Edge Weighted Online Windowed Matching
Name
edge-weight.pdf
Description
Accepted version
Size
912.81 KB
Format
Adobe PDF
Checksum (MD5)
417db5291061bb0778404b30e4375dba
Author(s)
Burq, Maximilien
Jaillet, Patrick
Date Issued
June 2019
Journal
ACM EC 2019 - Proceedings of the 2019 ACM Conference on Economics and Computation
Publisher
Association for Computing Machinery (ACM)
Citation
Ashlagi, Itai et al. “Edge Weighted Online Windowed Matching.” ACM EC 2019 - Proceedings of the 2019 ACM Conference on Economics and Computation" Paper presented at the ACM EC 2019, Phoenix, AZ, June 24-28, 2019, Association for Computing Machinery (ACM): 729–742 © 2019 The Author(s)
Version
Author's final manuscript
Abstract
Motivated by applications from ride-sharing and kidney exchange, we study the problem of matching agents who arrive at a marketplace over time and leave after d time periods. Agents can only be matched while they are present in the marketplace. Each pair of agents can yield a different match value, and the planner's goal is to maximize the total value over a finite time horizon. First we study the case in which vertices arrive in an adversarial order. We provide a randomized 1/4- competitive algorithm building on a result by Feldman et al. [14] and Lehmann et al. [23]. We extend the model to the case in which departure times are drawn independently from a distribution with non-decreasing hazard rate, for which we establish a 1/8-competitive algorithm. When the arrival order is chosen uniformly at random, we show that a batching algorithm, which computes a maximum-weighted matching every (d + 1) periods, is 0.279-competitive.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3328526.3329573
