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·· 3 小时前AI 评分26
基于韩国城市数据的矩阵式蚁群搜索与批量解码分层补货路径研究
Matrix-Based Ant Colony Search with Batched Decoding for Hierarchical Replenishment Routing: A Stylized Case Study Based on Korean City Data
AI 导读
一项研究用矩阵式蚁群算法（MACO）求解单车分层补货路径问题，基于韩国人口与地理数据构建 100 节点、10 层级的基准测试。冻结参数并跑 30 次未见种子后，MACO 平均总距离为 3081.7 km，同频率局部搜索的遗传算法为 3074.9 km（无显著差异），MAX-MIN 蚁群系统为 3142.2 km。
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