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