Article
Version 1
This version is not peer-reviewed
Graph Neural Network for Daily Supply Chain Problems
Version 1
: Received: 29 September 2024 / Approved: 30 September 2024 / Online: 30 September 2024 (06:07:57 CEST)
How to cite: Raul Gonzalez, F. Graph Neural Network for Daily Supply Chain Problems. Preprints 2024, 2024092376. https://doi.org/10.20944/preprints202409.2376.v1 Raul Gonzalez, F. Graph Neural Network for Daily Supply Chain Problems. Preprints 2024, 2024092376. https://doi.org/10.20944/preprints202409.2376.v1
Abstract
In this paper, we explore the theoretical foundation of Graph Neural Network (GNN) model and apply it to various traditional supply chain logistics problem. In particular, we study the Route Optimization Problem, Demand Forecasting Problem, Risk Assessment and Anomaly Detection, Supplier Section and Procurement Optimization, Inventory Optimization Problem, and Green Supply Chain Problem. The study helps us the expand the use the GNN and gives daily supply chain management a boost.
Keywords
supply chain; graph neural network; machine learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
