---
id: 20260302-T0-19
title: "基于强化学习的min-max多旅行商问题优化"
title_en: "Reinforcement Learning Optimizes Min-Max TSP"
url: https://ai.daily.yangsir.net/daily/20260302-T0-19
issue_date: 2026-03-02
publish_date: 2026-03-02T05:00:00.000Z
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2602.23579
---

# 基于强化学习的min-max多旅行商问题优化

该研究提出一种强化学习方法解决min-max多旅行商问题。通过构建、合并、求解和适应四阶段框架，有效优化多路径规划。实验显示，该方法将最长路径长度缩短15%，同时保持整体效率。适用于物流配送、车辆路径规划等需要平衡负载分配的场景。

## English Version

**Reinforcement Learning Optimizes Min-Max TSP**

This research proposes a reinforcement learning approach to solve the min-max multi-traveling salesman problem. A four-stage framework of construction, merging, solving, and adaptation effectively optimizes multi-path planning. Experiments show this method reduces the longest path length by 15% while maintaining overall efficiency. It applies to logistics and vehicle routing scenarios requiring balanced load distribution.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2602.23579)

**详情页**：https://ai.daily.yangsir.net/daily/20260302-T0-19

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