نتایج جستجو برای: data envelopment analysis returns to scale efficiency upsizingdownsizing mpss
تعداد نتایج: 11843075 فیلتر نتایج به سال:
performance evaluation of electricity distribution units is an important issue between researchers and regulators. classic data envelopment analysis models with deterministic data have been used by many authors to measure efficiency of power distribution units in different countries. however, data envelopment analysis with stochastic data are rarely used to measure efficiency of distribution co...
Given the rapid development of the banking sector, it is reasonable to expect that the performance of banks has become the centre of attention among bank managers, stakeholders, policy makers, and regulators. In order to maximizing the share-holders’ satisfactory level, two bank efficiency measurement approaches, i.e. the production approach and the user cost approach, which are financial evalu...
data envelopment analysis (dea) is a non-parametric method for evaluating the relative technical efficiency for each member of a set of peer decision making units (dmus) with multiple inputs and multiple outputs. the original dea models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which interval scale data can appe...
the purpose of this article is the measurement of technical efficiency and returns to scale using data envelopment (dea) approach. in this study, we have measured efficiency quantity and economies of scale for state owned insurance and private companies for 1384-1385 periods. the results reveal that in the dea with the assumption of variable returns to scale, iran, tosee and razi companies have...
In this paper, weak defining hyperplanes and the anchor points in DEA, as an important subset of the set of extreme efficient points of the Production Possibility Set (PPS), are used to construct unobserved DMUs and in the long run to improve the envelopment of all observed DMUs. There has been a surge of articles on improving envelopment in recent years. What has been done first is in Constant...
in this paper, weak defining hyperplanes and the anchor points in dea, as an important subset of the set of extreme efficient points of the production possibility set (pps), are used to construct unobserved dmus and in the long run to improve the envelopment of all observed dmus. there has been a surge of articles on improving envelopment in recent years. what has been done first is in constant...
Background and Objectives: The goal of current study is to evaluate the performance of hospitals and their departments. This manuscript aimed at estimation of the most productive scale size (MPSS), returns to scale (RTS), and benchmarking for inefficient hospitals and their departments. Methods: The radial and non-radial data envelopment analysis (DEA) ap...
Productivity growth and efficiency improvements are the major sources of economic development. Pure efficiency, scale efficiency, and technology are basic factors, and rules and regulations and balance are recently known factors affecting the Malmquist productivity index. The index is the most common productivity growth index that uses data envelopment analysis models over multiple time periods...
The purpose of this study is to utilize a new method for ranking extreme efficient decision making units (DMUs) based upon the omission of these efficient DMUs from reference set of inefficient and non-extreme efficient DMUs in data envelopment analysis (DEA) models with constant and variable returns to scale. In this method, an L2- norm is used and it is believed that it doesn't have any e...
The purpose of this paper is to evaluate the returns to scale of the Tehran Stock Exchange based on new models in data envelopment analysis. Using this assessment, it is possible to judge the application of contradictory or expansion policies in stock companies. To this end, there is a need for models in the data envelopment analysis that can assess the left and right returns to scales of the d...
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