Delay Prediction in Supply Chains: A Hybrid Graph-Based and Machine Learning Approach - Prototype
Contributors
Description
Delay Prediction in Supply Chains: A Hybrid Graph-Based and Machine Learning Approach
This Zenodo record documents a research prototype developed by the author as part of a master’s thesis at the Department of Informatics, Systems, and Communication (DISCo) of the University of Milano‑Bicocca, in collaboration with Cefriel Innovation Center. The prototype implements the core software architecture described in the associated research and demonstrates its practical feasibility.
The software was built on AWS cloud infrastructure, utilizing AWS Lambda functions to execute code and the AWS Cloud Development Kit (CDK) to define and provision cloud resources programmatically.
Important Notes
- The code represents an initial prototype of the system.
- The repository is provided for documentation and reproducibility purposes only.
Intended use
- Reference for the methodology used in the thesis and related publications.
- Transparency for academic review and citation.
Files
iFoxz17/sc-delay-prediction-v0.0.1.zip
Files
(79.2 MB)
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md5:8fe8350f290e7f6d09132ea03309df4a
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Additional details
Related works
- Is described by
- Working paper: 10.5281/zenodo.18613309 (DOI)
- Is supplement to
- Software: https://github.com/iFoxz17/sc-delay-prediction/tree/v0.0.1 (URL)
Software
- Repository URL
- https://github.com/iFoxz17/sc-delay-prediction
- Programming language
- Python , TypeScript
- Development Status
- Concept