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Approximation algorithms for stochastic online matching with reusable resources

Author

Listed:
  • Meghan Shanks

    (University of Illinois at Urbana-Champaign)

  • Ge Yu

    (Amazon Inc.)

  • Sheldon H. Jacobson

    (University of Illinois at Urbana-Champaign)

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.

Suggested Citation

  • Meghan Shanks & Ge Yu & Sheldon H. Jacobson, 2023. "Approximation algorithms for stochastic online matching with reusable resources," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 98(1), pages 43-56, August.
    • Handle: RePEc:spr:mathme:v:98:y:2023:i:1:d:10.1007_s00186-023-00822-3
    DOI: 10.1007/s00186-023-00822-3
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    References listed on IDEAS

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