best worst-case performance. The paper also provides many additional references.…”

Section: Literature Reviewmentioning

confidence: 99%

Learning and Pricing with Models That Do Not Explicitly Incorporate Competition

Cooper

Homem-de-Mello

Kleywegt

2015

Operations Research

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In revenue management research and practice, demand models are used that describe how demand for a seller's products depends on the decisions, such as prices, of that seller. Even in settings where the demand for a seller's products also depends on decisions of other sellers, the models often do not explicitly account for such decisions. It has been conjectured in the revenue management literature that such monopoly models may incorporate the effects of competition, because the parameter estimates of the monopoly models are based on data collected in the presence of competition. In this paper we take a closer look at such a setting to investigate the behavior of parameter estimates and decisions if monopoly models are used in the presence of competition. We consider repeated pricing games in which two competing sellers use mathematical models to choose the prices of their products. Over the sequence of games, each seller attempts to estimate the values of the parameters of a demand model that expresses demand as a function only of its own price using data comprised only of its own past prices and demand realizations. We analyze the behavior of the sellers' parameter estimates and prices under various assumptions regarding the sellers' knowledge and estimation procedures, and identify situations in which (a) the sellers' prices converge to the Nash equilibrium associated with knowledge of the correct demand model, (b) the sellers' prices converge to the cooperative solution, and (c) the sellers' prices have many potential limit points that are neither the Nash equilibrium nor the cooperative solution and that depend on the initial conditions. We compare the sellers' revenues at potential limit prices with their revenues at the Nash equilibrium and the cooperative solution, and show that it is possible for sellers to be better off when using a monopoly model than at the Nash equilibrium.

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Section: Literature Reviewmentioning

confidence: 99%

Learning and Pricing with Models That Do Not Explicitly Incorporate Competition

Cooper

Homem-de-Mello

Kleywegt

2015

Operations Research

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“…They show that these two models can be analyzed in a unified manner and both models provide nested booking limits. In a related work, Lan et al (2011) formulate a joint overbooking and seat allocation model, where both the random demand and no-shows are characterized using interval uncertainty. They focus on the seller's regret in not being able to find the optimal policy due to the lack of information.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In particular, we conduct simulation experiments to benchmark the policies obtained with our lower bounding model (P LB I ), upper bounding model (P UB I ) and the dynamic model (P DM ) against some well-known approaches used in the literature (Lan et al, 2008(Lan et al, , 2011. We next explain our simulation setup in detail and then present our numerical results.…”

Section: Computational Experimentsmentioning

confidence: 99%

Single-Leg Airline Revenue Management with Overbooking

Aydin

Birbil

Frenk

et al. 2013

Transportation Science

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Airline revenue management is concerned with identifying the maximum revenue seat allocation policies. 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.Keywords: Revenue management; airline; overbooking; cancellation; static model; dynamic model; dynamic programming; simulation 1. Introduction. Historically, airline industry plays the steering role in revenue management. This can be attributed to the quick responses of the airline executives, who have realized the importance of controlling the reservation process in order to increase their gains over a fiscal year. The main problem, then and now, in airline revenue management is to determine how to reserve the seats for the requests coming from the passengers. Naturally, the objective of this problem is to maximize the total revenue. We refer to (Talluri and van Ryzin, 2005, Section 1.2) for a historical account of the role of airline industry in revenue management.

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“…Luo et al (2009) formulate a static overbooking model with fixed weight and volume capacity for the air cargo business. Lan et al (2011) and Aydın et al (2013) address combined overbooking and capacity allocation problem for multiple booking classes for a single-leg flight, while Mookherjee and Friesz (2008) propose an integrated approach to overbooking, capacity allocation, and pricing for an oligopoly of service providers.…”

Section: Introductionmentioning

confidence: 99%

Integrating Overbooking with Capacity Planning: Static Model and Application to Airlines

2019

Production and Operations Management

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Many firms that practice overbooking are able to adjust capacity prior to the service time. This study proposes an integrated approach for a firm's overbooking and capacity planning. We formulate a static model that simultaneously optimizes the firm's capacity level and overbooking limit. We find that the optimal level of capacity with overbooking can be higher and lower than that without overbooking. This is because overbooking has both positive and negative effects on the marginal value of capacity. It suggests that the level of capacity can be set too low or too high if overbooking is not taken into account when making capacity planning decisions. Moreover, our analysis shows that overbooking leads to a lower optimal capacity level if and only if the marginal cost of capacity is below a threshold. It is also shown that a higher cost per denied service or a higher service level does not necessarily lead to a higher optimal capacity level. We then extend the static model to explicitly account for an airline's demand‐driven dispatch (i.e., reassignments of aircraft types to flight legs). Numerical experiments show that the integrated approach can significantly improve an airline's expected profit, compared to the sequential approach that optimizes capacity planning (aircraft type assignments) and overbooking decisions in two separate and consecutive steps. The benefit of the integrated approach remains significant when a service level constraint is imposed.

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Regret in Overbooking and Fare-Class Allocation for Single Leg

Cited by 34 publications

References 28 publications

Learning and Pricing with Models That Do Not Explicitly Incorporate Competition

Learning and Pricing with Models That Do Not Explicitly Incorporate Competition

Single-Leg Airline Revenue Management with Overbooking

Integrating Overbooking with Capacity Planning: Static Model and Application to Airlines

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