On Policies for Single-Leg Revenue Management with Limited Demand Information
Name
1810.10900.pdf
Description
Submitted version
Size
878.78 KB
Format
Adobe PDF
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Author(s)
Ma, Will
Simchi-Levi, David
Teo, Chung-Piaw
Date Issued
December 2020
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Will Ma, David Simchi-Levi, Chung-Piaw Teo (2020) On Policies for Single-Leg Revenue Management with Limited Demand Information. Operations Research 69(1):207-226.
Version
Original manuscript
Abstract
In this paper, we study the single-item revenue management problem, with no information given about the demand trajectory over time. When the item is sold through accepting/rejecting different fare classes, the tight competitive ratio for this problem has been established by Ball and Queyranne through booking limit policies, which raise the acceptance threshold as the remaining inventory dwindles. However, when the item is sold through dynamic pricing instead, there is the additional challenge that offering a low price may entice high-paying customers to substitute down. We show that despite this challenge, the same competitive ratio can still be achieved using a randomized dynamic pricing policy. Our policy incorporates the price-skimming technique originated by Eren and Maglaras, but importantly we show how the randomized price distribution should be stochastically increased as the remaining inventory dwindles. A key technical ingredient in our policy is a new “Valuation Tracking” subroutine, which tracks the possible values for the optimum, and follows the most “inventory-conservative” control, which maintains the desired competitive ratio. Finally, we demonstrate the empirical effectiveness of our policy in simulations, where its average-case performance surpasses all naive modifications of the existing policies.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1287/OPRE.2020.2048
