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| Format: | Preprint |
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2026
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| Online Access: | https://arxiv.org/abs/2601.21660 |
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| _version_ | 1866915761116676096 |
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| author | Zhao, Jingyang Xiao, Mingyu |
| author_facet | Zhao, Jingyang Xiao, Mingyu |
| contents | The capacitated vehicle routing problem (CVRP) is one of the most extensively studied problems in combinatorial optimization. In this problem, we are given a depot and a set of customers, each with a demand, embedded in a metric space. The objective is to find a set of tours, each starting and ending at the depot, operated by the capacititated vehicle at the depot to serve all customers, such that all customers are served, and the total travel cost is minimized. We consider the unplittable variant, where the demand of each customer must be served entirely by a single tour. Let $α$ denote the current best-known approximation ratio for the metric traveling salesman problem. The previous best approximation ratio was $α+1+\ln 2+δ<3.1932$ for a small constant $δ>0$ (Friggstad et al., Math. Oper. Res. 2025), which can be further improved by a small constant using the result of Blauth, Traub, and Vygen (Math. Program. 2023). In this paper, we propose two improved approximation algorithms. The first algorithm focuses on the case of fixed vehicle capacity and achieves an approximation ratio of $α+1+\ln\bigl(2-\frac{1}{2}y_0\bigr)<3.0897$, where $y_0>0.39312$ is the unique root of $\ln\bigl(2-\frac{1}{2}y\bigr)=\frac{3}{2}y$. The second algorithm considers general vehicle capacity and achieves an approximation ratio of $α+1+y_1+\ln\left(2-2y_1\right)+δ<3.1759$ for a small constant $δ>0$, where $y_1>0.17458$ is the unique root of $\frac{1}{2} y_1+ 6 (1-y_1)\bigl(1-e^{-\frac{1}{2} y_1}\bigr) =\ln\left(2-2y_1\right)$. Both approximations can be further improved by a small constant using the result of Blauth, Traub, and Vygen (Math. Program. 2023). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21660 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improved Approximations for the Unsplittable Capacitated Vehicle Routing Problem Zhao, Jingyang Xiao, Mingyu Data Structures and Algorithms The capacitated vehicle routing problem (CVRP) is one of the most extensively studied problems in combinatorial optimization. In this problem, we are given a depot and a set of customers, each with a demand, embedded in a metric space. The objective is to find a set of tours, each starting and ending at the depot, operated by the capacititated vehicle at the depot to serve all customers, such that all customers are served, and the total travel cost is minimized. We consider the unplittable variant, where the demand of each customer must be served entirely by a single tour. Let $α$ denote the current best-known approximation ratio for the metric traveling salesman problem. The previous best approximation ratio was $α+1+\ln 2+δ<3.1932$ for a small constant $δ>0$ (Friggstad et al., Math. Oper. Res. 2025), which can be further improved by a small constant using the result of Blauth, Traub, and Vygen (Math. Program. 2023). In this paper, we propose two improved approximation algorithms. The first algorithm focuses on the case of fixed vehicle capacity and achieves an approximation ratio of $α+1+\ln\bigl(2-\frac{1}{2}y_0\bigr)<3.0897$, where $y_0>0.39312$ is the unique root of $\ln\bigl(2-\frac{1}{2}y\bigr)=\frac{3}{2}y$. The second algorithm considers general vehicle capacity and achieves an approximation ratio of $α+1+y_1+\ln\left(2-2y_1\right)+δ<3.1759$ for a small constant $δ>0$, where $y_1>0.17458$ is the unique root of $\frac{1}{2} y_1+ 6 (1-y_1)\bigl(1-e^{-\frac{1}{2} y_1}\bigr) =\ln\left(2-2y_1\right)$. Both approximations can be further improved by a small constant using the result of Blauth, Traub, and Vygen (Math. Program. 2023). |
| title | Improved Approximations for the Unsplittable Capacitated Vehicle Routing Problem |
| topic | Data Structures and Algorithms |
| url | https://arxiv.org/abs/2601.21660 |