Data envelopment analysis (DEA) is the leading technique for measuring the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and multiple outputs.In this technique, the weights for inputs and outputs are estimated in the best advantage for each unit so as to maximize its relative efficiency. But, this flexibility in selecting the weights deters the comparison among DMUs on a common base. For dealing with this difficulty, Kao and Hung (2005) proposed a compromise solution approach for generating common weights under the DEA framework. The proposed MCDM model was derived from the original non-linear DEA model. This paper presents an improvement to Kao and Hung's approach by means of introducing an MCDM model which is derived from a new linear DEA model.

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Data Envelopment Analysis (DEA) is basically a linear programming based technique used for measuring the relative performance of organizational units, referred to as Decision Making Units (DMUs). The flexibility in selecting the weights in standard DEA models deters the comparison among DMUs on a common base. Moreover, these weights are not suitable to measure the preferences of a decision maker (DM). For dealing with the first difficulty, the concept of common weights was proposed in the DEA literature. But, none of the common weights approaches address the second difficulty. This paper proposes an alternative approach we term 'preference common weights' which is both practical and intellectually consistent with the DEA philosophy. To do this, we introduce an MOLP model in which objective functions are input/output variables subject to the constraints similar to the equations which define production possibility set (PPS) of standard DEA models. Then by using the Zionts-Wallenius method, we can generate common weights as the DM's underlying value structure about objective functions.

In many applications of DEA, ranking of DMUs and finding the most efficient DMU are desirable, as reported by Toloo (2013). In this paper, after introducing an improvement to the measure of cross-efficiency by Jahanshahloo et al. (2011), we develop a new ranking method under the condition of variable returns to scale (VRS). Numerical example illustrates the effectiveness of the proposed cross-efficiency based ranking method and demonstrates the advantages of our proposal, against the other ranking approaches.

As its title suggests, this note constitutes a critique of the paper by Seiford and Zhu (1999), "Sensitivity and Stability of the Classifications of Returns to Scale (RTS) in Data Envelopment Analysis". By means of counter examples, we discuss some problems related to results presented in that paper. JEL Classification: C44, C61, C67