Presenting A Multi-objective Location Model in GIS Environment: A Case Study in East Tehran Emergency Stations

Background: This study aims to design a multi-objective model for locating emergency relief bases with maximum coverage and minimum costs. As a result, this model is expected to reduce mortality by increasing the efficiency of relief services. Materials and Methods: Based on the ambulance distribution in the east of Tehran City, Iran, and the statistical information on demand (at least 30 samples for data normalization) in the last six months of 2018, we introduced and implemented a definitive mathematical model. We also evaluated the model with GAMS software. Using previous studies and interviews, we identified key and practical indicators of site locations. These indicators are being easy to access, locating in high-demand areas, such as an urban area, and not being too close to another relief base. These factors were then prioritized using the hierarchical method, and the output indicates the high weight of the factor of “being in a place with high demand”. The objective functions are to maximize coverage, minimize costs, and provide equity in relief time. To deal with the uncertainty of the parameters, we used the robust optimization approach. To initially select potential proposed sites to establish the database, we used Geographic Information System (GIS). To test the above mathematical model in the real world, we conducted a case study in East Tehran. Results: Based on the designed model, the initial points proposed by GIS were identified. Finally, it was found that the number of stations in East Tehran must increase from 27 to 34 bases. Conclusion: By implementing this model, the emergency medical service can provide the highest level of coverage. Also, the maximum relief time at stations will be reduced to 8 minutes.

Presenting A Multi-objective Location Model in GIS Environment: A Case Study in East Tehran Emergency Stations Keywords:

Linear programming , Uncertainty , Reliability , Maximizing coverage , Cost minimization