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News Wire / technology

Supply Chain Network Design Using Machine Learning

Modernity/arxiv 1h1h Impact 7
A new paper investigates a viable two-echelon supply chain network design problem using machine learning-derived chance-constrained programming. The research is authored by Mohammad Rohaninejad, Behdin Vahedi-Nouri, Elham Jelodari Mamaghani, Mehdi Foumani, and Olga Battaia. This approach aims to optimize efficiency and resilience in supply chains. The application of AI in supply chain management is a growing area of research. This method aims to optimize logistics and resource allocation. The research could lead to more efficient and resilient supply chains.

Topics

supply chain machine learning optimization

Developing

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Sources · 7 independent

Modernity/arxiv

“Viable Supply Chain Network Design: Machine Learning-Derived Chance-Constrained Programming. Authors: Mohammad Rohaninejad, Behdin Vahedi-Nouri, Elham Jelodari Mamaghani, Mehdi Foumani, Olga Battaia Abstract: This paper investigates a viable two-echelon supply chain network design problem wi”

Modernity/arxiv

“Youjun Wu, Yuanxin Zhong, Rui Zhang, Yunlong Wang, Xiaosong Jia, Yu-Gang Jiang Abstract: Dexterous hands are essential for fine-grained manipulation, but their hardware designs va...”

Radio Progreso HN

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Modernity/arxiv

“Machine learning is being used for supply chain network design.”

Supply Chain Dive

“The research aims to optimize logistics and improve supply chain efficiency.”

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