Data envelopment analysis is a linear programming-based approach used to evaluate the relative performance of decision making units (DMU's) that perform the same tasks with multiple inputs and multiple outputs. Due to the optimistic view of the DEA in evaluating the per More
Data envelopment analysis is a linear programming-based approach used to evaluate the relative performance of decision making units (DMU's) that perform the same tasks with multiple inputs and multiple outputs. Due to the optimistic view of the DEA in evaluating the performance of homogeneous decision making units, multiple units with a maximum relative efficiency score (equal to unit) are highly likely. Therefore, ranking models were presented to distinguish between efficient units. Cross efficiency evaluation is one of the most useful tools for ranking DMUs in data envelopment analysis. This model has two major flaws in implementation. First, it yields different results in the presence of optimal alternatives; and second, there is no compelling reason to use the arithmetic mean to integrate the cross-performance matrix results. In this paper, a new approach, inspired by the preferential voting process and the idea proposed in the TOPSIS method, is presented to combine cross-performance results in the presence of undesirable outputs. The results are then used to rank suppliers in the presence of undesirable outputs.
Manuscript profile
Cross efficiency is one of the useful methods for ranking of decision making units (DMUs) in data envelopment analysis (DEA). Since the optimal solutions of inputs and outputs weights are not unique so the selection of them are not simple and the ranks of DMUs can be ch More
Cross efficiency is one of the useful methods for ranking of decision making units (DMUs) in data envelopment analysis (DEA). Since the optimal solutions of inputs and outputs weights are not unique so the selection of them are not simple and the ranks of DMUs can be changed by the difference weights. Thus, in this paper, we introduce a method for ranking of DMUs which does not have a unique problem. In the real life, the outputs can be shown as desirable and undesirable outputs. So it is important to provide models for the ranking of DMUs in present of desirable and undesirable outputs. The classic DEA models deals with certain data. But, in the real word, all data are not necessarily certain. For solve of this problem, we present a new method that compute the ranks of all DMUs by uncertain data and calculate the lower and upper bounds for the ranks of DMUs. Finally, the results of a simple example are given.
Manuscript profile
One way to rank DMUs in DEA is the cross efficiency method. In this method, the efficiencyof each DMU is calculated by other DMUs optimum weights, which makes the ranking moreacceptable for managers. Existing alternative optimum weights in cross efficiency methodlead to More
One way to rank DMUs in DEA is the cross efficiency method. In this method, the efficiencyof each DMU is calculated by other DMUs optimum weights, which makes the ranking moreacceptable for managers. Existing alternative optimum weights in cross efficiency methodlead to several ranks for DMUs. Several secondary goals have introduced to avoid thisproblem, till now. In this paper, a new model is presented, that would be satisfying andacceptable for all DMUs. Therefore, by solving this model, the optimum weights areagreeable and fairy for DMUs.
Manuscript profile
The aim of this study is to propose a new method for portfolio optimization based on financial ratios. In this method, cross efficiency scores are produced from financial ratios, using Data Envelopment Analysis. Mathematical interpretation of these cross efficiency scor More
The aim of this study is to propose a new method for portfolio optimization based on financial ratios. In this method, cross efficiency scores are produced from financial ratios, using Data Envelopment Analysis. Mathematical interpretation of these cross efficiency scores that allocates several score to each company is efficiency of company in probably future situations. Efficiency scores calculated based on proper financial ratios can be considered as financial strength. Thus cross efficiency scores produced from financial ratios, can be considered as potential financial strength. As future is not clear, potential financial strength can be presented in expectation and risk indices that are mean and variance of cross efficiencies. Fraction of expectation to risk for potential financial strengths can be used as a criterion for pairwise comparison of companies. Eigenvector associated with the biggest eigenvalue of pairwise comparison matrix reflects relative importance weights of companies. This paper proposes relative importance weights of companies as a basis for portfolio optimization. Based on sharp index Performance of proposed method is acceptable and better than marker portfolio and portfolio of one similar method.
Manuscript profile
