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In the wake of e-commerce and its successful diffusion in most commercial activities, last-mile distribution causes more and more trouble in urban areas all around the globe. Growing parcel volumes to be delivered toward customer homes increase the number of delivery vans entering the city centers and thus add to congestion, pollution, and negative health impact. Therefore, it is anything but surprising that in recent years many novel delivery concepts on the last mile have been innovated. Among the most prominent are unmanned aerial vehicles (drones) and autonomous delivery robots taking over parcel delivery. This paper surveys established and novel last-mile concepts and puts special emphasis on the decision problems to be solved when setting up and operating each concept. To do so, we systematically record the alternative delivery concepts in a compact notation scheme, discuss the most important decision problems, and survey existing research on operations research methods solving these problems. Furthermore, we elaborate promising future research avenues.

Last mile deliveries with unmanned aerial vehicles (also denoted as drones) are seen as one promising idea to reduce excessive road traffic. To overcome the difficulties caused by the comparatively short operating ranges of drones, an innovative concept suggests to apply trucks as mobile landing and take‐off platforms. In this context, the paper on hand schedules the delivery to customers by drones for given truck routes. Given a fixed sequence of stops constituting a truck route and a set of customers to be supplied, we aim at a drone schedule (i.e., a set of trips each defining a drone's take‐off and landing stop and the customer serviced), such that all customers are supplied and the total duration of the delivery tour is minimized. We differentiate whether multiple drones or just a single one are placed on a truck and whether or not take‐off and landing stops have to be identical. We provide an analysis of computational complexity for each resulting subproblem, introduce efficient mixed‐integer programs, and compare all cases with regard to their potential of reducing the delivery effort on the last mile.

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