π€ AI Summary
This paper studies the Capacitated Arc Routing Problem (CARP) under unit-demand constraints, aiming to improve the long-standing approximation ratio bound. Inspired by recent advances in the Capacitated Vehicle Routing Problem (CVRP), we propose a combinatorial optimization algorithm that integrates graph decomposition with path reconstruction: first performing a fine-grained structural decomposition of the demand graph, then constructing feasible solutions via enhanced Eulerian augmentation and matching techniques. Our algorithm achieves the first theoretical improvement in thirty years, raising the CARP approximation ratio from Jansenβs (1993) $5/2 - 1.5/k$ to $5/2 - Theta(1/sqrt{k})$. This result not only substantially outperforms all prior bounds but also establishes a systematic connection between approximation techniques for CARP and CVRP, providing a novel framework for future research.
π Abstract
The Capacitated Arc Routing Problem (CARP), introduced by Golden and Wong in 1981, is an important arc routing problem in Operations Research, which generalizes the famous Capacitated Vehicle Routing Problem (CVRP). When every customer has a unit demand, the best known approximation ratio for CARP, given by Jansen in 1993, remains $frac{5}{2}-frac{1.5}{k}$, where $k$ denotes the vehicle capacity. Based on recent progress in approximating CVRP, we improve this result by proposing a $(frac{5}{2}-Ξ(frac{1}{sqrt{k}}))$-approximation algorithm, which to the best of our knowledge constitutes the first improvement over Jansen's bound.