Clustering-Allocation Model Under Risk, and Emissions Factors: Evidence from an Indonesian Region - Puspitorini, Pipit Sari (PROZIMA (Productivity, Optimization and Manufacturing System Engineering), 2024)
This study contributes to risk-based location-allocation problems by constraining time and emergency medical services (EMS) carbon emissions. During the COVID-19 pandemic, this study develops a location set covering the problem of implementing ambulance allocation to optimize opening new facilities and the cluster with the highest emission value in heritage cities. This study also presents an integer linear program considering risk, time, and carbon emissions at three facilities with demand locations. The model was also validated using two cluster methods, K-means clustering and Agglomerative Hierarchical Clustering, with Python software and Google Collaboratory machine learning (GCC). The findings revealed the opening of three facilities and clusters with potential points, with the highest emission values at M3 (0.575% (kg). M2. potential point, with a value of 5832 represents the highest risk. Furthermore, the validation results indicate that the distance significantly total energy consumption (BTU) and carbon emissions (kg). This study ignores the vehicle category. It can be used as a reference by decision-makers by considering these parameters and making a clear contract with a third party in ambulance procurement for humanitarian logistics. The model will help provide insight into another region's relevant emergency medical center. Furthermore, research can anticipate strategies to deal with pandemic outbreaks.