A compositional approach to two-stage Data Envelopment Analysis in intuitionistic fuzzy environment

Abstract

Classical methods of Data Envelopment Analysis operate by measuring the efficiency of decision-making units (DMUs) compared to similar units, without taking their internal structure into account. However, some DMUs consist of two stages, with the first stage producing an intermediate product, which is then consumed in the second stage to produce the final output. The efficiency of this type of DMU is often measured using two-stage Network Data Envelopment Analysis. In real world, most data are vague; therefore the inputs and outputs of systems with vagueness data create uncertainty challenges for DMUs. As a result, when uncertainty appears, intuitionistic fuzzy sets can show more information than classical fuzzy sets. This paper presents a model of two-stage Network Data Envelopment Analysis based on intuitionistic fuzzy data, which measures the efficiency of the first and second stages of each DMU, and ultimately the overall efficiency measures based on the stage efficiencies.