The latency location routing problem integrates the facility location problem and the multi-depot cumulative capacitated vehicle routing problem. This problem involves making simultaneous decisions about depot locations and vehicle routes to serve customers while aiming to minimize the sum of waiting (arriving) times for all customers. To address this computationally challenging problem, we propose a reinforcement learning guided hybrid evolutionary algorithm following the framework of the memetic algorithm. The proposed algorithm relies on a diversity-enhanced multi-parent edge assembly crossover to build promising offspring and a reinforcement learning guided variable neighborhood descent to determine the exploration order of multiple neighborhoods. Additionally, strategic oscillation is used to achieve a balanced exploration of both feasible and infeasible solutions. The competitiveness of the algorithm against state-of-the-art methods is demonstrated by experimental results on the three sets of 76 popular instances, including 51 improved best solutions (new upper bounds) for the 59 instances with unknown optima and equal best results for the remaining instances. We also conduct additional experiments to shed light on the key components of the algorithm.

翻译：延迟位置路由问题整合了设施选址问题和多车场累积容量车辆路径问题。该问题需同时决策车场位置和车辆路径以服务客户，目标是最小化所有客户的等待（到达）时间总和。针对这一计算挑战性问题，我们遵循模因算法框架，提出一种融合强化学习的混合进化算法。该算法采用多样性增强的多父代边缘装配交叉算子生成优质子代，并通过强化学习引导的可变邻域下降机制确定多个邻域的探索顺序。此外，利用策略振荡在可行解与不可行解之间实现均衡探索。在包含76个广泛使用实例的三组测试集上的实验结果表明，本算法相较于现有最优方法具有竞争力：在59个未知最优解的实例中获得51个改进的最优解（新上界），其余实例均达到已知最优结果。我们进一步通过消融实验揭示了算法核心组件的关键作用。