Technical Note: Split Algorithm in O(n) for the Capacitated Vehicle Routing Problem

Abstract

The Split algorithm is an essential building block of route-first cluster-second heuristics and modern genetic algorithms for vehicle routing problems. The algorithm is used to partition a solution, represented as a giant tour without occurrences of the depot, into separate routes with minimum cost. As highlighted by the recent survey of [Prins, Lacomme and Prodhon, Transport Res. C (40), 179-200], no less than 70 recent articles use this technique. In the vehicle routing literature, Split is usually assimilated to the search for a shortest path in a directed acyclic graph $\mathcal{G}$ and solved in $O(nB)$ using Bellman's algorithm, where $n$ is the number of delivery points and $B$ is the average number of feasible routes that start with a given customer in the giant tour. Some linear-time algorithms are also known for this problem as a consequence of a Monge property of $\mathcal{G}$. In this article, we highlight a stronger property of this graph, leading to a simple alternative algorithm in $O(n)$. Experimentally, we observe that the approach is faster than the classical Split for problem instances of practical size. We also extend the method to deal with a limited fleet and soft capacity constraints.

Cited by in corpus (5)

• Industrial and Tramp Ship Routing Problems: Closing the Gap for Real-Scale Instances
• Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood
• Heuristic Rectangle Splitting: Leveraging Single-Objective Heuristics to Efficiently Solve Multi-Objective Problems
• The Vehicle Routing Problem with Service Level Constraints
• BROUTE: a benchmark suite for the implementation of standard vehicle routing algorithms