Abstract:In this study, Data Envelopment Analysis (DEA) models are improved by employing spherical fuzzy sets (SFSs), which is an extension of generalized fuzzy sets. SFSs were recently introduced as a novel type of fuzzy set that allows decision-makers to express their level of uncertainty directly. As a result, SFSs provide a more preferred domain for decision-makers. Fundamental Charnes-Cooper-Rhodes (CCR) model is discussed on the context of spherical trapezoidal fuzzy numbers (STrFNs), which consider each data val… Show more
“…[29] constructed a novel approach that extends the fuzzy DEA model utilizing the local α-cut, addressing some of the drawbacks of the α-cut method, particularly its inability to encompass all uncertain information. Recent advancements in the field have witnessed the adoption of alternative fuzzy set extensions like Fermatean fuzzy sets and spherical fuzzy sets for constructing DEA models, along with the development of corresponding solution techniques, as evident in studies by [30,31,32]. Progress in the field of fuzzy sets extends beyond neutrosophic sets.…”
Section: Related Workmentioning
confidence: 99%
“…Progress in the field of fuzzy sets extends beyond neutrosophic sets. some studies introduced various other extensions, including Fermatean FS, Plithogenic Set [33], and Spherical FS was proposed by [32,34], have been successfully employed in the development of DEA models and the establishment of solution methodologies. These extensions have demonstrated significant effectiveness in aiding decision-making processes within uncertainty and have found applications across diverse domains.…”
Evaluating the performance of pharmaceutical manufacturing companies is crucial for their success and competitiveness in the rapidly evolving Egyptian market. However, traditional data envelopment analysis (DEA) approaches often overlook the inherent uncertainty in the data, which can significantly impact efficiency assessments. To address this issue, this paper proposes a novel integrated DEA model to enhance the efficiency assessment of Egyptian pharmaceutical manufacturing companies. This two-stage framework effectively handles imprecise and ambiguous data, accommodating various forms of uncertainty, including fuzzy, stochastic, and neutrosophic data, alongside deterministic data. The results demonstrate that the proposed model outperforms traditional DEA models in capturing data uncertainty and providing more accurate efficiency evaluations.
“…[29] constructed a novel approach that extends the fuzzy DEA model utilizing the local α-cut, addressing some of the drawbacks of the α-cut method, particularly its inability to encompass all uncertain information. Recent advancements in the field have witnessed the adoption of alternative fuzzy set extensions like Fermatean fuzzy sets and spherical fuzzy sets for constructing DEA models, along with the development of corresponding solution techniques, as evident in studies by [30,31,32]. Progress in the field of fuzzy sets extends beyond neutrosophic sets.…”
Section: Related Workmentioning
confidence: 99%
“…Progress in the field of fuzzy sets extends beyond neutrosophic sets. some studies introduced various other extensions, including Fermatean FS, Plithogenic Set [33], and Spherical FS was proposed by [32,34], have been successfully employed in the development of DEA models and the establishment of solution methodologies. These extensions have demonstrated significant effectiveness in aiding decision-making processes within uncertainty and have found applications across diverse domains.…”
Evaluating the performance of pharmaceutical manufacturing companies is crucial for their success and competitiveness in the rapidly evolving Egyptian market. However, traditional data envelopment analysis (DEA) approaches often overlook the inherent uncertainty in the data, which can significantly impact efficiency assessments. To address this issue, this paper proposes a novel integrated DEA model to enhance the efficiency assessment of Egyptian pharmaceutical manufacturing companies. This two-stage framework effectively handles imprecise and ambiguous data, accommodating various forms of uncertainty, including fuzzy, stochastic, and neutrosophic data, alongside deterministic data. The results demonstrate that the proposed model outperforms traditional DEA models in capturing data uncertainty and providing more accurate efficiency evaluations.
“…Again, Abdelfattah [2] in 2021 applied his proposed model [1] as an application for measuring the performance of the 32 regional hospitals of in Tunisia. Recently, other extension of FS like Fermatean fuzzy set [3] and Spherical fuzzy set [18,19] are used to construct the DEA models and its solution techniques are developed.…”
The evaluation of the performance of decision-making units (DMUs) that use comparable inputs to produce related outputs can be accomplished through a non-parametric linear programming (LP) technique called Data Envelopment Analysis (DEA). However, the observed data are occasionally imprecise, ambiguous, inadequate, and inconsistent which may result in incorrect decision-making when these criteria are ignored. Neutrosophic Set (NS) is an extension of fuzzy sets which is used to represent unclear, erroneous, missing, and wrong information. This paper proposes a neutrosophic version of the DEA model, and a novel solution technique for Neutrosophic DEA (Neu-DEA) model. The possibility mean for triangular neutrosophic number (TNN) is redefined and modified the Khatter’s approach to convert directly the Neu-DEA model into its crisp DEA model. As a result, the Neu-DEA model is simplified to a crisp LP problem with a risk parameter (δ ∈ [0, 1]) that represents the attitude of the decision-maker towards taking risk. The efficiency score of the DMUs is computed by using various risk factors and divided into efficient and inefficient groups. The ranking of DMUs is determined by calculating the mean efficiency score of DMUs, which is based on various risk parameters. A numerical example is illustrated here to describe the suggested approach’s flexibility and authenticity and compared with some of the existing approaches.
“…Advancements have not been limited to the neutrosophic sets, however. Other extensions of fuzzy set, such as Fermatean fuzzy set [35], Plithogenic Set [36], and Spherical fuzzy set [37,38], have been constructively employed to develop DEA models and establish solution techniques. These extensions have proven invaluable for decision-making under uncertainty, with widespread applications across multiple disciplines.…”
Neutrosophic sets, expanded from the constructs of fuzzy and intuitionistic fuzzy sets, can accommodate degrees of truth, indeterminacy, and falsity for each element. This attribute equips them with an aptitude for a more refined interpretation of ambiguous or uncertain data. This study presents an innovative application of Neutrosophic Data Envelopment Analysis (Neu-DEA), incorporating pentagonal neutrosophic numbers in both input and output data. This novel methodology involves the transformation of traditional DEA models into a Pentagonal neutrosophic DEA model, subsequently converting it into a Crisp Linear Programming (CrLP) model. A unique ranking function is integral to this process. Performance evaluation of decision-making units (DMUs) is accomplished through the resolution of the CrLP model, with subsequent ranking of the DMUs based on their relative efficiency scores. The utility and effectiveness of this novel technique is validated through a numerical example.
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