Using Bayesian Graph Neural Networks for resilient supply chain routing under disruption

Short summary

This article introduces Probabilistic Graph Neural Networks (PGNNs) for resilient circular manufacturing supply chains during mission-critical recovery windows. It argues deterministic GNNs fail under real-world disruptions and proposes Bayesian GNN layers that output probability distributions instead of point estimates. A code implementation using PyTorch, PyTorch Geometric, and torchbnn demonstrates a Bayesian graph convolutional layer for predicting material flow success under uncertainty.

• •Deterministic GNNs are brittle under supply chain disruptions; Bayesian GNNs model uncertainty explicitly
• •PGNNs replace deterministic message passing with Bayesian layers for distributional predictions
• •Implementation uses PyTorch Geometric and torchbnn for probabilistic graph convolution

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