Fuzzy data envelopment analysis (DEA): a possibility approach

Lertworasirikul, Saowanee; Fang, Shu‐Cherng; Joines, Jeffrey A.; Nuttle, Henry L. W.

doi:10.1016/s0165-0114(02)00484-0

Cited by 358 publications

(241 citation statements)

References 15 publications

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“…Another popular approach is the possibility approach. Guo et al [45] initially built fuzzy DEA models based on possibility and necessity measures and then Lertworasirikul [46] and Lertworasirikul et al [22] have proposed two methods for solving the ranking problem in fuzzy DEA models called the ''possibility models'' and the ''credibility models.'' They used the possibility approach from both optimistic and pessimistic view points by considering the uncertainty in fuzzy objectives and fuzzy constraints with possibility measures.…”

Section: Dea and Fdea Modelsmentioning

confidence: 99%

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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%

“…In order to obtain the efficiency scores, we used the BRONF approach proposed by Tlig and Rebai [23] and the possibility approach developed by Lertworasirikul et al [22]. The BRONF approach is a technique based on relations between fuzzy numbers.…”

Section: Used 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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“…Source: Guo and Tanaka (2001) Fuzzy efficiencies of DMUs using standard fuzzy DEA model (2) and with different α value solved by the method suggested in [24] is reported in Table (2). The results of the possibility approach introduced in [19] have given in Table (4). The results shown in the first row of Table 4 are more accurate than the results suggested in [24], as seen in Theorem 2, Kao and Liu [16] model obtained the results at endpoints of the intervals only.…”

Section: An Application and Comparison With Other Methodsmentioning

confidence: 99%

“…Lertworasirikula [19] considered each constraint in the DEA as a fuzzy event; hence he transferred fuzzy DEA model to possibility linear programming problem. Lertworasirikul et al [21] further developed a fuzzy BCC model where the possibility and credibility approaches are provided and compared with an α -cut level based approach for solving the FDEA models.…”

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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“…For instance, Khodabakhshi, Gholami, and Kheirollahi [22] used an additive model to provide an alternative approach for estimating the returns to scale in both stochastic DEA and fuzzy DEA. Lertworasirikul et al [25] introduced the possibility, credibility, and necessity approaches to solve fuzzy DEA models. They explained that by using the possibility, credibility, and necessity approaches, the relationship between the primal and dual models of the fuzzy BCC models is revealed and fuzzy efficiency can be constructed.…”

Section: Introductionmentioning

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 data envelopment analysis (DEA): a possibility approach

Cited by 358 publications

References 15 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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