Efficient Delivery Geometry
Thibaut explains how graph neural networks (GNN) outperform simpler regression models by accurately predicting district costs based on geometry and depot positioning. He highlights that while compact districts may seem ideal, practical delivery routes often require more nuanced shapes, like petal-like configurations, to minimize costs. Through emergent learning, GNNs can adapt and identify these effective structures, although they require extensive training data for optimal performance.In this clip
From this podcast
Data Skeptic
Optimizing Supply Chains with GNN
Related Questions