Incorporating Neutrosophic Uncertainty in Data Envelopment Analysis

Abstract

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. 

DOI: 10.5281/zenodo.13997062