An Optimization Algorithms-based Approach to District Health System Population Areas in Iran

Background: One of important subject in the operations' management fields is partitioning matter that was investigated in the study. This topic has recently received more attention from researchers of the healthcare management systems' field. This subject is important because planning about improvement of the healthcare system structure is considered as one of the most important management problems in each society. The goal of solving this problem was to district a society into several areas, so that each area can cover its  health services completely. Methods: This fundamental-applied study was conducted based on the Genetic optimization algorithm, particle swarm, and differential evolution to improve the current structures with regard to the existing health structure in Iran. Moreover, the health system strategic model was applied to categorize the population regions into 10 partitions. According to nature of the investigated problem, the objective function is maximizing the equilibrium amount in each district. The constraints included exclusive assignment and not-existing unusual assignment. Unusual assignment is defined as existence of no contiguity and holes in partitions. Results: According to the obtained results, the particle swarm algorithm had the most efficiency, while differential evolution had the lowest efficiency. However, the stated constraints were satisfied completely in all algorithms, which represented appropriate efficiency of the modified algorithm in the generation solutions. Conclusion: The results obtained from solving this problem can be used as a useful tool in improving the existing healthcare system in Iran.