Linear robust data envelopment analysis: CCR model with uncertain data

Abstract

Data envelopment analysis (DEA) traditionally assumes that input and output data of the different decision making units (DMUs) are measured with precision. However, in many real applications inputs and outputs are often imprecise. This paper proposes a linear robust data envelopment analysis (LRDEA) model using imprecise data represented by an uncertainty set. The method is based on the robust optimisation approach of Bertsimas and Sim to seek maximisation of efficiency under uncertainty (as does the original DEA model). In this approach, it is possible to vary the degree of conservatism to allow for a decision maker to understand the tradeoff between a constraint's protection and its efficiency. The method incorporates the degree of conservatism in the maximum probability bound for constraint violation. Application of the proposed model (LRDEA) to analyse the technical and scale efficiency of potato production in 23 Iranian provinces demonstrates the reliability and flexibility of the model.