Approximation algorithms for stochastic online matching with reusable resources

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 mult...

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Bibliographic Details
Published inMathematical methods of operations research (Heidelberg, Germany) Vol. 98; no. 1; pp. 43 - 56
Main Authors Shanks, Meghan, Yu, Ge, Jacobson, Sheldon H.
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023
Springer Nature B.V
Subjects
Algorithms
Approximation
Business and Management
Calculus of Variations and Optimal Control; Optimization
Employment
Expected values
Markov analysis
Matching
Mathematical analysis
Mathematics
Mathematics and Statistics
Operations research
Operations Research/Decision Theory
Original Article
Passenger screening
Queuing
Random variables
Scheduling
Success
Workers
Stochastic matching
Online algorithms
Approximation algorithms
Reusable resources
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1432-2994
1432-5217
DOI:10.1007/s00186-023-00822-3