arXiv cs.AI· Jun Zhang, Wei-Jie Yu, Tong Qian, Eui Jeong Kwak, Sang-Woon Jeon, Hua Ding, Qijia Jiang, Hu Jin, Haewoon Nam, Changjun Zhou·· 1 天前AI 评分18
基于矩阵蚁群搜索与批量解码的分层补货路径规划：韩国城市数据风格化案例研究
Matrix-Based Ant Colony Search with Batched Decoding for Hierarchical Replenishment Routing: A Stylized Case Study Based on Korean City Data
AI 导读
研究提出 MACO——基于矩阵的蚁群搜索结合批量局部搜索，用于单车辆分层补货路径问题，在韩国人口与地理数据构建的 100 节点基准上平均总距离 3081.7 km。相同局部搜索下遗传算法为 3074.9 km（无显著差异），MAX-MIN 蚁群系统为 3142.2 km。消融显示批量局部搜索是解质量的主要贡献者，负载感知转移因子无显著增益。
正文 · 原文
Abstract:We study a stylized single-vehicle replenishment-routing problem in which the cities of a national logistics network are both demand points and supply points: a vehicle of limited capacity delivers to every city once, and when its load is insufficient for the next city it must detour to a city already served that can still supply goods. A 100-node benchmark is built from Korean population and geographic data, with the cities divided into 10 levels; the per-visit supply coefficient and the allowed number of stops decrease geometrically with the level. Building on matrix-based ant colony optimization, we keep the states of the whole colony in arrays, couple route construction with a fixed greedy replenishment-decoding policy, and evaluate the candidate moves of a local search in batches. With frozen parameters and 30 runs on seeds never used for tuning, the matrix-based ant colony search with the batch local search (MACO) reaches a mean total distance of 3081.7 km; a genetic algorithm equipped with the same local search at the same frequency reaches 3074.9 km (no significant difference), and a MAX-MIN ant system with the same local search 3142.2 km. Ablation identifies the batch local search as the main contributor to solution quality; a load-aware transition factor gives no significant gain. Within the parameter ranges tested, raising the supply coefficients of the cities below the top level shortens the route more than relaxing the stop limits, which are not binding here.
| Comments: | 9 pages, 6 figures, 4 tables; code, data and raw records in the ancillary files |
| Subjects: | Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE); Optimization and Control (math.OC) |
| Cite as: | arXiv:2611.15338 [cs.NE] |
| (or arXiv:2611.15338v1 [cs.NE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15338 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jun Zhang [view email]
[v1]
Wed, 11 Nov 2026 11:16:36 UTC (3,662 KB)
来源：arXiv cs.AI · arxiv.org