Integrated Vehicle Routing and Service Scheduling Under Time and Cancellation Uncertainties with Application in Nonemergency Medical Transportation
Integrated Vehicle Routing and Service Scheduling Under Time and Cancellation Uncertainties with Application in Nonemergency Medical Transportation
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DOI：
10.1287/serv.2021.0277
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发表时间：
2021-09
期刊：
影响因子：
2.3
通讯作者：
Xian Yu;Siqian Shen;Huizhu Wang
中科院分区：
文献类型：
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作者：
Xian Yu;Siqian Shen;Huizhu Wang
在本文中，我们考虑了一个集成的车辆路径和服务调度问题，为客户提供服务，在分布式位置谁需要皮卡，下车，或交付服务。我们考虑到随机的行程时间，不可忽略的服务时间，和可能的客户取消，在此情况下，一个设计不良的时间表可能会导致不良的车辆闲置和客户等待。我们建立了一个随机混合整数规划，以最小化运营成本加上顾客等待时间、车辆闲置和加班的期望惩罚成本。此外，为了处理实时到达的服务请求，我们开发了基于K-均值聚类的算法来动态更新计划的路线和时间表。该算法根据相似性将客户分配到车辆，然后分别为每辆车计划时间表。我们进行数值实验的基础上，从人口普查数据和数据从福特汽车公司的GoRide服务，评估结果的敏感性，并比较不同的方法的样本内和样本外的性能产生的各种情况。基于不同的参数选择和不确定性设置，使用数值结果提供管理见解。
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.