Solving Gradual Govering of Healthcare Services by Simulated Annealing and K-means and Artificial Neural Network Clustering Methods

In the recent covering studies it is assumed that the coverage is dependent to the distance from the service point and the coverage will be decreased by increasing the distance. Also in the large scaled problems the solution time will be growth exponentially by exact solution methods. In this paper three solution methods based on the simulated annealing, K-means clustering and clustering by artificial neural network are presented for the gradual covering location problem. Moreover the results by mentioned three solution methods were compared and analyzed. The results showed that the K-means clustering in gradual covering problem has reasonable results and computational time. The proposed method was analyzed more in a case study of Iran technical hospitals covering problem.