用图神经网络做供应链优化的代理模型，可加速设计与敏感性分析。
On the Potential of Graph Neural Networks as Metamodels for Supply Chain Optimization: Dataset, Architectures, and Directions
- 构建供应链图数据集，用仿真生成参数与性能指标。
- GNN在节点和网络层级预测上准确率高，计算效率优于仿真。
- 支持拓扑梯度优化，适合快速探索设计方案。
图神经网络（GNN）作为一类可微分学习模型，在图结构系统中表现出强大能力，其跨拓扑泛化特性为联合结构与参数优化提供了传统代理模型无法实现的可能。供应链是理想应用场景，但该方向仍处于探索阶段。本文提出基础框架，构建了大规模公开训练数据集，通过SupplyNetPy仿真库生成带输入参数与稳态性能指标的程序化供应链图。初步探索了适用于节点级与网络级预测的GNN架构，并分析其与仿真的精度-计算权衡。最重要的是，指出了未来关键方向：基于梯度的拓扑优化、快速设计空间探索与敏感性分析。
原文摘要 · Abstract (English)
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.
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