Optimizing of Universal DEA Model with Multi-Level Integration under  Neutrosophic Environment

Hamiden Abd El- Wahed Khalifa; Loay Alkhalifa; Sultan S. Alodhaibi; Basma E. El- Demerdash

Authors

Hamiden Abd El- Wahed Khalifa Department of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi Arabia
Loay Alkhalifa Department of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi Arabia
Sultan S. Alodhaibi Department of Mathematics, College of Science, Qassim University, Buraydah 51452, Saudi Arabia1
Basma E. El- Demerdash Department of Operations and Management Research, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza 12613, Egypt

Keywords:

Data Envelopment Analysis; Neutrosophic Analysis; Efficiency Assessment; Uncertainty Integration; Multi-Level Uncertainty; Healthcare Facility; Triangular neutrosophic number

Abstract

This research introduces a comprehensive Universal Data Envelopment Analysis (DEA) model
to handle real-world problems fraught with uncertainty of every operational facet. Apart from all this, this
framework has a feature of embracing many level variations: deterministic, stochastic, fuzzy, and
neutrosophic, in both input and output variables, unlike the traditional DEA models, which are limited to
deterministic. Besides accommodating different orientation types detected in models such as input
oriented and output-oriented, it also incorporates variable and constant return types of scale. The main
objective of this study is to develop a flexible DEA model that can measure and rank, under uncertain
conditions, the performance efficiency of organizations having comparable input-output structures. The
proposed model seeks to identify inefficient organizations so as to improve specifically on those areas to
have a much better outcome in overall efficiency. This provides decision-makers with very strong and
versatile options for efficient measures, even in the context of heterogeneous and imprecise data. The
paradigm for this study lies in bringing together several ways of handling uncertainty under one umbrella.
This study is much better than the conventional DEA models. Possible restrictions, however, on
applicability in very large data sets and computation time complexities would need further probing.

DOI: 10.5281/zenodo.15265461