A cross-inefficiency approach based on the deviation variables framework. (September 2022) - Ebrahimi, Bohlool; Dhamotharan, Lalitha; Ghasemi, Mohammad Reza; Charles, Vincent
Highlights: We present a cross-inefficiency approach to improve the discriminating power of DEA. Our approach includes a pair of benevolent and aggressive models. Our approach can be used under both CRS and VRS assumptions. Our approach uses the deviation variables framework to rank all units. Our approach solves the non-uniqueness of ranking scores. Abstract: This paper presents a solution to the problem of ranking efficient decision-making units (DMUs) in data envelopment analysis (DEA). We develop a cross-inefficiency approach for the deviation variables framework based on a pair of epsilon-based benevolent and aggressive models for both constant and variable returns-to-scale technologies. The new method improves the discriminating power of DEA, solves the non-uniqueness of ranking solutions, and avoids the negative efficiency scores associated with current models in the deviation variables framework. We illustrate the performance of the approach using a real-life case study. Not only does the research improve the discriminating power, but it also encourages the first step towards integrating the deviation variables framework in the context of decision-making uncertainty.