Impact of Autonomous Vehicle Assisted Last-Mile Delivery in Urban to Rural Settings

Abstract

We seek to determine in what geographies autonomous vehicle assisted delivery is most valuable for last-mile delivery. To build insights across urban-to-rural settings, we conduct a case study by generating test instances that reflect real-world geographies. We integrate real-world data for these instances, including driving and walking times, as well as obstacles, such as one-way streets, and their impact on last-mile delivery. We model the capacitated autonomous vehicle assisted delivery problem as an integer program on a general graph. To solve this model on realistically sized instances, we exploit the structure of the optimal solution to develop a number of preprocessing techniques to reduce the large number of variables present in the generic problem. We also introduce valid inequalities that raise the lower bound and reduce the size of the branch-and-bound tree. Autonomous vehicle assisted delivery reduces the completion time of the delivery tour and provides the most cost-effective business model in all customer geographies. In particular, a delivery person saves more time in urban environments than in rural environments. These savings are the result of both a reduction in the time to park but also in the amount of walking that the delivery person does. This increased productivity could reduce fleet size and ultimately the number of vehicles on the road. These conclusions support businesses with urban deliveries considering investment in this technology. However, higher savings in rural environments may be achieved by reducing the loading time.