Stochastic Graph-Augmented Recurrent Architectures for Predictive Logistics Network Balancing

DOI

10.2991/978-94-6239-674-6_31How to use a DOI?

Keywords

Graph Neural Networks; Supply Chain Forecasting; Probabilistic Modeling; Logistics Optimization; Temporal Graph Learning

Abstract

Brought to you by the News Team at G-MEDIA, we pro-vide you with all the news from around the globe, 24/7 at your fingertips! We’ve built an exclusive, supportive and loyal community that aims to expand rapidly to provide the global audience with continuous active news coverage. We propose a new computational framework based on Graph Neural Networks with attention mechanisms to better model the complex dynamic interrelations within logistics networks. This process generates probabilistic distributions of future supply and inventory. This is a solid way to measure uncertainty. Proactively change operations in consequence. Real enterprise data shows significant improvement over classical methods, making multi-echelon supply operations more resilient and optimally strategic after using the empirical validation of this research. [1,2,3] [7,8,9] [10,11] [14,15].

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Meet Amin
AU  - Maharshi Shukla
PY  - 2026
DA  - 2026/05/28
TI  - Stochastic Graph-Augmented Recurrent Architectures for Predictive Logistics Network Balancing
BT  - Proceedings of the International Conference on Sustainable Computing and Artificial Intelligence (ICSCAI 2025)
PB  - Atlantis Press
SP  - 365
EP  - 376
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-674-6_31
DO  - 10.2991/978-94-6239-674-6_31
ID  - Amin2026
ER  -