Non-dominated DEA cross efficiency scores; a secondary goal approach

Data envelopment analysis (DEA) is a non-parametric programming method for evaluating the relative efficiency of a set of peer decisionmaking 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 nonuniqueness optimal weights of DEA models. Because different weights give us different cross-scores and subsequently different crossefficiencies 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.