Incorporating Neutrosophic Uncertainty in Data Envelopment Analysis

In the realm of Data Envelopment Analysis (DEA), the assessment of relative efficiency has historically relied on deterministic variables. Yet, real-world data often presents diverse uncertainties, including a neutrosophic nature, which can significantly impact the accuracy and reliability of DEA outcomes. Therefore, the current paper proposes a new DEA model entirely to address this issue and in new DEA model designed to address this issue, preexisting approaches that fall short due to the inability to address neutrosophic uncertainty, which can be commonly found in actual data, to enhance efficiency evaluation precision. This paper introduces the new DEA model to show the potential to deal with the neutrosophic uncertainty in the input and output to decide whether the certain variable is deterministic or neutrosophic. In addition, they assure the flexibility of the model regarding the orientation and to embrace both the output-orientation and input-orientation as well as both constant and variable return on scale. In its simplest form, the model converts the neutrosophic constraints into their determinate forms, which makes the DEA issues feasible in the determinate domain. On this way, the current model enhances the drawbacks of conventional DEA methodologies, and the actual data reflected by neutrosophic indeterminacies. The research discusses that the proposed model holds a high capability and flexibility through theoretical examination and through the given cases. This particular methodology helps researchers and decision makers to be able to use all its functions in enhancing defensibility and precision in the assessment of efficiency thereby increasing the accuracy of the decision-making process in every aspect of its use.