AI 中文总结
该研究针对物流网络合成需求生成的约束适配问题，提出考虑约束的条件生成框架，经工业数据验证较基线方法提升16%，可用于容量规划等场景。
AI 中文摘要
大规模物流网络需要合成数据生成能力，以支持网络重构、需求冲击等新条件下的场景规划。现有方法主要依赖历史观测数据，无法生成适应网络拓扑变化且满足运营约束的需求模式。本文提出一种用于分层物流网络合成起讫点需求生成的考虑约束的条件生成框架，该框架将需求建模为每个起点对应的目的地条件分布，实现拓扑感知的合成，兼具拓扑真实性与运营可行性。运营指导通过可微约束直接融入生成目标，灵活的条件机制支持各类运营场景及对动态网络配置的适配。本文基于条件生成模型实例化该框架，在工业实际履约与运输网络上的实验验证显示，该框架较图神经网络基线方法提升16%，运营合规率达87%，且具备高效的冷启动适配能力，可应用于容量规划、网络设计评估及路径优化。
英文摘要
Large-scale logistics networks require synthetic data generation capabilities to support scenario-based planning under novel conditions-such as network reconfiguration and demand shocks. Existing approaches, which rely primarily on historical observations, lack the ability to generate demand patterns that adapt to changes in network topology while respecting operational constraints. We propose a constraint-aware conditional generative framework for synthetic origin-destination demand generation in hierarchical logistics networks. The framework models demand as a conditional distribution over destinations given each origin, enabling topology-aware synthesis that is both topologically realistic and operationally feasible. Operational guidance is incorporated directly into the generative objective via differentiable constraints, while a flexible conditioning mechanism supports various operational contexts and adaptation to evolving network configurations. We instantiate the proposed framework based on a conditional generative model. Experimental validation on industrial real fulfillment and transportation network demonstrates 16% improvement over graph neural network baselines, 87% operational compliance, and efficient cold-start adaptation, enabling applications in capacity planning, network design evaluation, and routing optimization.
Comments13 pages
