空间众包下面向任务需求的在线三类分配策略

Online Three-Object Allocation Strategy for Task Requirements under Spatial Crowdsourcing

目前,有关任务分配的研究主要集中于任务和工作者两种角色,通常假设任务都是同质的,忽略了任务需求和配送地点对任务分配的影响。为解决任务指定配送点附近工作者较少导致移动成本和执行时间过高的问题,提出一种基于贪心的延时匹配算法。一方面,利用任务等待时间获取更多优质工作者以提高分配效果;另一方面,通过自适应阈值和约束条件设置快速筛选匹配对以减小遍历规模并加快分配效率。最后,在不同参数分布下的模拟数据集和真实数据集上对所提出的算法开展实验分析,证明所该方法具有可行性和有效性。

Most of the current research on task assignment focuses on roles of tasks and workers,and they usually assume that tasks are homogeneous,ignoring the influence of task requirement and pick-up location on task assignment.In order to solve the problem that the travel cost and execution time are too high due to the lack of workers near the pick-up location designated by tasks,propose a time-delayed matching greedy algorithm.On the one hand,the waiting time of tasks is used to obtain more high-quality workers to improve the allocation effect.And on the other hand,matching triples are quickly filtered by the setting of adaptive threshold and constraints to reduce the traversal size and speed up the allocation efficiency.Finally,the performance of the proposed algorithm under different parameters is compared and analyzed under the simulated dataset and the real dataset through experiments,which proves the feasibility and effectiveness of the proposed method.