Hybrid Metaheuristics for the Clustered Vehicle Routing Problem

TL;DR

This paper introduces two hybrid metaheuristic algorithms for the Clustered Vehicle Routing Problem, improving solution efficiency through problem-specific local search and dynamic programming techniques, validated on benchmark and large-scale instances.

Contribution

It proposes novel hybrid metaheuristics combining local search and genetic algorithms with dynamic programming for CluVRP, enhancing solution quality and computational efficiency.

Findings

Algorithms perform well on benchmark instances.

Recommendations vary with average cluster size.

Hybrid methods outperform traditional approaches.

Abstract

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.