Fuzzy DEA: a perceptual evaluation method

Guo, Peijun; Tanaka, Hideo

doi:10.1016/s0165-0114(99)00106-2

Cited by 373 publications

(196 citation statements)

References 9 publications

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“…In this approach the main idea is to obtain the fuzzy efficiency scores of the DMUs using fuzzy linear programs which require ranking fuzzy sets. The fuzzy ranking approach was initially developed by Guo and Tanaka [36]. Tlig and Rebai [23] proposed an approach based on the ordering relations between LR-fuzzy numbers to solve the primal and the dual of FCCR.…”

Section: Dea and Fdea Modelsmentioning

confidence: 99%

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

Tlig¹,

Hamed²

2017

Data Envelopment Analysis and Decision Science

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The banking sector is of great importance to Tunisian's economy. Major commercial banks continue to spend high proportion of their budgets on new technologies and innovation in order to satisfy their customers and enhance their competitiveness. Consequently, performance analysis has become part of their management practices.This paper aims to evaluate the efficiency of commercial Tunisian banks in terms of several crisp and imprecise data. Two approaches of fuzzy data envelopment analysis (FDEA), the possibility approach and the approach based on relations between fuzzy numbers (BRONF), are used to obtain the efficiency score of each bank. The results show that, in a competitive environment, no-financial inputs and outputs should be taken into account in order to obtain credible and realistic efficiency scores.

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Section: Dea and Fdea Modelsmentioning

confidence: 99%

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

Tlig¹,

Hamed²

2017

Data Envelopment Analysis and Decision Science

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“…According to Theorems 2, if the objective functions minimizing in (13) are deleted from the model, the optimal solution for inputs and outputs will be arisen at its endpoints of interval of fuzzy numbers. Furthermore, if the objective function maximizing in (13) is eliminated, Theorem 1 is adopted and its optimal solutions are fuzzy number.…”

Section: Discussionmentioning

confidence: 99%

“…Sengupta [26] applied principle of fuzzy set theory to introduce fuzziness in the objective function and the right-hand side vector of the conventional DEA model [3]. Guo and Tanaka [13] used the ranking method and introduced a bi-level programming model. Lertworasirikul [20] developed the method in which first, inputs and outputs were defazified and then the model was solved using the α-cut approach.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy data envelopment analysis: A discrete approach

Angiz

Emrouznejad

Mustafa

2012

Expert Systems with Applications

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Data envelopment analysis (DEA) as introduced by Charnes et al [3] is a linear programming technique that has widely been used to evaluate the relative efficiency of a set of homogenous decision making units (DMUs). In many real applications, the input-output variables cannot be precisely measured. This is particularly important in assessing efficiency of DMUs using DEA, since the efficiency score of inefficient DMUs are very sensitive to possible data errors. Hence, several approaches have been proposed to deal with imprecise data. Perhaps the most popular fuzzy DEA model is based on -cut. One drawback of the -cut approach is that it cannot include all information about uncertainty. This paper aims to introduce an alternative linear programming model that can include some uncertainty information from the intervals within the α-cut approach. We introduce the concept of "local α-level" to develop a multiobjective linear programming to measure the efficiency of DMUs under uncertainty. An example is given to illustrate the use of this method.

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“…The example is taken from Guo and Tanaka [18]. The fuzzy inputs and fuzzy outputs are given in Table 2.…”

Section: Fuzzy Numerical Examplementioning

confidence: 99%

An input relaxation model for evaluating congestion in fuzzy DEA

Kheirollahi

Hessari

Charles

et al. 2017

Croat. Oper. Res. Rev.

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Abstract. This paper develops a BCC input relaxation model for identifying input congestion as a severe form of inefficiency of decision-making units in fuzzy data envelopment analysis. The possibility approach is presented to obtain the models equivalent to fuzzy models. We use a one-model approach to determine input congestion based on the BCC input relaxation model. A numerical example is given to illustrate the proposed model and identify the congestion with precise and imprecise data. The proposed model is also used to determine the congestion in 16 hospitals using four fuzzy inputs and two fuzzy outputs with a symmetrical triangular membership function.

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Fuzzy DEA: a perceptual evaluation method

Cited by 373 publications

References 9 publications

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis

Fuzzy data envelopment analysis: A discrete approach

An input relaxation model for evaluating congestion in fuzzy DEA

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