A mathematical model for efficient ambulance location based on DSM-MALP integration.

Area of Science:

• Operations Research
• Public Health
• Emergency Medical Services

Background:

• Effective medical facility and vehicle placement is critical for emergency services, directly impacting patient survival rates.
• Urban challenges like fluctuating populations and traffic congestion necessitate optimized ambulance station locations to minimize delays and improve healthcare efficiency.
• Existing ambulance services in Isfahan's four districts were evaluated for potential improvements.

Purpose of the Study:

• To develop a static probabilistic model integrating the double standard model (DSM) and maximum availability location problem (MALP) for ambulance station siting.
• To enhance ambulance availability and increase station coverage radius by incorporating an availability factor.
• To assess the efficiency of the developed model through sensitivity analysis.

Main Methods:

• Integration of the double standard model (DSM) and maximum availability location problem (MALP).
• Development of a static probabilistic model for ambulance station location.
• Sensitivity analysis using four different approaches to evaluate model efficiency.

Main Results:

• The developed model demonstrated an increase in efficiency compared to the default model.
• Sensitivity analysis confirmed the effectiveness of the integrated approach.
• Relocating stations and optimizing ambulance numbers showed potential for improved service availability.

Conclusions:

• The integrated probabilistic model offers a superior approach to optimizing ambulance station locations for enhanced emergency medical services.
• Improvements in ambulance availability and strategic station placement can significantly reduce patient mortality in urban settings.
• The study provides a framework for enhancing healthcare sector efficiency through optimized emergency vehicle deployment.