Revenue Management Under a General Discrete Choice Model of Consumer Behavior

By Kalyan T. Talluri, G. V. Ryzin. Management Sciences, 2004

1,184 citations recorded by the source corpus.

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 reserve management problem in which the buyers' choice behavior is modeled explicitly. The choice model is very general, simply specifying the probability of purchase for each fare product as a 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 for this problem has a quite simple form. Namely, it consists of identifying an ordered family of "efficient" subsets S 1 ,..., S m , and at each point in time opening one of these sets S k , where the optimal index k is increasing in the remaining capacity x and decreasing in the remaining time. That is, the more capacity (or less time) available, the further the optimal set is along this sequence. We also show that the optimal policy is a nested allocation policy if and only if the sequence of efficient sets is nested, that is S 1 ? S 2 ?... ? S m . Moreover, we give a characterization of when nesting by fare order is optimal. 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.

Subfields: Management Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/d2fed6d1289f610638861960f8d93438d9e26622

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Citations in the corpus

On the Choice-Based Linear Programming Model for Network Revenue Management (2008, methodology): “In particular, in Section 5 we use the CDLP and our asymptotic analysis to extend the notion of efficient sets introduced by Talluri and van Ryzin [27].”
Managing Flexible Products on a Network (2005, neutral): “Talluri and Van Ryzin [18] apply the Multinomial Logit Model to a single-leg case.”
A Column Generation Algorithm for Choice-Based Network Revenue Management (2009, background): “On the theoretical side, Talluri and van Ryzin [14] provide an exact analysis of the optimal control policy for a single-leg RM model under a general discrete choice model of demand.”
Appointment scheduling in health care: Challenges and opportunities (2008, background): “Such models have the potential to improve appointment scheduling via algorithmic decision support tools, similar to their successful application in other service industries such as the airlines, car rental agencies and hotels (see Talluri and Van Ryzin (2004a) ).”
Customer Behavior Modeling in Revenue Management and Auctions: A Review and New Research Opportunities (2007, methodology): “Using the linear programming formulation and the asymptotic results, the authors extend the notion of efﬁcient sub-sets, introduced by Talluri and van Ryzin (2004), to the network setting.”
Active Ranking with Subset-wise Preferences (2018, background): “), and perhaps to extend this theory to newer formulations such as assortment selection [5, 16], revenue maximization with item prices [35, 1], or even in contextual scenarios [17] where every individual user comes with their own model parameter.”
Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint (2010, background): “Numerical experiments based on sales data from an online retailer indicate that our policy performs well.”
Battle of Bandits (2018, background): “multinomial logit [19], mallows and mixture of mallows [9], markov chain based choice models [10], single transition model [15], general discrete choice models [7] etc.”
Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters (2014, methodology): “Talluri and van Ryzin (2004b) study a revenue management problem over a single ﬂight leg.”
Assortment Optimization Under General Choice (2011, background): “As shown in Talluri and van Ryzin (2004), the solution to the dynamic program reduces to solving a slight variant of the static assortment optimization problem.”
Joint Stocking and Product Offer Decisions Under the Multinomial Logit Model (2013, background): “Talluri and van Ryzin (2004) study revenue management problems over a single ﬂight leg and characterize the structure of the optimal policy.”
Dynamic Pricing Strategies for Multiproduct Revenue Management Problems (2006, background): “Note that the deterministic trajectory of the fluid model is such that x/(T − t) = C/T for all t if λ̂ ≥ C/T , and x/(T − t) = (C − λ̂t)/(T − t) ≥ C/T if λ̂ C/T .”