Problem
Research questions and friction points this paper is trying to address.
Graph Neural Networks
Metamodels
Supply Chain Optimization
Structural Optimization
Parametric Optimization
On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions
🤖 AI Summary
Traditional surrogate models struggle to simultaneously optimize both the structure and parameters of supply chains and lack generalization capabilities for graph-structured systems. This work presents the first systematic exploration of graph neural networks (GNNs) as surrogate models for supply chain design, introducing a programmatically generated supply chain graph dataset and leveraging a custom simulation library, SupplyNetPy, to produce large-scale training data for end-to-end differentiable modeling. The proposed approach enables performance prediction at both node and network levels, effectively balancing predictive accuracy and computational cost. By supporting gradient-based topology optimization and sensitivity analysis, this method establishes a foundation for novel design-space exploration strategies, significantly enhancing the efficiency of supply chain configuration and optimization.
📝 Abstract
Graph Neural Networks (GNNs) have emerged as a powerful, differentiable class of learning models for graph-structured systems. Their ability to generalize across topologies opens the prospect of a surrogate for combined structural and parametric optimization, which classical metamodels cannot offer. Supply chains are a natural target, yet the use of GNN surrogates for supply chain problems is largely unexplored. This paper lays the foundation, presents initial steps, and discusses key research directions. As a foundation, we formulate the problem and create a large public training dataset of programmatically generated supply chain graphs with input parameters and steady-state performance metrics obtained using our SupplyNetPy simulation library. As initial steps, we explore GNN architectures that work well as surrogates for node- and network-level predictions, and analyze their accuracy-compute trade-off against simulation. Most importantly, we outline the exciting directions this opens, namely gradient-based optimization over topology, fast design-space exploration, and sensitivity analysis.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Graph Neural Networks
Metamodels
Supply Chain Optimization
Topology Optimization
Design-Space Exploration
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