2026-09-18T17:34:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1217132026-06-16T18:51:59Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Itai Ashlagi and Patrick Jaillet.
Burq, Maximilien.
Massachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
2019-07-15T20:37:35Z
2019-07-15T20:37:35Z
2019
2019
https://hdl.handle.net/1721.1/121713
1104134969
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 203-213).
We study marketplaces in which participants arrive over time, looking to interact with each other. While such interactions have historically been decentralized, the past few years have seen a dramatic increase in the number of internet-enabled platforms which facilitate the process of connecting together, or matching, sets of two or more participants. We will focus mainly on centralized matching markets such as kidney exchange and carpooling platforms. In such platforms, the algorithm which determines whom to match and when to do so plays an important role in the efficiency of the marketplace. In the first part, we study the interface between the participant heterogeneity, the types of matchings that are allowed, and the frequency at which the platform computes the allocations. We provide an empirical analysis of the effect of match frequency based on data from major US Kidney exchange programs. We then study models that enable us to compare the participants' match rates and waiting times under varying matching policies. We show both in theory and in practice that matching quickly can be beneficial, compared to policies which try to increase opportunities for optimization through artificial waiting. Until now, the theory of matching algorithms has focused mostly on static environments and little is known in the case where all participants arrive and depart dynamically. In our second part, we help bridge this gap by introducing a new theoretical problem for dynamic matching when anyone can arrive online. We provide new algorithms with state-of-the-art theoretical guarantees, both in the case of adversarial and random order inputs. Finally, we show that these algorithms perform well on kidney exchange and carpooling data.
by Maximilien Burq.
Ph.D.
Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
279 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Operations Research Center.
Dynamic matching algorithms
Thesis
application/pdf
2019-07-15T20:37:32Z
Publication
Doctoral
Sloan
OperRes
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="08d1a8bd-6c58-48df-9f5f-ed48f37239f0">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Dynamic matching algorithms</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2019</PublicationDate>
<Authors>
<Author>
<DisplayName>Burq, Maximilien.</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Operations Research Center.</Keyword>
<Abstract>We study marketplaces in which participants arrive over time, looking to interact with each other. While such interactions have historically been decentralized, the past few years have seen a dramatic increase in the number of internet-enabled platforms which facilitate the process of connecting together, or matching, sets of two or more participants. We will focus mainly on centralized matching markets such as kidney exchange and carpooling platforms. In such platforms, the algorithm which determines whom to match and when to do so plays an important role in the efficiency of the marketplace. In the first part, we study the interface between the participant heterogeneity, the types of matchings that are allowed, and the frequency at which the platform computes the allocations. We provide an empirical analysis of the effect of match frequency based on data from major US Kidney exchange programs. We then study models that enable us to compare the participants' match rates and waiting times under varying matching policies. We show both in theory and in practice that matching quickly can be beneficial, compared to policies which try to increase opportunities for optimization through artificial waiting. Until now, the theory of matching algorithms has focused mostly on static environments and little is known in the case where all participants arrive and depart dynamically. In our second part, we help bridge this gap by introducing a new theoretical problem for dynamic matching when anyone can arrive online. We provide new algorithms with state-of-the-art theoretical guarantees, both in the case of adversarial and random order inputs. Finally, we show that these algorithms perform well on kidney exchange and carpooling data.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>