In many applications, discrimination among decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA). The DEA models unable to discriminate between extremely efficient DMUs. Hence, there is a growing int More
In many applications, discrimination among decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA). The DEA models unable to discriminate between extremely efficient DMUs. Hence, there is a growing interest in improving discrimination power in DEA yet. The aim of this paper is ranking extreme efficient DMUs in DEA based on exploiting the leave-one out idea and combining of Manhattan and infinity norms with constant and variable returns to scale. The proposed method has been able to overcome the existing difficulties in some ranking methods.
Manuscript profile
One of the difficulties of Data Envelopment Analysis(DEA) is the problem of de_ciency discrimination among efficient Decision Making Units(DMUs) and hence, yielding large number of DMUs as efficient ones. The main purpose of this paper is to overcome this inability. One More
One of the difficulties of Data Envelopment Analysis(DEA) is the problem of de_ciency discrimination among efficient Decision Making Units(DMUs) and hence, yielding large number of DMUs as efficient ones. The main purpose of this paper is to overcome this inability. One of the methods for ranking efficient DMUs is minimizing the Coefficient of Variation (CV) for inputs-outputs weights. In this paper, it is introduced a nonlinear model for ranking efficient DMUs based on the minimizing the mean absolute deviation of weights and then we convert the nonlinear model proposed into a linear programming form.
Manuscript profile
In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same eff More
In many applications, ranking of decision making units (DMUs) is a problematic technical task procedure to decision makers in data envelopment analysis (DEA), especially when there are extremely efficient DMUs. In such cases, many DEA models may usually get the same efficiency score for different DMUs. Hence, there is a growing interest in ranking techniques yet. The purpose of this paper is ranking extreme efficient DMUs in DEA based on exploiting the leave-one out and minimizing the maximum distance between DMU under evaluation and boundary efficient in input and output directions. The proposed method has been able to overcome the lacks of infeasibility and unboundedness in some DEA ranking methods.
Manuscript profile
