A DEA integrated grey factor analysis approach for efficiency evaluation and ranking in uncertain systems

Huang, Mengdie; Xia, Tangbin; Chen, Zhen; Pan, Ershun; Xi, Lifeng

doi:10.1016/j.cie.2021.107681

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…Charnes et al proposed that DEA is the most popular non-parametric method to measure the relative efficiency of decisionmaker units, and it was developed later on by Banker et al [8,9]. The DEA method is widely used among various fields: Bank [10][11][12], energy [13][14][15][16], educational system [17,18], entrepreneurship and innovation [19,20], software engineering [21,22]. Andreu proposed four variants of the slacks-based measure of efficiency (SBM) to evaluate the efficiency of the strategic style of pension funds [23].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China

Feng

Jiang

et al. 2022

Sustainability

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China is facing an increasingly serious aging problem, which puts forward higher requirements for the smoothness of the endowment insurance system. Accurate evaluation of the efficiency of the system can help the government to find problems and improve the system. Some scholars have used data envelopment analysis (DEA) method to measure the efficiency of endowment insurance system. However, according to the literature, the impact of government policy adjustment and economic shocks on output of the data was ignored. In this study, a robust optimization method is applied to deal with uncertainty. Robust DEA models proposed in this paper are based on three kinds of uncertainty sets. A data-driven robust optimization method is also applied to resolve the over-conservative problem. Compared with the robust DEA method, based on analysis it is found that the data-driven robust DEA method is more flexible and reliable for efficiency estimating strategies. The results of data-driven robust DEA models illustrate that the government should increase its support for the endowment insurance system, especially for the underdeveloped regions.

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Section: Introductionmentioning

confidence: 99%

Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China

Feng

Jiang

et al. 2022

Sustainability

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show abstract

“…Gong, Shao, and Zhu [36] proposed a new method for evaluating the energy efficiency of ethylene production by incorporating DEA and FA into the operation classification. Mengdie Huang et al [37] conducted their evaluation employing a multi-attribute efficiency evaluation model, a multivariate statistical method, and grey theory, a so-called DEA integrated grey FA (GFA-DEA) approach, to support efficiency evaluation and ranking in uncertain systems. In this study, an efficiency measures generation technique is integrated into the grey factor analysis method for the benchmarking of DMUs.…”

Section: Dea With Multivariate Techniques (Except Pca)mentioning

confidence: 99%

A novel algorithm for complete ranking of DMUs dealing with negative data using Data Envelopment Analysis and Principal Component Analysis: Pharmaceutical companies and another practical example

Yazdi,

Movahedi Sobhani,

Lotfi

et al. 2023

PLoS ONE

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When there is an extensive number of inputs and outputs compared to the number of DMUs, one of the drawbacks of Data Envelopment Analysis appears, which incorrectly classifies inefficient DMUs, as efficient ones. Accordingly, the DEA ranking power becomes further moderated. To improve the ranking power, this paper renders the details of an algorithm that presents a model combining the Principal Component Analysis and the Slacks-Based Measure (PCA-SBM) which reduces the number of the incorrectly determined efficient DMUs. Also to complete ranking of DMUs, the algorithm presents a Super-Efficiency model integrated with PCA (PCA-Super SBM) which can rank the efficient DMUs (extreme and non-extreme). Whereas the most important previous models for ranking efficient units cannot rank non-extreme ones. Additionally, in most previous studies, DEA models combined with PCA fail to handle negative data, while, the presented models can cover this data. Two case studies (pharmaceutical companies listed on the Iranian stock market and bank branches) are manipulated to demonstrate the applicability and performance of the algorithm. To show the superiority of the presented models, the SBM model without PCA and the Super SBM model without PCA have been implemented on the data of both cases. In comparing the two methods (PCA-SBM and SBM), the PCA-SBM model has higher ranking power (five efficient DMUs versus nineteen in the case of pharmaceutical companies and four efficient DMUs versus twenty-nine in the case of bank branches). Also in comparing the PCA-Super SBM and Super SBM, the PCA-Super SBM model works more powerfully in complete ranking. As the Super SBM model cannot rank non-extreme units unlike the PCA-Super SBM. Consequently, the presented algorithm works successfully in ranking the DMUs completely (inefficient, extreme, and non-extreme efficient) with low complexity.

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“…Li et al, 2017). According to the characteristics of the time series data, to compare and rank multiple units, this paper uses the super efficiency DEA model (SE-DEA) (Huang et al, 2021) to measure industrial efficiency, operational efficiency, and innovation efficiency. The SE-DEA model enables a comprehensive evaluation of efficiency by considering both technical and allocative efficiency.…”

Section: Variable Definition Dependentmentioning

confidence: 99%

Supply Chain Finance and Industrial Efficiency: Evidence From ICT Industry

Meng,

Wang,

Yang

2023

Sage Open

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This study explores the impact of supply chain finance on industrial efficiency and the internal mechanism. The super-efficiency model and quantitative analysis method were used based on the time series data of the ICT industry in Shenzhen from 2005 to 2020. In order to better investigate the impact of supply chain finance on industrial efficiency, this study categorized industrial efficiency into operational efficiency and innovation efficiency to reflect the durability and virtuousness of the industry development. Results indicate that supply chain finance has an inverted U-shaped nonlinear effect on industrial efficiency. Even though the long-term effect on operational efficiency is positive, the effect on innovation efficiency reveals an inverted U-shape, mainly due to the mediating effect of industrial concentration. Theoretically, this study sheds light on the intricate effects of supply chain finance at the industrial level, and also provides valuable insights into the potential inhibition mechanism, which enriches and deepens the related research on the supply chain finance from the nonlinear perspective, make the research findings more consistent with the fact of industrial development and avoid the linear stereotypes of the existing theory consensus. Practically, it is conductive to an objective understanding for managers and policymakers, so that supply chain finance can facilitate both operational and innovation efficiencies and realize the effective integration of supply chain finance and industrial development.

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A DEA integrated grey factor analysis approach for efficiency evaluation and ranking in uncertain systems

Cited by 8 publications

References 31 publications

Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China

Data-Driven Robust DEA Models for Measuring Operational Efficiency of Endowment Insurance System of Different Provinces in China

A novel algorithm for complete ranking of DMUs dealing with negative data using Data Envelopment Analysis and Principal Component Analysis: Pharmaceutical companies and another practical example

Supply Chain Finance and Industrial Efficiency: Evidence From ICT Industry

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