Facility Location by Machine Learning Approach with Risk-averse

This paper proposes a novel approach for facility location by integrating machine learning techniques with a risk-averse framework, using the k-means algorithm. Traditional facility location problems often assume a risk-neutral perspective, which may not optimally capture the inherent uncertainties and risks associated with real-world decision-making. By incorporating risk-averse preferences, this study aims to enhance the decision-making process in facility location problems. The proposed approach utilizes a machine learning algorithm, k-means, to identify suitable facility locations based on historical data and risk-averse criteria. Numerical experiments are conducted to demonstrate the effectiveness and efficiency of the proposed methodology. The results show the potential of using machine learning algorithms with risk-averse frameworks in facility location decision-making.This paper proposes a novel approach for facility location by integrating machine learning techniques with a risk-averse framework, using the k-means algorithm. Traditional facility location problems often assume a risk-neutral perspective, which may not optimally capture the inherent uncertainties and risks associated with real-world decision-making. By incorporating risk-averse preferences, this study aims to enhance the decision-making process in facility location problems. The proposed approach utilizes a machine learning algorithm, k-means, to identify suitable facility locations based on historical data and risk-averse criteria. Numerical experiments are conducted to demonstrate the effectiveness and efficiency of the proposed methodology. The results show the potential of using machine learning algorithms with risk-averse frameworks in facility location decision-making.