Algorithms for Online Matching, Assortment, and Pricing with Tight Weight-Dependent Competitive Ratios

Will Ma () and David Simchi-Levi ()
Additional contact information
Will Ma: Graduate School of Business, Columbia University, New York, New York 10027
David Simchi-Levi: Institute for Data, Systems, and Society, Department of Civil and Environmental Engineering and Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

Operations Research, 2020, vol. 68, issue 6, 1787-1803

Abstract: 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.

Keywords: online algorithms; revenue management; online matching; Adwords (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://doi.org/10.1287/opre.2019.1957 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:68:y:2020:i:6:p:1787-1803

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().