Robust non-radial data envelopment analysis models under data uncertainty

Hatami-Marbini, Adel; Arabmaldar, Aliasghar; Toloo, Mehdi; Nehrani, Ali Mahmoodi

doi:10.1016/j.eswa.2022.118023

Cited by 5 publications

(2 citation statements)

References 83 publications

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“…The objective is to maintain solution stability in the face of uncertain conditions. Marbini et al [63] pioneered the development of novel robust non-radial DEA models, specifically designed to gauge the performance of decision-making units under conditions of data uncertainty. Their approach involves the utilization of Interval DEA, enabling the assessment of interval efficiencies based on both optimistic and pessimistic viewpoints.…”

Section: Stochastic Optimization For Deamentioning

confidence: 99%

“…In the majority of resource allocation DEA models, the input and output data are known and precise, while in real-world problems, these data are often unavailable or erroneous. The stochastic DEA models on resource allocation [59][60][61][62][63][64][65] deal with uncertainty, however, to the best of our knowledge, the DMUs under examination have no bilevel structure.…”

Section: The Proposed Stochastic Frameworkmentioning

confidence: 99%

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A Bilevel DEA Model for Efficiency Evaluation and Target Setting with Stochastic Conditions

Georgiou,

Kaparis,

Vretta

et al. 2024

Mathematics

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The effective allocation of limited resources and the establishment of targeted goals play a pivotal role in enhancing the overall efficiency of large enterprises and organizations. To achieve optimal organizational efficiency, managers seek dynamic strategies that adapt to the constraints of limited and uncertain historical data. This paper introduces an evaluation of organizational efficiency through a stochastic framework, employing a bilevel data envelopment analysis (DEA) approach. This decision-making process is centralized within a decision-making unit (DMU) overseeing subordinate decision-making units (subDMUs). Discrete scenarios, each associated with a realization probability, define the uncertain parameters in the bilevel DEA-based model. This stochastic approach allows for recourse actions upon scenario realization leading to an enhanced overall organizational strategy. Decision-makers acting within uncertain and dynamic environments can benefit from this research since it allows the investigation of efficiency assessment under alternative scenarios in the presence of volatility and risk. The potential impact of applying this methodology varies depending on the specific domain. Although, the context of this paper focuses on banking, in general, enhancing resource allocation and target setting under stochasticity, contributes to advancing sustainability across all its three dimensions (economic, environmental, social). As mentioned earlier, the practical application of our approach is demonstrated via a case study in the banking sector.

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Section: Stochastic Optimization For Deamentioning

confidence: 99%