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Edge Weighted Online Windowed Matching

Authors:

Maximilien Burq,

Patrick Jaillet,

Chris SholleyAuthors Info & Claims

EC '19: Proceedings of the 2019 ACM Conference on Economics and Computation

Pages 729 - 742

https://doi.org/10.1145/3328526.3329573

Published: 17 June 2019 Publication History

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.

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Cited By

Amano YIgarashi AKawase YMakino KOno H(2025)Fair Ride Allocation on a LineACM Transactions on Economics and Computation10.1145/3708491Online publication date: 4-Jan-2025
https://dl.acm.org/doi/10.1145/3708491
Xu PSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Parameter-dependent competitive analysis for online capacitated coverage maximization through boostings and attenuationsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694326(54831-54851)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694326
Chen YKanoria YKumar AZhang W(2023)Feature Based Dynamic MatchingSSRN Electronic Journal10.2139/ssrn.4451799Online publication date: 2023
https://doi.org/10.2139/ssrn.4451799
Show More Cited By

Index Terms

Edge Weighted Online Windowed Matching
1. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis
    2. Online algorithms
  2. Theory and algorithms for application domains

Recommendations

Edge-Weighted Online Bipartite Matching
Online bipartite matching is one of the most fundamental problems in the online algorithms literature. Karp, Vazirani, and Vazirani (STOC 1990) gave an elegant algorithm for unweighted bipartite matching that achieves an optimal competitive ratio of 1-1/e ...
Fully Online Matching
Distributed Computing, Parameterized Complexity Theory, Randomized Algorithms, and Computational Geometry

We introduce a fully online model of maximum cardinality matching in which all vertices arrive online. On the arrival of a vertex, its incident edges to previously arrived vertices are revealed. Each vertex has a deadline that is after all its neighbors’...
Edge-Weighted Online Windowed Matching
Consider a matching problem, in which agents arrive to a marketplace over time and leave after some time periods. Agents can only be matched while present in the marketplace. Each pair of agents can yield a different match value, and a social planner ...

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Published In

cover image ACM Conferences

EC '19: Proceedings of the 2019 ACM Conference on Economics and Computation

June 2019

947 pages

ISBN:9781450367929

DOI:10.1145/3328526

General Chair:
Anna Karlin
University of Washington, USA
,
Program Chairs:
Nicole Immorlica
Microsoft Research, USA
,
Ramesh Johari
Stanford University, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGecom: Special Interest Group on Economics and Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2019

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Office of Naval Research

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EC '19

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EC '19: ACM Conference on Economics and Computation

June 24 - 28, 2019

AZ, Phoenix, USA

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EC '19 Paper Acceptance Rate 106 of 382 submissions, 28%;

Overall Acceptance Rate 664 of 2,389 submissions, 28%

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Cited By

Amano YIgarashi AKawase YMakino KOno H(2025)Fair Ride Allocation on a LineACM Transactions on Economics and Computation10.1145/3708491Online publication date: 4-Jan-2025
https://dl.acm.org/doi/10.1145/3708491
Xu PSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Parameter-dependent competitive analysis for online capacitated coverage maximization through boostings and attenuationsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3694326(54831-54851)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3694326
Chen YKanoria YKumar AZhang W(2023)Feature Based Dynamic MatchingSSRN Electronic Journal10.2139/ssrn.4451799Online publication date: 2023
https://doi.org/10.2139/ssrn.4451799
Luo KSpieksma F(2022)Minimizing Travel Time and Latency in Multi-Capacity Ride-Sharing ProblemsAlgorithms10.3390/a1502003015:2(30)Online publication date: 18-Jan-2022
https://doi.org/10.3390/a15020030
Ezra TFeldman MGravin NTang Z(2022)Prophet Matching with General ArrivalsMathematics of Operations Research10.1287/moor.2021.115247:2(878-898)Online publication date: May-2022
https://doi.org/10.1287/moor.2021.1152
Chen WZheng JYu HChen GChen YLi D(2022)Online Learning Bipartite Matching with Non-stationary DistributionsACM Transactions on Knowledge Discovery from Data10.1145/350273416:5(1-22)Online publication date: 9-Mar-2022
https://dl.acm.org/doi/10.1145/3502734
Xiao GZheng JDai H(2022)A Unified Model for Bi-objective Online Stochastic Bipartite Matching with Two-sided Limited PatienceIEEE INFOCOM 2022 - IEEE Conference on Computer Communications10.1109/INFOCOM48880.2022.9796963(1079-1088)Online publication date: 2-May-2022
https://doi.org/10.1109/INFOCOM48880.2022.9796963
Ashlagi INikzad AStrack P(2022)Matching in Dynamic Imbalanced MarketsThe Review of Economic Studies10.1093/restud/rdac04490:3(1084-1124)Online publication date: 2-Aug-2022
https://doi.org/10.1093/restud/rdac044
Shanks MJacobson S(2022)Online total bipartite matching problemOptimization Letters10.1007/s11590-021-01814-016:5(1411-1426)Online publication date: 18-Jan-2022
https://doi.org/10.1007/s11590-021-01814-0
Danassis PSakota MFilos-Ratsikas AFaltings B(2022)Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocationArtificial Intelligence Review10.1007/s10462-022-10145-055:7(5781-5844)Online publication date: 15-Feb-2022
https://doi.org/10.1007/s10462-022-10145-0
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