Algorithms for Online Matching, Assortment, and Pricing with Tight Weight-Dependent Competitive Ratios
Name
1905.04770.pdf
Description
Submitted version
Size
1.04 MB
Format
Adobe PDF
Checksum (MD5)
a8ca2f3108408ea7098eaf326655f36d
Author(s)
Ma, Will
Simchi-Levi, David
Date Issued
2020
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Ma, Will and Simchi-Levi, David. 2020. "Algorithms for Online Matching, Assortment, and Pricing with Tight Weight-Dependent Competitive Ratios." Operations Research, 68 (6).
Version
Original manuscript
Abstract
Copyright: © 2020 INFORMS Motivated by the dynamic assortment offerings and item pricings occurring in e-commerce, we study a general problem of allocating finite inventories to heterogeneous customers arriving sequentially. We analyze this problem under the framework of competitive analysis, where the sequence of customers is unknown and does not necessarily follow any pattern. Previous work in this area, studying online matching, advertising, and assortment problems, has focused on the case where each item can only be sold at a single price, resulting in algorithms which achieve the best-possible competitive ratio of 1−1/e. In this paper, we extend all of these results to allow for items having multiple feasible prices. Our algorithms achieve the best-possible weight-dependent competitive ratios, which depend on the sets of feasible prices given in advance. Our algorithms are also simple and intuitive; they are based on constructing a class of universal value functions that integrate the selection of items and prices offered. Finally, we test our algorithms on the publicly available hotel data set of Bodea et al. [Bodea T, Ferguson M, Garrow L (2009) Data set-Choice-based revenue management: Data from a major hotel chain. Manufacturing Service Oper. Management 11(2):356-361.], where there are multiple items (hotel rooms), each with multiple prices (fares at which the room could be sold). We find that applying our algorithms, as a hybrid with algorithms that attempt to forecast and learn the future transactions, results in the best performance.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1287/OPRE.2019.1957
