Real-time vehicle dispatching operations in traditional car-sharing systems is an already computationally challenging scheduling problem. Electrification only exacerbates the computational difficulties as charge level constraints come into play. To overcome this complexity, we employ an online minimum drift plus penalty (MDPP) approach for SAEV systems that (i) does not require a priori knowledge of customer arrival rates to the different parts of the system (i.e. it is practical from a real-world deployment perspective), (ii) ensures the stability of customer waiting times, (iii) ensures that the deviation of dispatch costs from a desirable dispatch cost can be controlled, and (iv) has a computational time-complexity that allows for real-time implementation. Using an agent-based simulator developed for SAEV systems, we test the MDPP approach under two scenarios with real-world calibrated demand and charger distributions: 1) a low-demand scenario with long trips, and 2) a high-demand scenario with short trips. The comparisons with other algorithms under both scenarios show that the proposed online MDPP outperforms all other algorithms in terms of both reduced customer waiting times and vehicle dispatching costs.

中文翻译：

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一种具有性能保证的共享自动驾驶电动汽车的实时调度策略

传统汽车共享系统中的实时车辆调度操作在计算上已经是一个具有挑战性的调度问题。随着电荷水平限制的发挥，电气化只会加剧计算困难。为了克服这种复杂性，我们对 SAEV 系统采用在线最小漂移加惩罚 (MDPP) 方法，该方法 (i) 不需要对系统不同部分的客户到达率的先验知识（即从实际世界部署角度），(ii) 确保客户等待时间的稳定性，(iii) 确保可以控制调度成本与理想调度成本的偏差，以及 (iv) 具有计算时间复杂度，允许实时时间执行。使用SAEV系统开发的基于代理的模拟器，我们在两个场景下测试了 MDPP 方法，其中包含真实世界的校准需求和充电器分布：1) 长行程的低需求场景，以及 2) 短行程的高需求场景。在两种情况下与其他算法的比较表明，所提出的在线 MDPP 在减少客户等待时间和车辆调度成本方面优于所有其他算法。