This paper studies the problem of allocating tasks from different customers to vehicles in mobility platforms, which are used for applications like food and package delivery, ridesharing, and mobile sensing. A mobility platform should allocate tasks to vehicles and schedule them in order to optimize both throughput and fairness across customers. However, existing approaches to scheduling tasks in mobility platforms ignore fairness. We introduce Mobius, a system that uses guided optimization to achieve both high throughput and fairness across customers. Mobius supports spatiotemporally diverse and dynamic customer demands. It provides a principled method to navigate inherent tradeoffs between fairness and throughput caused by shared mobility. Our evaluation demonstrates these properties, along with the versatility and scalability of Mobius, using traces gathered from ridesharing and aerial sensing applications. Our ridesharing case study shows that Mobius can schedule more than 16,000 tasks across 40 customers and 200 vehicles in an online manner.

翻译：本文研究从不同客户向机动平台车辆分配任务的问题,机动平台用于食品和包装交付、搭便车和移动感测等应用;机动平台应当向车辆分配任务并安排任务,以优化吞吐量和客户之间的公平性;然而,现有在机动平台上安排任务的做法忽视了公平性;我们引入了Mobius,这是一个使用引导优化实现高吞吐量和客户公平性的系统;Mobius支持快速、多样和动态的客户需求;它提供了一种原则性方法,用以在共享流动性造成的公平和吞吐之间实现内在的权衡。我们的评估展示了这些特性,同时展示了Mobius的多功能性和可伸缩性,同时使用了从搭载和空中感测应用中收集的痕迹。我们的搭乘案例研究显示,Mobius可以在线方式为40个客户和200个车辆安排16 000多项任务。