Optimize a chain convenience store location prediction model by using MTS-machine learning methodology

In the past, convenience store location selection required substantial time, cost and effort for field research, and the location selection decision was often limited by the subjective experience of the decision-maker. The rapid development of machine learning (ML) in recent years has enabled many studies of model training. This study introduces a novel hybrid approach integrating Mahalanobis-Taguchi System (MTS) for variable reduction with ML algorithms to address the challenges of large-scale, multi-variable retail location selection. The proposed model reduces computational requirements and supports data-driven decision-making for convenience store chains. To achieve this, a hybrid approach combining the Mahalanobis-Taguchi System (MTS) and machine learning algorithms-XGBoost, Random Forest (RF), and support vector machine (SVM)-were employed to develop a location prediction model for chain convenience stores. Using real-world location data from Taiwanese convenience stores, MTS analysis mainly performed features selection, reducing the number of variables from nine to five. The research results show that the prediction ability of the MTS-XGBoost algorithm can be accurately maintained at more than 75% under different training set proportions, and it has more accurate prediction results than MTS-RF and MTS-SVM. This approach enables more efficient and accurate store location decisions, potentially transforming strategic planning in the retail sector.