  The Clustered Vehicle Routing Problem (CluVRP) is a variant of the
Capacitated Vehicle Routing Problem in which customers are grouped into
clusters. Each cluster has to be visited once, and a vehicle entering a cluster
cannot leave it until all customers have been visited. This article presents
two alternative hybrid metaheuristic algorithms for the CluVRP. The first
algorithm is based on an Iterated Local Search algorithm, in which only
feasible solutions are explored and problem-specific local search moves are
utilized. The second algorithm is a Hybrid Genetic Search, for which the
shortest Hamiltonian path between each pair of vertices within each cluster
should be precomputed. Using this information, a sequence of clusters can be
used as a solution representation and large neighborhoods can be efficiently
explored by means of bi-directional dynamic programming, sequence
concatenations, by using appropriate data structures. Extensive computational
experiments are performed on benchmark instances from the literature, as well
as new large scale ones. Recommendations on promising algorithm choices are
provided relatively to average cluster size.
