混合强化学习与启发式方法求解分层有向弧路由问题
Hybridising Reinforcement Learning and Heuristics for Hierarchical Directed Arc Routing Problems
AI总结：
本文针对分层有向容量约束弧路由问题（HDCARP）提出混合强化学习与启发式算法HRDA，利用强化学习动态引导局部搜索算子选择，在保持解质量的同时显著提升求解速度。
摘要：
分层有向容量约束弧路由问题（HDCARP）是容量约束弧路由问题（CARP）的扩展，其中图的弧根据优先级被划分为不同类别。这些类别的遍历顺序由优先约束或分层目标决定，从而形成两种不同的HDCARP变体。据我们所知，目前仅有一种数学启发式方法被提出用于这些变体，但其运行速度相对较慢，尤其在大规模实例上表现明显（Ha et al., 2024）。本文提出一种快速启发式方法，以高效应对HDCARP的计算挑战。此外，我们将强化学习（RL）融入启发式方法中，有效引导局部搜索算子的选择，从而形成一种混合算法。我们将该混合算法命名为面向有向弧路由的混合强化学习与启发式算法（HRDA）。该混合算法能够动态适应问题的变化，利用实时反馈，通过整合启发式方法来改进路由策略和解的质量。在人工实例上的大量计算实验表明，该混合方法在显著提升启发式方法速度的同时，并未降低解的质量。我们的源代码公开于：https://github.com/HySonLab/ArcRoute
英文摘要：
The Hierarchical Directed Capacitated Arc Routing Problem (HDCARP) is an extension of the Capacitated Arc Routing Problem (CARP), where the arcs of a graph are divided into classes based on their priority. The traversal of these classes is determined by either precedence constraints or a hierarchical objective, resulting in two distinct HDCARP variants. To the best of our knowledge, only one matheuristic has been proposed for these variants, but it performs relatively slowly, particularly for large-scale instances (Ha et al., 2024). In this paper, we propose a fast heuristic to efficiently address the computational challenges of HDCARP. Furthermore, we incorporate Reinforcement Learning (RL) into our heuristic to effectively guide the selection of local search operators, resulting in a hybrid algorithm. We name this hybrid algorithm as the Hybrid Reinforcement Learning and Heuristic Algorithm for Directed Arc Routing (HRDA). The hybrid algorithm adapts to changes in the problem dynamically, using real-time feedback to improve routing strategies and solution's quality by integrating heuristic methods. Extensive computational experiments on artificial instances demonstrate that this hybrid approach significantly improves the speed of the heuristic without deteriorating the solution quality. Our source code is publicly available at: https://github.com/HySonLab/ArcRoute
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