With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing companies have become an important element of the urban mobility system. There are two critical components in the operations of ride-hailing companies: driver–customer matching and vehicle rebalancing. In most previous literature, each component is considered separately, and performances of vehicle rebalancing models rely on the accuracy of future demand predictions. To better immunize rebalancing decisions against demand uncertainty, a novel approach, the matching-integrated vehicle rebalancing (MIVR) model, is proposed in this paper to incorporate driver–customer matching into vehicle rebalancing problems to produce better rebalancing strategies. The MIVR model treats the driver–customer matching component at an aggregate level and minimizes a generalized cost including the total vehicle miles traveled (VMT) and the number of unsatisfied requests. For further protection against uncertainty, robust optimization (RO) techniques are introduced to construct a robust version of the MIVR model. Problem-specific uncertainty sets are designed for the robust MIVR model. The proposed MIVR model is tested against two benchmark vehicle rebalancing models using real ride-hailing demand and travel time data from New York City (NYC). The MIVR model is shown to have better performances by reducing customer wait times compared to benchmark models under most scenarios. In addition, the robust MIVR model produces better solutions by planning for demand uncertainty compared to the non-robust (nominal) MIVR model.

近年来，随着按需出行（MoD）市场的快速增长，网约车公司已成为城市出行系统的重要组成部分。网约车公司的运营有两个关键组成部分：司机-客户匹配和车辆再平衡。在大多数以前的文献中，每个组件都是单独考虑的，车辆再平衡模型的性能依赖于未来需求预测的准确性。为了更好地使再平衡决策免受需求不确定性的影响，本文提出了一种新方法，即匹配集成车辆再平衡（MIVR）模型，将驾驶员-客户匹配纳入车辆再平衡问题中，以产生更好的再平衡策略。MIVR 模型在聚合级别处理驾驶员-客户匹配组件，并最小化广义成本，包括总车辆行驶里程 (VMT) 和未满足请求的数量。为了进一步防止不确定性，引入了稳健优化 (RO) 技术来构建 MIVR 模型的稳健版本。特定问题的不确定性集是为稳健的 MIVR 模型设计的。使用来自纽约市 (NYC) 的实际乘车需求和旅行时间数据，针对两个基准车辆再平衡模型对拟议的 MIVR 模型进行了测试。在大多数情况下，与基准模型相比，MIVR 模型通过减少客户等待时间显示出更好的性能。此外，