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Approximation algorithms for stochastic online matching with reusable resources

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Abstract

We consider a class of stochastic online matching problems, where a set of sequentially arriving jobs are to be matched to a group of workers. The objective is to maximize the total expected reward, defined as the sum of the rewards of each matched worker-job pair. Each worker can be matched to multiple jobs subject to the constraint that previously matched jobs are completed. We provide constant approximation algorithms for different variations of this problem with equal-length jobs.

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Acknowledgements

We would like to thank the anonymous reviewers for their comments that resulted in a significantly improved manuscript.

Funding

This research was supported in part by the Air Force Office of Scientific Research (FA9550-19-1-0106). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the United States Air Force, Department of Defense, or the United States Government.

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Authors and Affiliations

  1. Dept. Of Computer Science, University of Illinois at Urbana-Champaign, 201 North Goodwin Avenue, Urbana, IL, 61801, USA

    Meghan Shanks & Sheldon H. Jacobson

  2. Amazon Inc., 101 Main St., Cambrdige, MA, 01242, UK

    Ge Yu

Authors
  1. Meghan Shanks
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  2. Ge Yu
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  3. Sheldon H. Jacobson
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Correspondence to Meghan Shanks.

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Shanks, M., Yu, G. & Jacobson, S.H. Approximation algorithms for stochastic online matching with reusable resources. Math Meth Oper Res 98, 43–56 (2023). https://doi.org/10.1007/s00186-023-00822-3

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  • DOI: https://doi.org/10.1007/s00186-023-00822-3

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