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Research questionHow can synthetic origin-destination demand adapt to changing logistics topologies while remaining operationally feasible?Historical demand does not reveal how flows should change when a logistics network is reconfigured or demand conditions shift. Generated origin-destination patterns must remain realistic while satisfying operational constraints.
AI
Machine Learning
Research Paper
Statistical Machine Learning
Technology
Latest papersRecent research connected to this question, newest first.A Constraint-Aware Generative Framework for Synthetic Origin-Destination Demand in Logistics NetworksThe source addresses conditional synthetic demand generation in hierarchical fulfillment and transportation networks, with adaptation to evolving configurations and cold-start scenarios. Evidence comes from industrial real-network experiments reporting improvements over graph neural network baselines and 87% operational compliance.research paper · Sep 3, 2026
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