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The researchers proposed a constraint-aware generation framework for logistics networks, aimed at synthesizing start and end requirements. This framework models requirements as conditional distributions of destinations from a given origin, and incorporates operational guidance directly into the generation targets through differentiable constraints. It also uses flexible conditional mechanisms to support different operational scenarios and network configuration variations. Experimental validation based on the conditional generation model showed that this method achieved 16% higher performance compared to graph neural network baselines, achieving a 87% operational compliance rate and efficiently adapting to cold starts. This framework is suitable for tasks such as capacity planning, network design evaluation, and routing optimization.
研究人员提出了一种用于物流网络中合成起讫需求的约束感知生成框架。该框架将需求建模为给定每个原点的目的地的条件分布，通过可微约束将运营指导直接纳入生成目标，并采用灵活的条件机制支持不同运营场景及网络配置演变。基于条件生成模型在工业真实履约和运输网络上的实验验证显示，该方法相比图神经网络基线提升了 16%，实现了 87% 的运营合规率，并能高效适应冷启动，适用于容量规划、网络设计评估和路由优化。