Version 1
: Received: 4 October 2024 / Approved: 4 October 2024 / Online: 5 October 2024 (16:56:43 CEST)
How to cite:
Cvancara, C. Graph Neural Network Based Learning for Facility Location Optimization. Preprints2024, 2024100329. https://doi.org/10.20944/preprints202410.0329.v1
Cvancara, C. Graph Neural Network Based Learning for Facility Location Optimization. Preprints 2024, 2024100329. https://doi.org/10.20944/preprints202410.0329.v1
Cvancara, C. Graph Neural Network Based Learning for Facility Location Optimization. Preprints2024, 2024100329. https://doi.org/10.20944/preprints202410.0329.v1
APA Style
Cvancara, C. (2024). Graph Neural Network Based Learning for Facility Location Optimization. Preprints. https://doi.org/10.20944/preprints202410.0329.v1
Chicago/Turabian Style
Cvancara, C. 2024 "Graph Neural Network Based Learning for Facility Location Optimization" Preprints. https://doi.org/10.20944/preprints202410.0329.v1
Abstract
This paper investigates the application of Graph Neural Networks (GNNs) in solving the facility location decision problem. By adapting the learning objectives and structure of GNNs, this research bridges the gap between traditional optimization approaches and modern machine learning techniques. The proposed method demonstrates improved decision-making capabilities, providing promising results for facility location optimization. Additionally, potential future research directions are outlined, highlighting areas where GNNs can further enhance decision-making processes in complex supply chain networks. Hybrid GNN and Mixed Integer Programming solutions have been proposed.
Keywords
Deep Learning; Graph Neural Network; Facility Location; Mixed Integer Programming
Subject
Computer Science and Mathematics, Computer Science
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.
