Theoretical perspectives of trade-off analysis using DEA

Asmild, Mette; Paradi, Joseph C.; Reese, David N.

doi:10.1016/j.omega.2004.11.002

Cited by 33 publications

(18 citation statements)

References 9 publications

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“…This approach eliminates the hassle of running the DEA model for each and every DMU in the sample. Asmild et al (2006) proposed methods for evaluating non-marginal tradeoffs between variables used in a DEA model. These methods are capable of handling scalar as well as additive changes.…”

Section: Dea Model Used For Bankruptcy Assessmentmentioning

confidence: 99%

DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

Premachandra

Bhabra

Sueyoshi

2009

European Journal of Operational Research

143

100

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Section: Dea Model Used For Bankruptcy Assessmentmentioning

confidence: 99%

DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

Premachandra

Bhabra

Sueyoshi

2009

European Journal of Operational Research

143

100

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“…It is unsuitable to compare an input with an output directly. As mentioned in [37], it is not suitable to compare the importance between the input variables and output variables, for these two kinds of variables are not substitutional, but complementary in DEA models. Therefore, in this step we normalized the non-zero input weights and output weights separately.…”

Section: Remarkmentioning

confidence: 99%

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

2014

Entropy

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Data Envelopment Analysis (DEA) is a non-parametric method for evaluating the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. In the traditional DEA models, the DMU is allowed to use its most favorable multiplier weights to maximize its efficiency. There is usually more than one efficient DMU which cannot be further discriminated. Evaluating DMUs with different multiplier weights would also be somewhat irrational in practice. The common weights DEA model is an effective method for solving these problems. In this paper, we propose a methodology combining the common weights DEA with Shannon's entropy. In our methodology, we propose a modified weight restricted DEA model for calculating non-zero optimal weights. Then these non-zero optimal weights would be aggregated to be the common weights using Shannon's entropy. Compared with the traditional models, our proposed method is more powerful in discriminating DMUs, especially when the inputs and outputs are numerous. Our proposed method also keeps in accordance with the basic DEA method considering the evaluation of the most efficient and inefficient DMUs. Numerical examples are provided to examine the validity and effectiveness of our proposed methodology.

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“…To overcome the problem of nondifferentiability, Podinovski and Førsund (2010) gave an explicit definition of differential characteristics on a nondifferentiable efficient frontier and proposed a directional-derivative approach to calculate elasticity measures without any simplifying assumptions. They applied differential characteristics to the DEA frontier and addressed elasticity measures and marginal rates of substitution (Asmild et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Directional marginal productivity: a foundation of meta-data envelopment analysis

Lee

2017

Journal of the Operational Research Society

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Differential characteristics of the production function represent elasticity measures and marginal rates of production technologies; in particular, marginal productivity (MP) plays an important role in economic theory and applications. This study provides a theoretical foundation of directional marginal productivity (DMP) supporting the meta-data envelopment analysis (meta-DEA) which measures the efficiency via marginal-profitmaximized orientation. In addition, the segmented marginal rate of technical substitution is developed based on DMP. In fact, DMP is developed to address finding the improving direction of the efficient firm on the frontier towards the marginal profit maximization. This approach, which emphasizes ''planning'' over ''efficiency evaluation'', forms the basis for transforming a typical ''ex-post'' DEA into an ''ex-ante'' DEA study. Two case studies show that the DMP provides an explicit span of directions for productivity improvement via a trade-off between these distinct directions.

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Theoretical perspectives of trade-off analysis using DEA

Cited by 33 publications

References 9 publications

DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique

Determining Common Weights in Data Envelopment Analysis with Shannon’s Entropy

Directional marginal productivity: a foundation of meta-data envelopment analysis

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