A fuzzy DEA model with undesirable fuzzy outputs and its application to the banking sector in India

Puri, Jolly; Yadav, Shiv Prasad

doi:10.1016/j.eswa.2014.04.013

Cited by 91 publications

(65 citation statements)

References 26 publications

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“…This being the case, several researchers (Cooper et al, 1999;Despotis and Smirlis, 2002;Guo and Tanaka, 2001;Jahanshahloo et al, 2004;Kao and Liu, 2000b) started structuring FDEA models, allowing for the measurement of outputs and inputs as fuzzy numbers. Particularly with respect to FDEA applications on banking, studies to assess efficiency in the financial sector still remain scarce, and their major focus tends to relate to ranking of DMUs based on computed fuzzy efficiencies rather than predicting or explaining efficiency levels in terms of contextual variables (Chen et al, 2013;Puri & Yadav, 2014;Puri & Yadav, 2013;Wang et al, 2014;Hsiao et al, 2011;Wu et al, 2006). According to Hatami-Marbini et al (2011a), the huge dissemination of different models within a large scope of applications in terms of efficiency measurement demonstrates that FDEA models represent an effective path for handling uncertainty and vagueness when inputs/outputs are imprecise (Kao & Liu, 2000b).…”

Section: Review Of the Literaturementioning

confidence: 99%

“…Thus far, applications of FDEA to measure bank efficiency have been scarce and focused on ranking DMUs rather than predicting their efficiency levels based on a set of contextual variables (Chen et al, 2013;Puri & Yadav, 2014;Puri & Yadav, 2013;Wang, Lu & Liu, 2014;Hsiao et al, 2011;Wu, Yang, & Liang, 2006). This paper innovates first by focusing on Mozambican banks and second by simultaneously adopting three major FDEA models based on the α-level approach in combination with the conditional bootstrapped truncated regression, proposed…”

Section: Introductionmentioning

confidence: 99%

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Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks

Wänke

Barros²,

Emrouznejad

2016

European Journal of Operational Research

138

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Section: Review Of the Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks

Wänke

Barros²,

Emrouznejad

2016

European Journal of Operational Research

138

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“…Every respondent describes his judgment about the innovation degree in his bank by the following linguistic terms; very low, low, high and very high. These linguistic expressions were converted into fuzzy numbers as (5, 6, 7), (8, 10, 11), (12,13,14) and (15,16,17), respectively. In order to establish the imprecise value of the innovation level for each bank, we used the following aggregation function.…”

Section: Used Datamentioning

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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“…Each DMU utilizes m inputs to produce s outputs in which s 1 outputs are desirable and s 2 outputs are undesirable such that s ¼ s 1 þ s 2 : Let x lk be the lth input used by DMU k , and y g rk and y b pk be the rth desirable and pth undesirable outputs produced by DMU k respectively. The technical efficiency of DMU k with the undesirable outputs can be evaluated from the following model (Puri and Yadav 2014):…”

Section: Arithmetic Operations On Tfnsmentioning

confidence: 99%

“…In DEA, there are numerous studies on the inclusion of undesirable outputs in the production process of a DMU like Scheel (2001), Seiford and Zhu (2002), Färe and Grosskopf (2004), Jahanshahloo et al (2005), Liu et al (2010), Jahanshahloo et al (2012), Ramli and Munisamy (2013), and Puri and Yadav (2014). To the best of our knowledge, there are very few studies in the literature of MC-DEA which have considered the case of undesirable factors in the production process of MC-DEA.…”

Section: Introductionmentioning

confidence: 96%

A fully fuzzy approach to DEA and multi-component DEA for measuring fuzzy technical efficiencies in the presence of undesirable outputs

Puri

Yadav

2015

Int J Syst Assur Eng Manag

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DEA is a non-parametric technique for measuring the relative technical efficiencies of similar decision making units (DMUs) with multiple inputs and multiple outputs. In smoe real life situations, DMUs may perform different types of functions and can be segregated into independent components such that each component has its own set of inputs and outputs. Owing the importance of the internal structure of the DMUs, DEA has been extended to multi-component DEA (MC-DEA) which is a technique for measuring the technical efficiencies of the DMUs and their components. The conventional DEA and MC-DEA approaches require crisp input and output data which may not always be available precisely. However, in real life problems, data might be imprecise or vague which can suitably be represented by fuzzy numbers. Many researchers have proposed methods to deal with fuzzy parameters in DEA and MC-DEA. However, in real situations, the decision variables can also take fuzzy forms. Therefore, the aim of the present paper is fourfold: (i) to extend DEA in the presence of undesirable outputs to fully fuzzy DEA (FFDEA) for measuring the fuzzy technical efficiencies of the DMUs in fully fuzzy environments where all decision variables and parameters are taken as fuzzy numbers, in particular triangular fuzzy numbers, (ii) to extend MC-DEA in the presence of undesirable outputs to multi-component fully fuzzy DEA (MC-FFDEA) for measuring the fuzzy technical efficiencies of DMUs and their components in fully fuzzy environments, (iii) to propose a new ranking function approach to transform both the FFDEA and MC-FFDEA models into crisp linear programming problems, and (iv) to present numerical examples in order to validate the effectiveness and advantages of the proposed approach over the existing ones.

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A fuzzy DEA model with undesirable fuzzy outputs and its application to the banking sector in India

Cited by 91 publications

References 26 publications

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks

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

A fully fuzzy approach to DEA and multi-component DEA for measuring fuzzy technical efficiencies in the presence of undesirable outputs

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