大规模工业机器人车队共享受限的物理基础设施，导致车辆通行时间受安全间距、路口访问权、下游阻塞及站点争用等因素影响。本文以洁净室半导体制造厂中典型的天花板式物料搬运系统——天车搬运系统（Overhead Hoist Transport, OHT）为研究对象。静态最短路径路由无法刻画这些时变交通成本；而表格型Q路由（tabular Q-routing）虽可在线自适应，但其对每个“目标节点–动作”组合独立学习Q值，限制了稀疏访问路由场景间的信息共享，且初始行为易受不准确Q值估计的影响。为此，我们提出神经双Q路由（Neural Double Q-routing），以共享的状态–动作价值网络替代按目标索引的表格。该网络通过在混合仿真生成的路由轨迹上执行“剩余回报回归”（return-to-go regression）进行冷启动预训练，并进一步结合双Q更新、局部拥塞校正及事件分层结构化重放机制进行在线优化。在九组匹配的车队规模–到达率设置下（含100、150与200台OHT），所提框架相较表格型双Q路由将平均任务完成时间降低0.8%–8.8%。在全部六组150台与200台OHT设置中，其平均完成时间均为所有对比方法中最低；而在三组100台OHT设置中，Dijkstra算法仍表现最优。在九组设置中的八组中，其完成任务数与表格型双Q路由的差异控制在1%以内；八组设置中其95百分位完成时间亦有所下降。在两种匹配的启动场景下，离线初始化使完成任务数最多提升23%，尾部完成时间最多降低15%。

Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersection access, downstream blocking, and station contention. We study this problem in overhead hoist transport (OHT) systems, a representative ceiling-mounted material-handling system used in semiconductor fabs. Static shortest-path routing cannot account for these time-varying traffic costs, whereas tabular Q-routing adapts online but learns each destination--node--action value independently, limiting information sharing across sparsely visited routing contexts and making startup behavior sensitive to inaccurate value estimates. We propose Neural Double Q-routing, which replaces destination-indexed tables with a shared state--action value network. The network is warm-started through return-to-go regression on mixed simulator-generated routing trajectories and then refined online using Double-Q updates, local congestion correction, and event-stratified structured replay. Across nine matched fleet-size--arrival-rate settings with 100, 150, and 200 OHTs, the proposed framework reduces mean completion time relative to tabular Double Q-routing by $0.8\%$--$8.8\%$. It achieves the lowest mean completion time among all compared methods in the six 150- and 200-OHT settings, whereas Dijkstra remains best in the three 100-OHT settings. Completed-task counts remain within $1\%$ of tabular Double Q-routing in eight of nine settings, and 95th-percentile completion time decreases in eight settings. In two matched startup scenarios, offline initialization increases the number of completed tasks by up to $23\%$ and reduces tail completion time by up to $15\%$.