On Treating Input Oriented Data Envelopment Analysis Model under Neutrosophic Environment

Abstract

Data Envelopment Analysis (DEA) stands out as the most commonly employed 
approach for assessing the overall performance of a group of similar Decision-Making Units 
(DMUs) that utilize similar resources to produce comparable outputs. Nonetheless, the observed 
characteristics of symmetry or asymmetry in various types of data in real-world applications can 
often be imprecise, unclear, insufficient, or contradictory. Neglecting these conditions can 
potentially result in erroneous decision-making. Certain models take a more restrictive approach 
by assuming that inputs and outputs possess the same level of determinism. Regrettably, such 
constraints don't hold true for the majority of real-world scenarios. In actual situations, however, 
the observed input and output data may sometimes be neutrosophic numbers. So, the primary 
purpose of this study is to construct a Neutrosophic Input Oriented DEA (NIODEA) Model that 
incorporates both neutrosophic and deterministic output and/or input variables, handled in 
accordance with the scoring function.  The model we have developed has broad applicability 
across diverse organizations, aiding decision-makers in making informed choices and optimizing 
resource allocation, a particularly valuable asset in today's intensely competitive business 
environment. To underscore the practical utility of the model, we provide an illustrative example 
that demonstrates its effectiveness and relevance for decision-makers.