The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Revenue Management Under a General Discrete Choice Model of Consumer Behavior
Open accessAbstract
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.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
When choice is demotivating: can one desire too much of a good thing?
Iyengar, S. S., & Lepper, M. R. (2000) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.79.6.995 · Cited by 2,878
Yield management at American airlines
Smith, B. C., Leimkuhler, J. F., & Darrow, R. M. (1992) · Interfaces · doi:10.1287/inte.22.1.8 · Cited by 697 · cited 1×
“Significant revenue benefits have been documented from such techniques—often an improvement of 2%–8% in revenue over no revenue management or ad hoc, manual controls (Smith et al. 1992).”
Method
Cited by
Appointment Scheduling in Health Care: Challenges and Opportunities
Gupta, D., & Denton, B. (2008) · IIE Transactions · doi:10.1080/07408170802165880 · Cited by 1,043
An overview of research on revenue management: Current issues and future research
Chiang, W. C., Chen, J. C. H., & Xu, X. (2007) · International Journal of Revenue Management · doi:10.1504/ijrm.2007.011196 · Cited by 447
Dynamic Pricing and Inventory Control of Substitute Products
Dong, L., Kouvelis, P., & Tian, Z. (2009) · Manufacturing & Service Operations Management · doi:10.1287/msom.1080.0221 · Cited by 311
Strategic and tactical price decisions in hotel revenue management
Abrate, G., & Viglia, G. (2016) · Tourism Management · doi:10.1016/j.tourman.2016.02.006 · Cited by 224
Passenger demand forecasting in scheduled transportation
Banerjee, N., Morton, A., & Akartunalı, K. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.10.032 · Cited by 106
Metadata
- Authors
- Talluri, K., & van Ryzin, G.
- Venue
- Management Science
- Published
- 2004-01
- Volume
- 50
- Issue
- 1
- Pages
- 15-33
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2144425834
- Cited by
- 1,108
- References
- 3
- FWCI
- 29.16
- Type
- journal article
- Language
- en
- Open access
- green
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