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A Constraint-Aware Generative Framework for Synthetic Origin-Destination Demand in Logistics Networks

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

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 d

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