Neutrosophic Data Envelopment Analysis Based on the Possibilistic Mean Approach
Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wrocław University of Science and Technology
2023-01-01
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| Series: | Operations Research and Decisions |
| Online Access: | https://ord.pwr.edu.pl/assets/papers_archive/ord2023vol33no2_5.pdf |
| Summary: | Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally be confusing, imprecise, ambiguous, inadequate, and inconsistent in real- world applications. Thus, disregarding these factors may result in incorrect decision-making. Thus neutrosophic sets have been created as an extension of intuitionistic fuzzy sets to epresent ambiguous, erroneous, missing, and inaccurate information in real-world applications. In this study, we have proposed a technique for solving the neutrosophic form of the Charnes- Cooper-Rhodes (CCR) model based on single-value trapezoidal neutrosophic numbers (SVTrNNs). The possibilistic mean for SVTrNNs is redefined and applied the Mehar approach to transforming the neutrosophic DEA (Neu-DEA) model into its corresponding crisp DEA model. As a result, the efficiency scores of the DMUs are calculated using different risk parameter values lying in [0, 1]. A numerical example is given to analyze the performance of the all India institutes of medical sciences and compared it with Abdelfattah's ranking approach. (original abstract) |
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| ISSN: | 2081-8858 2391-6060 |