نتایج جستجو برای: making units in a data envelopment analysis dea model
تعداد نتایج: 21012345 فیلتر نتایج به سال:
سنجش کارایی تولیدو رتبهبندی محصولات یک واحد صنعتی با روش تحلیل پوششی دادهها در حالت قطعی و بازهای
Today, managers are seeking more efficient use of resources and manufacturing the products with the highest efficiencies. Data Envelopment Analysis (DEA) is a powerful tool for evaluating the efficiency of the decision making units. So far, in most studies, Data Envelopment Analysis has been applied to evaluate the efficiency of the separate organizations. However, in this study, DEA has been a...
data envelopment analysis (dea) is a technique for measuring the efficiency of decision making units. in all models of the dea, for each unit under assessment, the numerical efficiency is obtained which may be less than or equal to one. given the possible large number of functional units, we use various ranking methods for evaluating units. one of the rating methods is balance index and topsis....
data envelopment analysis (dea) is a technique for measuring the efficiency of decision making units. in all models of the dea, for each unit under assessment, the numerical efficiency is obtained which may be less than or equal to one. given the possible large number of functional units, we use various ranking methods for evaluating units. one of the rating methods is balance index and topsis....
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 covaria...
Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. The standard DEA models assume that all inputs and outputs are crisp and can be changed at the discretion of management. While crisp input and output data are fundamentally indispensable in the standard DEA evalua...
In This paper a hybrid DEA method consisting of four phases for assigning the financial efficiency of commercial banks in India is used. This paper is based on panel data of banks for the period from 2011 to 2015. The DEA analysis based on hybrid method of DEA AND TOPSIS is used for ranking efficient Decision Making Units(DMUs) in Data Envelopment Analysis (DEA). However, since each of these me...
Data envelopment analysis (DEA) is a mathematical programming method in Operations Research that can be used to distinguish between efficient and inefficient decision making units (DMUs). However, the conventional DEA models do not have the ability to rank the efficient DMUs. This article suggests bootstrapping method for ranking measures of technical efficiency as calculated via non-radial mod...
Data envelopment analysis (DEA) is a technique for measuring the efficiency of decision making units. In all models of the DEA, for each unit under assessment, the numerical efficiency is obtained which may be less than or equal to one. Given the possible large number of functional units, we use various ranking methods for evaluating units. One of the rating methods is Balance index and Topsis....
data envelopment analysis (dea) is a technique used to evaluate the relative efficiency of comparable decision making units (dmus) with multiple input-output. it computes a scalar measure of efficiency and discriminates between efficient and inefficient dmus. it can also provide reference units for inefficient dmus without consideration of the decision makers’ (dms) preferences. in this paper, ...
Data envelopment analysis (DEA) is a mathematical programming technique for identifying efficiency scores of decision making units (DMUs). Since DEA models cannot present efficient frontiers of PPS, in order to do this, we introduce a method for identifying efficient frontier for DMUs with interval data.
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