The recent increase in online orders in e-commerce leads to logistical challenges such as low hit rates (proportion of successful deliveries). We consider last-mile vehicle routing and scheduling problems in which customer presence probability data are taken into account. The aim is to reduce the expected cost resulting from low hit rates by considering both routing and scheduling decisions simultaneously in the planning phase. We model the problem and solve it by the means of an adaptive large neighborhood search metaheuristic which iterates between the routing and scheduling components of the problem. Computational experiments indicate that using customer-related presence data significantly can yield savings as large as 40% in system-wide costs compared with those of traditional vehicle routing solutions.

电子商务中在线订单的最近增加导致了物流方面的挑战，例如命中率低（成功交付的比例）。我们考虑了考虑客户存在概率数据的最后一英里的车辆路线和调度问题。目的是通过在计划阶段同时考虑路由和调度决策，以降低因命中率低而导致的预期成本。我们对问题进行建模并通过自适应大邻域搜索元启发式方法进行解决，该方法在问题的路由和调度组件之间进行迭代。计算实验表明，与传统的车辆路由解决方案相比，大幅使用与客户相关的状态数据可以节省多达40％的系统范围成本。