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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Veröffentlicht in:	Mathematical methods of operations research (Heidelberg, Germany) Jg. 98; H. 1; S. 43 - 56
Hauptverfasser:	Shanks, Meghan, Yu, Ge, Jacobson, Sheldon H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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