论文标题
动态汽车调度和定价：乘车平台的收入和公平性
Dynamic Car Dispatching and Pricing: Revenue and Fairness for Ridesharing Platforms
论文作者
论文摘要
乘车平台的一个主要挑战是同时保证利润和公平性，尤其是在驾驶员和骑手的激励措施不一致的情况下。我们专注于调度定价问题，以最大程度地提高总收入，同时使驾驶员和骑手都满意。我们研究了问题的计算复杂性，提供了一种新型的两步定价解决方案，并保证了收入和公平性，将其扩展到随机设置，并开发一种动态（又称学习 - 学习过程）算法，该算法在调度过程中积极收集数据以学习需求分布。我们还进行了广泛的实验，以证明算法的有效性。
A major challenge for ridesharing platforms is to guarantee profit and fairness simultaneously, especially in the presence of misaligned incentives of drivers and riders. We focus on the dispatching-pricing problem to maximize the total revenue while keeping both drivers and riders satisfied. We study the computational complexity of the problem, provide a novel two-phased pricing solution with revenue and fairness guarantees, extend it to stochastic settings and develop a dynamic (a.k.a., learning-while-doing) algorithm that actively collects data to learn the demand distribution during the scheduling process. We also conduct extensive experiments to demonstrate the effectiveness of our algorithms.
