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- Title
Approximation algorithms for stochastic online matching with reusable resources.
- Authors
Shanks, Meghan; Yu, Ge; Jacobson, Sheldon H.
- 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.
- Subjects
ONLINE algorithms; STOCHASTIC approximation; APPROXIMATION algorithms; ONLINE education
- Publication
Mathematical Methods of Operations Research, 2023, Vol 98, Issue 1, p43
- ISSN
1432-2994
- Publication type
Article
- DOI
10.1007/s00186-023-00822-3