Classifying inputs and outputs in interval data envelopment analysis

Data envelopment analysis (DEA) is an approach to measure the relative efficiency of decision-making units with multiple inputs and multiple outputs using mathematical programming. In the traditional DEA, it is assumed that we know the input or output role of each performance measure. But in some situations, the type of performance measure is unknown. These performance measures are called flexible measures. In addition, the traditional DEA needs crisp input and output data which may not always be available in real world applications. This paper discusses the input or output role of flexible measures using the DEA in environments with interval inputs and outputs. The application of the proposed DEA models is shown with a real dataset.