International Journal of Data Envelopment Analysis
Volume:10 Issue: 3, Aug 2022

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.

In the present world, calculating the efficiency of systems with an internal structure, suchas two-stage systems, is principally imperative. Conventional Data envelopment analysis (DEA) is a non-parametric approach that measures the efficiency of comparable black box systems. There is a weakness in traditional DEA that does not survey the internal structure of systems. Hence, for evaluating the efficiency of the systems with internal structure, Network DEA (NDEA) was presented. Two-stage systems are a special case of network systems. Many procedures were suggested to measure the efficiency of these systems such as slacks-based measure (SBM) approach. In this paper, we will focus on two-stage systems with shared inputs between stages and final output in stage 1 and we shall propose non-cooperative models to calculate the efficiency of these systems based on SBM procedure. Also, we shall indicate the overall efficiency of these systems as the product of the efficiency scores of stages. In the end, to explain the proposed approach, a numerical example will be presented.

Increasing the discrimination power the of data envelopment analysis method and choosing appropriate weights is one of the important issues in data envelopment analysis. One of the ways to overcome this problem is to use multi-objective data coverage analysis. In multi-objective problems, the goal of the objective functions is usually contradictory to each other, so it is not possible to find an optimal solution for all the objective functions simultaneously. In this article, we use the ideal programming approach to solve the problem of multi-criteria data envelopment analysis, and then we compare the presented method with previous methods in the framework of preferential voting.

Cross-efficiency is a ranking technique based on the peer-evaluation that can increase the discriminating power between efficient decision-making units. This paper intends to assess the two-stage processes consisting of undesirable outputs by applying the cross-efficiency evaluation. Given undesirable outputs, the directional distance function under the weak disposability assumption is utilized. The proposed model under variable returns to scale is designed, which makes it different from the previous models. Furthermore, it can reduce the zero optimal coefficients. By measuring the inputs and outputs inefficiency, the whole system and each of its two stages rank, simultaneously. To analyze the suggested method, an application on the industrial productions of 30 regions of China is used.

One of the best methods for computing the efficiency of decision-Making Units (DMU) is Data Envelopment Analysis (DEA) that is useful for improving organizational performance. If we added a new unit to our observation sets, we have to run the model again. Nowadays, datasets from many organizations in the real world have been growing. So, we need a huge amount of computation for examining efficiency for new dataset. To overcome this problem, we combine Machine Learning (ML) and DEA. We consider organizations have interval data. According to we have interval data set, so we use interval DEA. Actually, we link between interval DEA and ML algorithms. First, we compute the efficiency score of these organizations by using Interval DEA. Second, we compute two scores that come in the first stage. Then, use these scores in ML. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 89%.

Data envelopment analysis is a non-parametric method based on mathematical programming, which is used to evaluate the performance of a set of decision-making units with multiple homogeneous inputs and outputs. One use of data envelopment analysis is the integration of resources and inputs to create new efficient decision-making units. As a classic research topic in the fields of management and economics, the resource allocation problem has gained extensive attention from many researchers, and a large body of research has been conducted accordingly. The present study investigates some of the proposed methods and models of the allocation problem