  In this paper, we propose a new dataset of Capacitated Vehicle Routing
Problem instances which are up to two orders of magnitude larger than those in
the currently used benchmarks. Despite these sizes might not have an immediate
application to real-world logistic scenarios, we believe they could foster
fresh new research efforts on the design of effective and efficient algorithmic
components for routing problems. We provide computational results for such
instances by running FILO2, an adaptation of the FILO algorithm proposed in
Accorsi and Vigo (2021), designed to handle extremely large-scale CVRP
instances. Solutions for such instances are obtained by using an a standard
personal computer in a considerably short computing time, thus showing the
effectiveness of the acceleration and pruning techniques already proposed in
FILO. Finally, results of FILO2 on well-known literature instances show that
the newly introduced changes improve the overall scalability of the approach
with respect to the previous FILO design.
