Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA models, it is assumed that the input or output variables ar More
Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA models, it is assumed that the input or output variables are all non-negative and desirable. However, in some situations, a performance measure can take positive quantity for some DMUs and negative value for others. Also, undesirable (bad) inputs and outputs may be presented in the production process. Hence, the standard model cannot directly reflect the efficiency score. The paper proposes a modified model in which both undesirable and negative data are treated to improve the relative efficiency of the DMU under evaluation. The focus of this paper is on treating the negative data on the definition of the two non-negative variable and the decreasing of undesirable outputs. A real example of 20 bank branches shows applicability of the proposed approach
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Data envelopment analysis (DEA) as a non-parametric method has covered a wide range of applications in measuring comparative efficiency of decision making units (DMUs) with multiple incommensurate inputs and outputs. The standard DEA method requires that all input and o More
Data envelopment analysis (DEA) as a non-parametric method has covered a wide range of applications in measuring comparative efficiency of decision making units (DMUs) with multiple incommensurate inputs and outputs. The standard DEA method requires that all input and output variables be known as semi positive. In many real situations, the presence of undesirable and even negative data are inevitable. In DEA literature there have been various approaches to enable DEA to deal with negative data. On the other hand, the structure of interval data has recently attracted considerable attention among DEA researchers. According to importance of interval data, this paper proposes a radial measure which permits the presence of undesirable and negative data with interval structure. The proposed model can evaluate the efficiency of all DMUs and leads to improve the inefficient unit with interval negative and undesirable data. To elucidate the details of the proposed method an illustrative example of a private bank in IRAN explores the applicability of the proposed method.
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