خلاصة:
In the field of health losses resulting from failure to establish the facilities in a suitable location and the required number, beyond the cost and quality of service will result in an increase in mortality and the spread of diseases. So the facility location models have special importance in this area. In this paper, a successively inclusive hierarchical model for location of health centers in term of the transfer of patients from a lower level to a higher level of health centers has been developed. Since determination the exact number of demand for health care in the future is difficult and in order to make the model close to the real conditions of demand uncertainty, a fuzzy programming model based on credibility theory is considered. To evaluate the proposed model, several numerical examples are solved in small size. In order to solve large scale problems, a meta-heuristic algorithm based on harmony search algorithm was developed in conjunction with the GAMS software which indicants the performance of the proposed algorithm.
ملخص الجهاز:
"com A novel hierarchical model to locate health care facilities with fuzzy demand solved by harmony search algorithm Mehdi Alinaghiana*, Seyed Reza Hejazia, Noushin Bajoula a Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran Abstract In the field of health losses resulting from failure to establish the facilities in a suitable location and the required number, beyond the cost and quality of service will result in an increase in mortality and the spread of diseases.
Innovation of this paper can provide a new mathematical hierarchical model for locating treatment centers for patients to access appropriate medical care, considering the distance minimization objective function and the penalty for demand exceeds the capacity of the facility.
If i n go to sub step 2, otherwise go to sub step 6 Sub step 6: end Step 4: According to the Simulated demand point and the location and type of facility in each identified level of Cr, cost to get Step 5: The third and fourth steps performed 1,000 times and the average cost is calculated Step 6: The amount of Cr with the lowest mean value of the objective function to be reported as optimal Cr. 5.
Table 3- compares the output of the harmony search algorithm and CPLEX solver رجوع شود به تصویر صفحه In the above table, the first column indicates the number of sample problems of small size and at the next columns objective function and T, solution time (second), obtained by CPLEX solver and harmony search (HS) are presented, and GAP% is the difference between the optimal value of objective function obtained by CPLEX solver and the best objective function value is obtained by harmony search algorithm divide by optimal value of objective function multiplied by 100."