Engineered Linear Algebra with AI to Optimize Supply Chain Coupling Linear Algebra and AI to Solve One of The Most Complex Real-Time Problems
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There are many real-time situations which can be effectively solved by optimizing the basics of linear algebra by infusing it through latest AI models. This research paper is intended to bring into use the basic concepts of linear algebra along with the nuances of AI/ML to bring about optimization for solving supply chain scenario across industry. The challenge lies in Modeling disruptions (e.g., geopolitical events, pandemics) across global supply chains in real time. Linear Algebra’s Role lies in Matrix representations of supplier–buyer networks, eigenvalue analysis for systemic risk. The frontier lies in combining linear algebra with adaptive AI (reinforcement learning, quantum ML, and multi-agent systems).
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References
Below are the reference sites which I traversed to help build my understanding:
Introduction to Linear Algebra [Math.MIT.edu] – By Gilbert Strang ILA, 6th Ed. (2023)
OpenAI website - OpenAI (https://openai.com)
GenAI OpenAI key - OpenAI API (https://blog/open-api)
Linear Algebra- Linear Algebra - GeeksforGeeks
Linear Algebra - Erwin Kreyszig, In collaboration with Herbert Kreyszig and Edward J. Norminton - Advanced Engineering Mathematics, 10th Edition -Wiley (2011)
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