  Last-mile delivery in the logistics chain contributes to emissions and
increased congestion. Crowd-shipping is a sustainable and low-cost alternative
to traditional delivery, but relies heavily on the availability of occasional
couriers. In this work, we propose a hub-based crowd-shipping system that aims
to attract sufficient potential crowd-shippers to serve a large portion of the
demand for small parcels. While small-scale versions of this problem have been
recently addressed, a scaling to larger instances significantly complexifies
the problem. A heuristic approach based on continuum approximation is designed
to evaluate the quality of a potential set of hub locations. By combining an
efficient and accurate approximation method with a large neighborhood search
heuristic, we are able to efficiently find a good set of hub locations, even
for large scale networks. Furthermore, on top of determining good hub
locations, our methods allow to identify the expected number of delivered
parcels in every region, which can be used to design a smart dynamic assignment
strategy. A case study on the Washington DC network shows that hubs are built
at locations that are both geographically central, but most importantly are
popular origins for crowd-shippers. The optimal number of hubs is mainly
dependent on the marginal number of parcels that can be served by
crowd-shippers from a specific hub, relative to the costs involved in opening
that hub. The performance of our algorithm is close to that of a
simulation-optimization algorithm, yet being up to 25 times faster. Thereby,
the results show a dynamic assignment policy based on continuum approximation
estimates outperforms existing assignment strategies.
