2014/04/26 by Thibaut Vidal, Maria Battarra, Vidal, Thibaut +6
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Transportation and Mobility Innovations #Urban and Freight Transport Logistics #Vehicle Routing Optimization Methods #cs.AI
paper · pdf · doi:10.48550/arxiv.1404.6696
Working Paper, MIT -- 22 pages
arxiv created 2014/04/26 · openalex publication_date 2014/04/26 · arxiv updated 2014/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.