用强化学习设计低成本海运航线，比传统方法更高效。
Liner Shipping Network Design with Reinforcement Learning
- 不用拆解问题，直接用强化学习优化航线设计。
- 在公开数据集上表现媲美传统方法，且训练后可跨实例泛化。
- 适合物流优化与算法设计领域的研究者参考。
本文提出一种新型强化学习框架，解决复杂的航运网络设计问题（LSNDP），旨在设计成本高效的海上运输路线。传统方法通常将问题分解为网络设计和多商品流等子问题，再使用启发式或大邻域搜索（LNS）求解。本文方法采用无模型强化学习直接优化网络设计，并结合基于启发式的多商品流求解器，在公开的LINERLIB基准上取得具有竞争力的结果。此外，该方法在仅用扰动实例训练后，仍能在原基准实例上生成高性能解，展现出良好的泛化能力。
原文摘要 · Abstract (English)
This paper proposes a novel reinforcement learning framework to address the Liner Shipping Network Design Problem (LSNDP), a challenging combinatorial optimization problem focused on designing cost-efficient maritime shipping routes. Traditional methods for solving the LSNDP typically involve decomposing the problem into sub-problems, such as network design and multi-commodity flow, which are then tackled using approximate heuristics or large neighborhood search (LNS) techniques. In contrast, our approach employs a model-free reinforcement learning algorithm on the network design, integrated with a heuristic-based multi-commodity flow solver, to produce competitive results on the publicly available LINERLIB benchmark. Additionally, our method also demonstrates generalization capabilities by producing competitive solutions on the benchmark instances after training on perturbed instances.
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