Data envelopment analysis is one of the best methods to evaluate the performance of decision-making units. This method is also used for benchmarking. benchmarking is a tool to evaluate organizational performance with a learning approach from others, it is also one of the practical methods in continuous improvement of the benchmarking method. The importance of benchmarking in all industries is clear. This paper considers the after-sales service network of an automobile company in Iran to evaluate the model. According to the structure of this network, a hierarchical structure is considered for benchmarking. In this paper, the purpose is to provide a model for benchmarking decision-making units with hierarchical structure and dependent parameters. In the real world, most decision-making units have a hierarchical structure and this structure needs more attention by researchers also dependent parameters can have a high impact on benchmarking. The proposed model for the after-sales service network of an automobile company in Iran was implemented and the results show the high impact of dependent variables on benchmarking and has increased modeling accuracy. The accuracy of benchmarking is very important for the success of decision-making units and the results show that paying attention to the relationships between the parameters increases the accuracy of benchmarking and according to the proposed model, more accurate benchmarking can be achieved. تفاصيل المقالة

In this study, given the sequence dependent setup times, we attempt using the technique of Response Surface Methodology (RSM) to set the parameters of the genetic algorithm (GA), which is used to optimize the scheduling problem of n job on 1 machine (n/1). It aims at finding the most suitable parameters for increasing the efficiency of the proposed algorithm. At first, a central composite design was created and then using the data relating to the plan, the complete second-degree model was fitted. Then, by solving the developed non-linear programming model the optimal values of the parameters determined. The performance of algorithm, considering the obtained parameters as inputs of the common Data Envelopment Analysis (DEA), was measured. This way, we can decide on the most effective kinds of problems that can be solved by GA in a similar volume. This study can be used as a model of setting parameters of evolutionary and meta-heuristic algorithms using scientific techniques to prevent disadvantages relating to trial and error methods. تفاصيل المقالة