In this paper, we consider an integrated vehicle routing and service scheduling problem for serving customers in distributed locations who need pick-up, drop-off, or delivery services. We take into account the random trip time, nonnegligible service time, and possible customer cancellations, under which an ill-designed schedule may lead to undesirable vehicle idleness and customer waiting. We build a stochastic mixed-integer program to minimize the operational cost plus expected penalty cost of customers’ waiting time, vehicles’ idleness, and overtime. Furthermore, to handle real-time arrived service requests, we develop K-means clustering-based algorithms to dynamically update planned routes and schedules. The algorithms assign customers to vehicles based on similarities and then plan schedules on each vehicle separately. We conduct numerical experiments based on diverse instances generated from census data and data from the Ford Motor Company’s GoRide service, to evaluate result sensitivity and to compare the in-sample and out-of-sample performance of different approaches. Managerial insights are provided using numerical results based on different parameter choices and uncertainty settings.

中文翻译：

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时间和取消不确定性下的综合车辆路线和服务调度在非紧急医疗运输中的应用

在本文中，我们考虑了一个集成的车辆路线和服务调度问题，用于为需要接送服务的分布式地点的客户提供服务。我们考虑了随机行程时间、不可忽略的服务时间和可能的客户取消，在这些情况下，设计不当的时间表可能会导致不良的车辆闲置和客户等待。我们构建了一个随机混合整数程序，以最小化运营成本加上客户等待时间、车辆闲置和加班的预期惩罚成本。此外，为了处理实时到达的服务请求，我们开发了基于 K-means 聚类的算法来动态更新计划的路线和时间表。算法根据相似性将客户分配给车辆，然后分别计划每辆车的时间表。我们根据人口普查数据和福特汽车公司 GoRide 服务数据生成的不同实例进行数值实验，以评估结果敏感性并比较不同方法的样本内和样本外性能。使用基于不同参数选择和不确定性设置的数值结果提供管理见解。