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- W3197953008 abstract "In this paper, we study a novel spatial crowdsourcing scenario, where a complex outsourced task is divided into a group of subtasks with dependency relationships. Under this scenario, we investigate a Task Graph Assignment problem in Spatial Crowdsourcing (TGA-SC), which strives to achieve an optimal task assignment solution, with the goal of minimizing the overall makespan and idle time, simultaneously. We propose two heuristic approaches, namely random walk-based algorithm RwalkS, and layered evolutionary algorithm LayGA to tackle TGA-SC problem. Using two real-world data sets, we implement extensive experiments to show the superiority of our proposed approaches." @default.
- W3197953008 created "2021-09-13" @default.
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- W3197953008 date "2021-01-01" @default.
- W3197953008 modified "2023-09-23" @default.
- W3197953008 title "Complex Task Allocation in Spatial Crowdsourcing: A Task Graph Perspective" @default.
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- W3197953008 doi "https://doi.org/10.1007/978-3-030-86137-7_25" @default.
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