Spatial Bottleneck Minimum Task Assignment with Time-Delay

Long Li, Jingzhi Fang, Bowen Du, Weifeng Lv

Abstract: Spatial crowdsourcing (SC) services become very popular. And one basic problem in SC is how to appropriately assign tasks to workers for better user experience. Most of existing researches focus on utilitarian optimization objectives for the benefit of the platform, such as maximizing the number of performed tasks, maximizing the total utility of the assignment, and minimizing the total cost to perform all tasks. However, users (i.e., task-requesters and workers) usually only care about their own cost (i.e., each user hopes his/her cost in the assignment to be small) instead of such those utilitarian optimization objectives. From the perspective of users, we propose an egalitarian version of online task assignment problem in SC, namely Minimizing Bottleneck with Time-Delay in Spatial Crowdsourcing (MBTD-SC). We further devise a heuristic algorithm to solve it. Finally, we validate the effectiveness of the proposed algorithm on both synthetic and real datasets.