  The vehicle routing problem is one of the most studied combinatorial
optimization topics, due to its practical importance and methodological
interest. Yet, despite extensive methodological progress, many recent studies
are hampered by the limited access to simple and efficient open-source solution
methods. Given the sophistication of current algorithms, reimplementation is
becoming a difficult and time-consuming exercise that requires extensive care
for details to be truly successful. Against this background, we use the
opportunity of this short paper to introduce a simple -- open-source --
implementation of the hybrid genetic search (HGS) specialized to the
capacitated vehicle routing problem (CVRP). This state-of-the-art algorithm
uses the same general methodology as Vidal et al. (2012) but also includes
additional methodological improvements and lessons learned over the past decade
of research. In particular, it includes an additional neighborhood called SWAP*
which consists in exchanging two customers between different routes without an
insertion in place. As highlighted in our study, an efficient exploration of
SWAP* moves significantly contributes to the performance of local searches.
Moreover, as observed in experimental comparisons with other recent approaches
on the classical instances of Uchoa et al. (2017), HGS still stands as a
leading metaheuristic regarding solution quality, convergence speed, and
conceptual simplicity.
