List of Articles Farhad Hosseinzadeh Lotfi

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  1 - Evaluating the efficiency of bank branches with random data
  Maryam Ghashami Farhad Hosseinzadeh lotfi
  Data Envelopment Analysis (DEA) is a mathematic technique to evaluate the relative efficiency of a group of homogeneous decision making units (DMUs) with multiple inputs and outputs. The efficiency of each unit is measured based on its distance to the production possibi More

  Data Envelopment Analysis (DEA) is a mathematic technique to evaluate the relative efficiency of a group of homogeneous decision making units (DMUs) with multiple inputs and outputs. The efficiency of each unit is measured based on its distance to the production possibility set (PPS). In this paper, the BCC model is used in output-oriented. The average return on profit as output and the covariance of profit (risk) are considered as inputs. In the continuation, the median and the mod earned investment as two factors of output to the model presented to provide a better analysis of the types of investment, and finally, let us mention a true example Manuscript profile
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  2 - Ranking of Non-Extreme Efficient units based on multi ideal DMUs in PPS
  Alireza Salehi Farhad Hosseinzadeh lotfi Mohsen Rostamy-Malkhalifeh
  Data envelopment analysis (DEA) is a body of research methodologies to evaluate overall efficiencies, identify the sources, and estimate the amounts of inefficiencies in inputs and outputs.efficient DMUs all have an efficiency of one, so that for these units no ranking More

  Data envelopment analysis (DEA) is a body of research methodologies to evaluate overall efficiencies, identify the sources, and estimate the amounts of inefficiencies in inputs and outputs.efficient DMUs all have an efficiency of one, so that for these units no ranking can be given. Since in evaluating by traditional DEA models many DMUs are classified as efficient, a large number of methods for fully ranking both efficient and inefficient DMUs have been proposed. In the last decade, ranking DEA efficient units has become the interests of many DEA researchers and a variety of models (called super-efficiency models) were developed to rank DEA efficient units. Super efficiency data envelopment analysis model can be used in ranking the performance of efficient DMUs. While the models developed in the past are interesting and meaningful, they have the disadvantages of being infeasible or instable occasionally. But the main problem of super-efficient models is lack of differentiation between non- extreme efficient DMUs, so these models cannot rank these DMUs. In this paper, we propose a new method for Ranking Non-extreme Efficient Decision making units in Data Envelopment Analysis based on benchmark. One of the main advantages of our approach is that, this method doesn’t apply any new models, rather this model applies a combination of the well-known models for ranking DMUs. Therefore, understanding our proposed method is easy for readers. One numerical example is examined to illustrate the potential applications of the proposed method. Manuscript profile
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  3 - Estimating Production Function under Endogeneity: A Model Based on Data Envelopment Analysis
  Roghyeh Malekii Vishkaeii Behrooz Danishian Farhad Hosseinzadeh lotfi
  Endogeneity and its impact on estimating economic models can be seen in many economic studies. Data envelopment analysis is one of the most common non-parametric methods in which different axioms are used to estimate the production function (efficient frontier). However More

  Endogeneity and its impact on estimating economic models can be seen in many economic studies. Data envelopment analysis is one of the most common non-parametric methods in which different axioms are used to estimate the production function (efficient frontier). However, the issue of endogeneity and its impact on estimating the efficient frontier is less considered. Cordero et al (2016) indicated that standard models of data envelopment analysis do not perform well in the presence of positive and high endogeneity. In this article, a model based on relaxing convexity axiom is presented in which the Cobb-Douglas function is considered as a real production function. Then, the efficiency of the proposed model is compared with the standard models of the data envelopment analysis and Cobb-Douglas function under positive and high endogeneity. The results show that the proposed model outperforms the counterparts. In addition, by comparing the models in different modes of return to scale, it is observed that the type of return to scale is also effective in determining the efficiency and efficiency of the proposed model compared to its economic counterpart. Manuscript profile
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  4 - Efficiency and Return to Scale of Two-Stage network in Data Envelopment Analysis Using Additive Model
  Mahboobeh Joghataie Farhad Hosseinzadeh lotfi ‪Tofigh Allahviranloo
  Data Envelopment Analysis is a nonparametric method based on the mathematical model. The concept of return to scale is one of the most important issues in economics and also in the data envelopment analysis, which includes a large part of the studies. Determining the ty More

  Data Envelopment Analysis is a nonparametric method based on the mathematical model. The concept of return to scale is one of the most important issues in economics and also in the data envelopment analysis, which includes a large part of the studies. Determining the type of return to scale (increasing, decreasing, and constant) will provide information to the manager through which he will be able to decide on how to achieve the optimal unit level under evaluation. Despite the fact that in the majority of the studies the concept of return to scale (RTS) has been investigated in radial models, this paper, in order to recognize the type of return to scale, has expressed and proved a method based on a non-radial additive model. We also developed this method for a two-stage network and in addition to inputs and outputs; we have introduced new entry intermediate measures in the intermediate products to the system. Then, we estimate and prove the type of return to scale for this network model. At the end, examples are given to examine the proposed method. Manuscript profile
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  5 - A technique for identifying congestion in Data Envelopment Analysis
  Amin Jabbari Farhad Hosseinzadeh Lotfi Mohsen Rostamy Malkhalifeh
  Data Envelopment Analysis (DEA) is a non-parametric mathematical programming method used to assess performance and measure the efficiency of Decision-making Units (DMUs) that operate with multiple concurrent inputs and outputs. The performance of these units is influenc More

  Data Envelopment Analysis (DEA) is a non-parametric mathematical programming method used to assess performance and measure the efficiency of Decision-making Units (DMUs) that operate with multiple concurrent inputs and outputs. The performance of these units is influenced by the utilization of input resources. While an increase in input utilization typically leads to higher production levels, there are scenarios where increased input usage results in decreased outputs. This phenomenon is termed congestion. Given that alleviating congestion can reduce costs and enhance production, it holds significant importance in economics. This paper introduces a method for identifying congestion based on a defined modeling framework. A DMU is considered congested when reducing inputs in at least one component leads to increased outputs in at least one component, and increasing inputs in at least one component can be achieved by reducing outputs in at least one component, without improvement in other indicators. The paper explores congestion in DMUs with both increasing and decreasing inputs. Manuscript profile