Several works related to crowdsourcing have been proposed in the direction where the task executors are to perform the tasks within the stipulated deadlines. Though the deadlines are set, it may be a practical scenario that majority of the task executors submit the tasks as late as possible. This situation where the task executors may delay their task submission is termed as procrastination in behavioural economics. In many applications, these late submission of tasks may be problematic for task requesters. In literature, how to prevent this procrastination within the deadline is not addressed in crowdsourcing scenario. However, in a bipartite graph setting one procrastination aware scheduling is proposed but balanced job distribution in different slots (also termed as schedules) is not considered there. In this paper, a procrastination aware scheduling of jobs is proliferated by proposing an (randomized) algorithm in crowdsourcing scenario (also applicable in mobile and spatial crowdsourcing). Our algorithm ensures that balancing of jobs in different schedules are maintained. Our scheme is compared with the existing algorithm through extensive simulation and in terms of balancing effect, our proposed algorithm outperforms the existing one. Analytically it is shown that our proposed algorithm maintains the balanced distribution.

翻译：关于众包的多项研究均着眼于任务执行者在规定期限内完成任务。尽管设定了截止日期，但实际场景中多数任务执行者可能尽可能晚地提交任务。这种任务执行者延迟提交任务的行为在行为经济学中被称为"拖延"。在许多应用中，此类任务的延迟提交可能给任务发布者带来问题。现有文献中尚未探讨如何在众包场景下防止截止日期内的拖延行为。虽有一项研究在二分图框架中提出了拖延感知调度方案，但未考虑不同时段（亦称调度周期）的任务均衡分配。本文通过提出一种（随机化）算法，在众包场景（同样适用于移动众包与空间众包）中推广了拖延感知的作业调度方案。该算法确保不同调度周期内的任务分配保持均衡。通过大量仿真实验与现有算法对比，本方案在均衡效果方面显著优于现有算法。理论分析表明，所提算法能够维持均衡分布。