发表机构
School of Mathematics and Computer Science, Indian Institute of Technology Goa（印度理工学院果阿分校数学与计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文探讨图神经网络在供应链优化中的潜力，创建大型公共训练数据集，探索适用于节点和网络级预测的GNN架构并分析其与模拟的精度-计算权衡，还概述了基于梯度的拓扑优化等关键研究方向。
AI 中文摘要
图神经网络（GNN）已成为用于图结构系统的强大且可微的学习模型类别。其跨拓扑的泛化能力为结构与参数组合优化提供了替代方案，这是传统元模型所不具备的。供应链是自然的应用对象，但GNN在供应链问题中的应用尚待探索。本文奠定基础、迈出初步步伐并讨论关键研究方向。作为基础，我们提出问题并创建了一个大型公共训练数据集。作为初步步骤，我们探索了适用于节点和网络级预测的GNN架构，并分析其与模拟的精度-计算权衡。最重要的是，我们概述了由此开启的激动人心的方向，即基于梯度的拓扑优化、快速设计空间探索和灵敏度分析。
英文摘要
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.
Comments14 pages, 5 figures
