Last-mile Robot Deliveries with Access Restrictions
Shaohua Yu  1@  , Ann Melissa Campbell  2@  , Jan Fabian Ehmke  3@  , Jakob Puchinger  4, 5@  
1 : School of Intelligent Manufacturing, Nanjing University of Science and Technology
2 : The University of Iowa, Department of Management Sciences, Tippie College of Business
Iowa City, IA 52242 -  États-Unis
3 : Otto-von-Guericke University Magdeburg
4 : Métis Lab
EM Normandie Business School - Paris Campus
5 : Laboratoire Génie Industriel
CentraleSupélec, Université Paris-Saclay

In recent years, the improvement of quality of life in cities through the reduction of local
pollutant emissions and traffic has been an increasingly important topic for city planners and
researchers. This leads to classical practices such as using vans for deliveries to be restricted and
other modes preferred in some areas such as campuses, pedestrian zones or inner-city districts.
Recent technological developments and experiments point to the potential of automated last-
mile deliveries using small battery-powered robots. We therefore investigate a two-echelon
hybrid van-based robot delivery system for last-mile logistics operations.
In existing research, using robots for second-echelon delivery has been considered. For example,
a two-echelon delivery model (2EVRP) is proposed in [1]. At the first echelon, vans transport
packages from a large depot to small local robot hubs. At the second echelon, robots deliver
items to customers. A van-based robot delivery model (VRD) concept with time windows is
studied in [2]. Here, robots are transported in the vans to perform second-level route deliveries.
According to a sensitivity analysis performed in [3], each of these delivery models have their
advantages. Depending on fixed costs of vans, robots or satellites, as well as other factors, one
of the models may perform better than the other.
We propose a hybrid delivery model to select the optimal distribution strategy considering
conventional vans, inner city hubs with robots, and delivery vans carrying robots, potentially
improving distribution efficiency. We model the problem as mixed-integer program and propose
an adaptive large neighborhood search (ALNS) to solve large instances efficiently. We conduct
computational experiments to evaluate how our proposed model performs and how different
characteristics (size, time) of the access restrictions impact its performance. We also evaluate
how different characteristics impact solution structure.


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