Optimizing Supply Chain Networks with the Power of Graph Neural Networks

• URL: http://arxiv.org/abs/2501.06221v1
• Date: Tue, 07 Jan 2025 02:31:24 GMT
• Title: Optimizing Supply Chain Networks with the Power of Graph Neural Networks
• Authors: Chi-Sheng Chen, Ying-Jung Chen,
• Abstract summary: Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data. This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset.
• Score: 0.0
• License:
• Abstract: Graph Neural Networks (GNNs) have emerged as transformative tools for modeling complex relational data, offering unprecedented capabilities in tasks like forecasting and optimization. This study investigates the application of GNNs to demand forecasting within supply chain networks using the SupplyGraph dataset, a benchmark for graph-based supply chain analysis. By leveraging advanced GNN methodologies, we enhance the accuracy of forecasting models, uncover latent dependencies, and address temporal complexities inherent in supply chain operations. Comparative analyses demonstrate that GNN-based models significantly outperform traditional approaches, including Multilayer Perceptrons (MLPs) and Graph Convolutional Networks (GCNs), particularly in single-node demand forecasting tasks. The integration of graph representation learning with temporal data highlights GNNs' potential to revolutionize predictive capabilities for inventory management, production scheduling, and logistics optimization. This work underscores the pivotal role of forecasting in supply chain management and provides a robust framework for advancing research and applications in this domain.

Related papers

• ReInc: Scaling Training of Dynamic Graph Neural Networks [6.1592549031654364]
  ReInc is a system designed to enable efficient and scalable training of Dynamic Graph Neural Networks (DGNNs) on large-scale graphs. We introduce key innovations that capitalize on the unique combination of Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) inherent in DGNNs.
  arXiv  Detail & Related papers  (2025-01-25T23:16:03Z)
• Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks [0.0]
  Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing. This work lays the groundwork for solving supply chain problems using GNNs, supported by conceptual discussions, methodological insights, and a comprehensive dataset.
  arXiv  Detail & Related papers  (2024-11-13T11:59:40Z)
• Applying graph neural network to SupplyGraph for supply chain network [0.0]
  Supply chain networks describe interactions between products, manufacture facilities, storages in the context of supply and demand of the products. This study assessed the supply chain dataset, SupplyGraph, with better clarity on analyses processes, data quality assurance, machine learning (ML) model specifications.
  arXiv  Detail & Related papers  (2024-08-23T23:42:18Z)
• SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks [0.0]
  Graph Neural Networks (GNNs) have gained traction across different domains such as transportation, bio-informatics, language processing, and computer vision. Supply chain networks are inherently graph-like in structure, making them prime candidates for applying GNN methodologies. A major setback in this approach lies in the absence of real-world benchmark datasets to facilitate the research and resolution of supply chain problems using GNNs.
  arXiv  Detail & Related papers  (2024-01-27T05:14:17Z)
• Adaptive Dependency Learning Graph Neural Networks [5.653058780958551]
  We propose a hybrid approach combining neural networks and statistical structure learning models to self-learn dependencies. We demonstrate significantly improved performance using our proposed approach on real-world benchmark datasets without a pre-defined dependency graph.
  arXiv  Detail & Related papers  (2023-12-06T20:56:23Z)
• Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs [94.44374472696272]
  We investigate NTKs and alignment in the context of graph neural networks (GNNs) Our results establish the theoretical guarantees on the optimality of the alignment for a two-layer GNN. These guarantees are characterized by the graph shift operator being a function of the cross-covariance between the input and the output data.
  arXiv  Detail & Related papers  (2023-10-16T19:54:21Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• Learning to Drop: Robust Graph Neural Network via Topological Denoising [50.81722989898142]
  We propose PTDNet, a parameterized topological denoising network, to improve the robustness and generalization performance of Graph Neural Networks (GNNs) PTDNet prunes task-irrelevant edges by penalizing the number of edges in the sparsified graph with parameterized networks. We show that PTDNet can improve the performance of GNNs significantly and the performance gain becomes larger for more noisy datasets.
  arXiv  Detail & Related papers  (2020-11-13T18:53:21Z)
• Data-Driven Learning of Geometric Scattering Networks [74.3283600072357]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2020-10-06T01:20:27Z)
• A Unified View on Graph Neural Networks as Graph Signal Denoising [49.980783124401555]
  Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. In this work, we establish mathematically that the aggregation processes in a group of representative GNN models can be regarded as solving a graph denoising problem. We instantiate a novel GNN model, ADA-UGNN, derived from UGNN, to handle graphs with adaptive smoothness across nodes.
  arXiv  Detail & Related papers  (2020-10-05T04:57:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.