Optimal and Approximate Control Policies for Airline Booking with Sequential Nonmonotonic Fare Classes

Robinson, Lawrence W.

doi:10.1287/opre.43.2.252

Cited by 161 publications

(89 citation statements)

References 14 publications

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“…airplane tickets) is computed for each consumer type. These thresholds can vary over time as demand unfolds (Littlewood 1972, Brumelle and McGill 1993, Robinson 1995. Literature on capacity allocation usually assumes a monopoly market structure with the exception that Netessine and Shumsky (2005) analyze seat allocation under both horizontal and vertical competitions using a game theoretical model.…”

Section: Literature Reviewmentioning

confidence: 99%

Strategic Consumer Response to Dynamic Pricing of Perishable Products

Cho

Fan

Zhou

2009

International Series in Operations Research &Amp; Management Science

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Dynamic pricing is a standard practice that sellers use for revenue management. With the vast availability of pricing and inventory data on the Internet, it is possible for consumers to become aware of the pricing strategies used by sellers and to develop strategic responses. In this paper, we study the strategic response of consumers to dynamic prices for perishable products. As price fluctuates with the changes in time and inventory, a strategic consumer may choose to postpone a purchase in anticipation of lower prices in the future. We analyze a threshold purchasing policy for the strategic consumer, and conduct numerical studies to study its impact on both the strategic consumer's benefits and the seller's revenue. We find that in most cases the policy can benefit both the strategic consumer and the seller. In practice, the seller could encourage consumers waiting by adopting a target price purchasing system.

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Section: Literature Reviewmentioning

confidence: 99%

Strategic Consumer Response to Dynamic Pricing of Perishable Products

Cho

Fan

Zhou

2009

International Series in Operations Research &Amp; Management Science

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“…Robinson (1995) employs Monte Carlo integration to solve an expected marginal seat revenue (EMSR) formulation. van Ryzin and McGill (2000) describe a Robbins-Monrolike approach-a stochastic approximation (SA)-type method-for a similar formulation.…”

Section: Introductionmentioning

confidence: 99%

Some Decomposition Methods for Revenue Management

Cooper

Homem-de-Mello

2007

Transportation Science

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W orking within a Markov decision process (MDP) framework, we study revenue management policies that combine aspects of mathematical programming approaches and pure MDP methods by decomposing the problem by time, state, or both. The "time decomposition" policies employ heuristics early in the booking horizon and switch to a more-detailed decision rule closer to the time of departure. We present a family of formulations that yield such policies and discuss versions of the formulation that have appeared in the literature. Subsequently, we describe sampling-based stochastic optimization methods for solving a particular case of the formulation. Numerical results for two-leg problems suggest that the policies perform well. By viewing the MDP as a large stochastic program, we derive some structural properties of two-leg problems. We show that these properties cannot, in general, be extended to larger networks. For such larger networks we also present a "state-space decomposition" approach that partitions the network problem into two-leg subproblems, each of which is solved. The solutions of these subproblems are then recombined to obtain a booking policy for the network problem.

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“…On a theoretical level, single-leg models in which demand for each fare product is assumed to occur in non-overlapping periods have been developed and analyzed by Brumelle and McGill [13], Curry [16], Robinson [35] and Wollmer [45]. A key result of this work is that the optimal policy can be implemented using a set of so-called nested allocations.…”

Section: Introduction and Overviewmentioning

confidence: 99%

Revenue Management Under a General Discrete Choice Model of Consumer Behavior

Talluri¹,

Ryzin²

2004

mnsc

141

158

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Customer choice behavior, such as "buy-up" and "buy-down", is an important phenomenon in a wide range of revenue management contexts. Yet most revenue management methodologies ignore this phenomenon -or at best approximate it in a heuristic way. In this paper, we provide an exact and quite general analysis of this problem. Specifically, we analyze a single-leg yield management problem in which the buyers' choice behavior is modeled explicitly. The choice model is perfectly general and simply specifies the probability of purchasing each fare product as function of the set of fare products offered. The control problem is to decide which subset of fare products to offer at each point in time. We show that the optimal policy is of a simple form. Namely, it consists of 1) identifying the ordered family of "nondominated" subsets S 1 , ..., S m , and 2) at each point in time opening one of these sets S k , where the optimal index k is increasing in the remaining capacity x. That is, the more capacity we have available, the further the optimal set is along this sequence. Moreover, we show that the optimal policy is nested if and only if the ordered sets are increasing, that is S 1 ⊆ S 2 ⊆ ... ⊆ S n , and we give a complete characterization of when nesting by fare order is optimal. We then show that two important models, the independent demand model and the multinomial logit model (MNL), satisfy this later condition and hence nested-by-fare-order policies are optimal in these cases. We also develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure.

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Optimal and Approximate Control Policies for Airline Booking with Sequential Nonmonotonic Fare Classes

Cited by 161 publications

References 14 publications

Strategic Consumer Response to Dynamic Pricing of Perishable Products

Strategic Consumer Response to Dynamic Pricing of Perishable Products

Some Decomposition Methods for Revenue Management

Revenue Management Under a General Discrete Choice Model of Consumer Behavior

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