Simulation and optimization models for emergency medical systems planning

Area of Science:

• Operations Research
• Healthcare Management
• Public Health

Background:

• Emergency Medical Systems (EMS) face complex strategic planning challenges.
• Optimizing resource allocation is crucial for timely emergency response.
• Balancing demand, cost, and service levels is a key operational issue.

Purpose of the Study:

• To develop analytical models for strategic decision-making in EMS.
• To address critical planning problems including ambulance deployment, request prioritization, and resource acquisition.
• To minimize costs while ensuring appropriate response times for emergency medical services.

Main Methods:

• Queuing theory for demand and service analysis.
• Discrete-event simulation for dynamic system modeling.
• Integer linear programming for resource allocation and contract optimization.

Main Results:

• Models were developed to optimize ambulance deployment based on forecasted demand and response times.
• Strategies for managing non-urgent requests to preserve resources for urgent cases were analyzed.
• An optimal mix of ambulance rental contracts was identified to minimize operational costs.

Conclusions:

• The proposed analytical models provide effective decision support for EMS strategic planning.
• The study demonstrates the feasibility of optimizing EMS operations using quantitative methods.
• Findings offer practical insights for improving the efficiency and cost-effectiveness of emergency medical services.