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dc.contributor.authorBasik, F.en_US
dc.contributor.authorGedik, B.en_US
dc.contributor.authorFerhatosmanoglu, H.en_US
dc.contributor.authorWu, K.en_US
dc.date.accessioned2019-02-21T16:05:55Zen_US
dc.date.available2019-02-21T16:05:55Zen_US
dc.date.issued2018en_US
dc.identifier.issn1939-1374 (online)en_US
dc.identifier.urihttp://hdl.handle.net/11693/50281en_US
dc.description.abstractFaster and more cost-efficient, crowdsourced delivery is needed to meet the growing customer demands of many industries. In this work, we introduce a new crowdsourced delivery platform that takes fairness towards workers into consideration, while maximizing the task completion ratio. Since redundant assignments are not possible in delivery tasks, we first introduce a 2-phase assignment model that increases the reliability of a worker to complete a given task. To realize the effectiveness of our model in practice, we present both offline and online versions of our proposed algorithm called F-Aware. Given a task-to-worker bipartite graph, F-Aware assigns each task to a worker that maximizes fairness, while allocating tasks to use worker capacities as much as possible. We present an evaluation of our algorithms with respect to running time, task completion ratio, as well as fairness and assignment ratio. Experiments show that F-Aware runs around <formula><tex>$10^7\times$</tex></formula> faster than the TAR-optimal solution and assigns 96.9% of the tasks that can be assigned by it. Moreover, it is shown that, F-Aware is able to provide a much fair distribution of tasks to workers than the best competitor algorithm. IEEEen_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Services Computingen_US
dc.relation.isversionofhttps://doi.org/10.1109/TSC.2018.2854866en_US
dc.subjectCrowdsourced deliveryen_US
dc.subjectCrowdsourcingen_US
dc.subjectFairnessen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectIndustriesen_US
dc.subjectMeasurementen_US
dc.subjectReliabilityen_US
dc.subjectResource managementen_US
dc.subjectSpatial crowdsourcingen_US
dc.subjectTask analysisen_US
dc.titleFair task allocation in crowdsourced deliveryen_US
dc.typeArticleen_US
dc.departmentComputer Technology and Information Systemsen_US
dc.identifier.doi10.1109/TSC.2018.2854866en_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US


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