Designing an ambulance routing optimization model using the combination of machine learning and genetic algorithm in conditions of uncertainty

This study presents modeling and solving an ambulance routing problem to reduce the time spent providing patient services. A neural network based on a learning machine is used to train the data, and all the input data is analyzed and entered into the genetic algorithm to optimize the mathematical model. The input data was limited to a dataset that only included longitude, latitude, response time, and transportation cost. It was observed that most of the emergency requests were completed within 10 to 20 min. By decreasing the ambulance response time, more emergency cases can be dealt with in less time, preventing casualties and reducing costs. Also, the results showed that the average optimal response time for heart attack is 10 min; for car accidents, 23 min; for fire cases, 13 min; for fall cases, 25 min; for overdose cases, 29 min; and for sudden nervous attack cases, 30 min.