There are different approaches to generate a common set of weights in DEA based on the p - distance measure. Deviation of an efficiency score derived from a CSW from target efficiency score may be related to the model and the parameter p. In this study, we try to clarif More
There are different approaches to generate a common set of weights in DEA based on the p - distance measure. Deviation of an efficiency score derived from a CSW from target efficiency score may be related to the model and the parameter p. In this study, we try to clarify points about choosing p, model, and data set if it is necessary to produce an efficiency score with the least deviation by a CSW. Two improved linear models are developed by analyzing the result of available models. The results of the proposed models have smaller individual and overall efficiency than corresponding prior ones that It has been confirmed with numerical examples and simulation analysis.
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Data envelopment analysis (DEA) is a non-parametric programming method for evaluating the relative efficiency of a set of peer decision-making units (DMUs) with multiple inputs and multiple outputs. The DEA cross-efficiency method is a well-known method that use to eval More
Data envelopment analysis (DEA) is a non-parametric programming method for evaluating the relative efficiency of a set of peer decision-making units (DMUs) with multiple inputs and multiple outputs. The DEA cross-efficiency method is a well-known method that use to evaluate and ranking a set of peer decision-making units. Whenever a DMU intends to evaluate other DMUs, it faces the problem of non-uniqueness optimal weights of DEA models. Because different weights give us different cross-scores and subsequently different cross-efficiencies scores and this will confuse the decision-maker to make an ultimate decision. The main drawback of this method is the alternate optimal solution set of the DEA model. The main purpose of this study is to propose an approach to this problem to generate non-dominated DEA cross-efficiency scores. We propose a linear programming secondary goal model to select a set of optimal weights for each DMU. Our proposed method is not only simpler than other methods presented with the same purpose, but also does not go beyond the main method.
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