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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Vydáno v:Mathematical methods of operations research (Heidelberg, Germany) Ročník 98; číslo 1; s. 43 - 56
Hlavní autoři: Shanks, Meghan, Yu, Ge, Jacobson, Sheldon H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023
Springer Nature B.V
Témata:
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
ISSN:1432-2994, 1432-5217
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1432-2994
1432-5217
DOI:10.1007/s00186-023-00822-3