A multidepot capacitated vehicle routing problem aims to serve customers’ demands using a fleet of capacitated vehicles located in multiple depots, such that the total travel cost of the vehicles is minimized. We study a variant of this problem, the k-depot split delivery vehicle routing problem (or k-DSDVRP in short), for the situation where each customer’s demand can be served by more than one vehicle, and the total number of depots, denoted by [Formula: see text], is a fixed constant. This is a challenging problem with broad applications in the logistics industry, for which no constant ratio approximation algorithm is known. We develop a new approximation algorithm for the k-DSDVRP, ensuring an approximation ratio of [Formula: see text] and a polynomial running time for any fixed constant [Formula: see text]. To achieve this, we propose a novel solution framework based on a new relaxation of the problem, a cycle splitting procedure, and a vehicle assignment procedure. To further enhance its efficiency for practical usage, we adapt the newly developed approximation algorithm to a heuristic, which runs in polynomial time even when k is arbitrarily large. Experimental results show that this heuristic outperforms a commercial optimization solver and a standard vehicle routing heuristic. Moreover, our newly proposed solution framework can be applied to developing new constant ratio approximation algorithms for several other variants of the k-DSDVRP with [Formula: see text] being a fixed constant. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971177, 71725001, U1811462], Research Grants Council of Hong Kong SAR, China [Grant 15221619], and Guangdong Basic and Applied Basic Research Foundation [Grant 2023A1515030260]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0193 . The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0193 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0193 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Abstract In this research, we study the capacitated traveling salesman problem with pickup and delivery (CTSPPD) on a tree, which aims to determine the best route for a vehicle with a finite capacity to transport amounts of a product from pickup points to delivery points on a tree network, such that the vehicle's total travel distance is kept to a minimum. It has several applications in logistics and is known to be NP‐hard. We develop a 2‐approximation algorithm that is a significant improvement over the best constant approximation ratio of 5 derived from existing CTSPPD literature. Computational results show that the proposed algorithm also achieves good average performance over randomly generated instances. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 63(2), 179–195 2014