网约车任务分配系统优化

网约车是一种广泛应用的共享移动应用, 其核心问题是将出租车请求分配给具有不同目标的司机, 尽管对网约车的任务分配进行了广泛的研究, 但在很大程度上忽视了司机之间收入的公平性, 由于优化视角的短视和分配技术的耗时, 先行者对网约车公平任务分配的研究在公平性、效用性方面还存在不足. 在本文中, 提出了公平分配学习(LAF)方法, 它既优化了效用又优化了公平性的高效任务分配方案, 采用强化学习以整体的方式进行分配, 并提出一套加速技术, 以实现大规模数据的快速公平分配. 实验结果表明, 公平分配学习方法在公平性、效用性和效率方面分别比现有水平高出86.7%、29.1%和797%.

Optimization of Task Allocation System for Online Car-hailing

Online car-hailing is a kind of widely used mobile application. Its core problem is to assign requests to taxi drivers with different goals. Although extensive research on task allocation has been carried out, a largely ignored problem is the income equality of drivers. Due to the short-sighted optimization and time-consuming allocation, fairness and utility receive less attention in the research on fair task allocation. In this study, an efficient task assignment scheme, learning to assign with fairness (LAF), was proposed to optimize both utility and fairness. It adopts reinforcement learning to allocate tasks holistically and proposes a set of acceleration techniques to achieve rapid and equitable allocation on a large scale. The experimental results show that the fairness, effectiveness, and efficiency of LAF are 86.7%, 29.1%, and 797% higher than the existing level, respectively.