AI 中文总结
研究稀疏非欧几里得网络上综合取送货问题，提出双通道图注意力（DCGA）强化学习框架，将网络可达性等分离到不同通道构建有效路线，实验表明其能实现秒级推理，在特定规模实例上提供最优解，是有效低延迟的路由和流量优化引擎。
AI 中文摘要
我们研究了稀疏非欧几里得网络上的综合取送货问题，该问题联合优化循环路由、货物流分配和跨周期服务。这些操作约束的紧密耦合创建了一个具有高度受限可行区域的复杂离散-连续决策空间。为克服计算挑战，我们提出了双通道图注意力（DCGA），这是一个端到端强化学习框架。DCGA将网络可达性和需求-服务逻辑分离到单独的图通道中，并使用模拟器耦合、约束告知解码器构建有效路线。在LinerLib基准测试上的实验表明，DCGA实现了秒级推理，并在特定规模以上的实例上提供了最优的解决方案质量，随着问题规模的增加，其相对于现有基线的优势显著扩大。广泛的稳定性和消融分析支持了我们的结果，表明这种结构感知学习方法为实际的路由和流量优化提供了一个有效、低延迟的引擎。
英文摘要
We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service. The tight coupling of these operational constraints creates a complex discrete-continuous decision space with highly restricted feasible regions. To overcome these computational challenges, we propose Double-Channel Graph Attention (DCGA), an end-to-end reinforcement learning framework. DCGA isolates network reachability and demand-service logic into separate graph channels and constructs valid routes using a simulator-coupled, constraint-informed decoder. Experiments on LinerLib benchmarks demonstrate that DCGA achieves seconds-level inference and delivers state-of-the-art solution quality on instances beyond a specific scale, with its advantage over existing baselines widening significantly as problem size increases. Supported by extensive stability and ablation analyses, our results demonstrate that this structure-aware learning approach provides an effective, low-latency engine for realistic routing-and-flow optimization.
Comments34 pages, 14 figures, and 10 tables
