Recent Developments in Fuzzy Dynamic Data Envelopment Analysis and Its Applications

Kaur, Rajinder; Puri, Jolly

doi:10.1007/978-981-19-8566-9_10

Cited by 1 publication

(1 citation statement)

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“…Building on this, subsequent studies by [35,36,7,37], and others applied fuzzy network DEA. Further expansions in the approaches to encounter the negative or zero values in inputs and outputs of the data were suggested by [38,39,40,41,42,43,44] by proposing a dynamic fuzzy network DEA approach. Moreover, [26] introduced an innovative fuzzy multiple objective network DEA approach.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Efficiency Assessment of Egyptian Pharmaceutical Companies through the Usage of Two-Stage Uncertainty Data Envelopment Analysis Algorithm

El-Demerdash,

Ming,

Abelrahman

2024

Preprint

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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.

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