Optimization of EMS Station Layout Based on a New Decision Support Framework - Peng Yang; Bozheng Zhang; Jingrong Yang (Systems)
The layout of emergency medical services (EMS) is of vital importance. A well-planned layout significantly impacts the timeliness of response and operational efficiency, which are crucial for saving lives and mitigating injury severity. This paper presents a novel decision support framework for optimizing EMS station layout. Employing the k-means clustering algorithm in combination with the elbow method and silhouette coefficient method, we conduct a clustering analysis on a patient call record dataset. Comprising 166,161 emergency center call records in the Shanghai area over one year, this dataset serves as the basis for our analysis. The analysis results are applied to determine EMS station locations, with the average ambulance patient pickup time as the evaluation criterion. A simulation model is utilized to validate the effectiveness and reliability of the decision-making framework. An experimental analysis reveals that compared with the existing EMS station layout, the proposed framework reduces the average patient pickup time from 11.033 min to 9.661 min, marking a 12.441% decrease. Furthermore, a robustness test of the proposed scheme is carried out. The results indicate that even when some first-aid sites fail, the average response time can still be effectively controlled within 9.9 min. Through this robustness analysis, the effectiveness and reliability of the decision framework are demonstrated, offering more efficient and reliable support for the EMS system.