The problem of location and design of the hub facility network has been extensively studied in location theory. This is usually accompanied by simultaneous decisions about the optimal number of hub facilities, their location, and the allocation of non-hub nodes. In this paper, a new and robust heuristic in the framework of the simulated genetic-refrigeration hybrid algorithm (GA-SA) has been developed to solve the problem of locating hub facilities with unique allocation and unlimited capacity. In the proposed heuristics, the genetic algorithm is used to form a number of different initial solutions, and then the simulated refrigeration algorithm is used to improve both the location vector and the problem assignment. Since the performance of heuristic algorithms is strongly influenced by the values ​​of their parameters, a robust parameter adjustment approach based on experimental design has been proposed which, in addition to improving and maintaining the algorithm's ability to achieve the appropriate answer, significantly reduces algorithm execution time. Gives. In order to explain the efficiency of the developed algorithm, the results obtained from the implementation of the algorithm on the standard CAB and AP data sets were compared with the results of the best algorithms in the literature. These results indicate that the proposed hybrid algorithm, in addition to higher computational speed than other algorithms, is successful in achieving optimal or near-optimal answers.