dc.contributor.author Basik, F. en_US dc.contributor.author Gedik, B. en_US dc.contributor.author Ferhatosmanoglu, H. en_US dc.contributor.author Wu, K. en_US dc.date.accessioned 2019-02-21T16:05:55Z en_US dc.date.available 2019-02-21T16:05:55Z en_US dc.date.issued 2018 en_US dc.identifier.issn 1939-1374 (online) en_US dc.identifier.uri http://hdl.handle.net/11693/50281 en_US dc.description.abstract Faster 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 $10^7\times$ 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. IEEE en_US dc.language.iso English en_US dc.source.title IEEE Transactions on Services Computing en_US dc.relation.isversionof https://doi.org/10.1109/TSC.2018.2854866 en_US dc.subject Crowdsourced delivery en_US dc.subject Crowdsourcing en_US dc.subject Fairness en_US dc.subject Heuristic algorithms en_US dc.subject Industries en_US dc.subject Measurement en_US dc.subject Reliability en_US dc.subject Resource management en_US dc.subject Spatial crowdsourcing en_US dc.subject Task analysis en_US dc.title Fair task allocation in crowdsourced delivery en_US dc.type Article en_US dc.department Computer Technology and Information Systems en_US dc.identifier.doi 10.1109/TSC.2018.2854866 en_US dc.publisher Institute of Electrical and Electronics Engineers en_US
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