Optimization of emergency health service logistic processes using queue modeling after an earthquake: A case study of Bartin province

After disasters, a portion or the entirety of the society is negatively affected, leading to an increase in social and economic losses, inadequate existing resources, and a consequent rise in the burden on emergency healthcare services in developing countries. Disasters, which cause numerous injuries and deaths in a short period, result in a heightened demand for urgent medical assistance. However, the capacity of emergency healthcare services does not increase proportionally. These reasons necessitate an increase in both the quantity and quality of research in the field to ensure the effectiveness of service delivery in emergency healthcare. Like all other disasters, earthquakes, depending on their intensity, destructive power, and population density in the affected area, render many individuals in need of urgent medical aid. This thesis primarily aims to develop a feasible model to reduce the waiting time for injured individuals in need of emergency assistance after earthquakes, and to deliver the required urgent healthcare services in the shortest possible time to prevent further harm and fatalities. The constructed model utilizes the waiting line theory, which detects real-life situations, and generates a mathematical model that employs solutions to enhance system efficiency. The model is validated using the GAMS program and proposes scenarios to reduce waiting times. The case study is centered on the city center of Bartın. To test the developed models, the GAMS program with the CPLEX solver is employed for mixed integer programming, and the ANTIGONE solver is utilized for nonlinear constraints. In the scenarios developed based on station capacities, ambulance numbers, and key performance indicators, it is observed that increasing station capacities does not alter the objective function of minimizing the intervention time for injured individuals. Moreover, the waiting time remains unchanged regardless of whether station capacities are decreased or increased. In the scenarios based on ambulance numbers, increasing the number of ambulances significantly reduces the waiting time. In the flexibility scenario of key performance indicators, the average arrival rate ʎ is kept constant while the average service rate µ is both increased and decreased. It is observed that the waiting time decreases when the average service rate µ is increased, and it increases when the average service rate µ is decreased. The findings of this thesis have a guiding value for decision-makers.