Efficiency Appraisal and Classification of Flexible Random Factors

Traditional data envelopment analysis (DEA) models assist in comprehending the performance of decision-making units (DMUs) in situations where a definitive set of inputs and outputs is available. However, in certain real-world scenarios, it becomes necessary to assess the performance of DMUs when dealing with flexible and random measures. Hence, in this chapter, we propose the utilization of two distinct methodologies, namely oriented and non-oriented chance-constrained DEA-based approaches, to evaluate the efficiency of diverse organizations incorporating both stochastic and flexible elements. The chance-constrained DEA-based patterns are transformed into deterministic mixed integer programming problems. A case study is used to estimate the relative efficiency and categorize flexible random variables by utilizing the proposed approach. According to the empirical evidence, efficiency levels exhibit no increment as risk levels increase. The utilization of stochastic observations is more informative when compared to the attainment of efficiency scores through precise measures.