A fuzzy data envelopment analysis approach for FMEA

Garcia, Pauli Adriano de Almada; Schirru, Roberto; E^Melo, P.F. Frutuoso

doi:10.1016/j.pnucene.2005.03.016

Cited by 122 publications

(54 citation statements)

References 5 publications

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“…Chang, Wei, and Lee (1999) used fuzzy linguistic terms such as Very Low, Low, Moderate, High and Very high to evaluate O, S and D, and utilized grey relational analysis to determine the risk priorities of potential causes. Garcia, Schirru, Frutuoso, and Melo (2005) presented a fuzzy data envelopment analysis (DEA) approach for FMEA in which typical risk factors O, S, and D were modeled as fuzzy sets, and the fuzzy possibility DEA model developed by Lertworasirikul, Fang, Joines, and Nuttle (2003) was used for determining the ranking indices among failure modes. The proposed approach was applied to a pressurized water reactor (PWR) auxiliary feed-water system.…”

Section: Literature Review Of Fuzzy Fmeamentioning

confidence: 99%

Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean

Wang

Chin

Poon

et al. 2009

Expert Systems with Applications

508

283

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Section: Literature Review Of Fuzzy Fmeamentioning

confidence: 99%

Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean

Wang

Chin

Poon

et al. 2009

Expert Systems with Applications

508

283

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“…Possibility theorem and credibility programming model have been applied to convert DEA model with fuzzy trapezoidal data into linear programming models by Lertworasirikul et al [24][25][26], Wu et al [27], and Wen and Li in [28]. Also, possible DEA model applied to establish a failure mode ranking that takes into account the linguistic variables by Garcia et al [29]. Fuzzy DEA approach has been used to compute fuzzy nonradial technical efficiency measures by Triantis [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective data envelopment analysis model in fuzzy dynamic environment with missing values

Jafarian-Moghaddam

Ghoseiri

2011

Int J Adv Manuf Technol

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Data envelopment analysis (DEA) is an important managerial tool for evaluating and improving the performance of decision making units. The existing DEA models are mostly limited to static environment using crisp data and are time-consuming and also have weak discriminating power. The aim of this work is to introduce a new fuzzy dynamic DEA model with missing values, which benefits from strengths of multi-objective modeling to overcome weakness and drawbacks of the classic DEA models. To check for quality and accuracy of the proposed model, this paper offers a comparative study to compare the discriminating power and computational efforts of the model with two problems in the literature taken as benchmarks. Also, this paper presents a real application of the fuzzy dynamic DEA model for assessing and ranking the level of performance for 56 railways around the globe using real data gathered from credible sources. The numerical case illustrates the model and the result may be used by railways to improve their performance efficiency compared to the best in the sample. Results for the comparative study and the real case reveal significant improvement in computational time and discriminating power.

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“…In order to overcome the above shortcomings, fuzzy logic is applied into the conventional FMEA in the present work [10,11]. …”

Section: Introductionmentioning

confidence: 99%

Optimization of process parameters through fuzzy logic and genetic algorithm – A case study in a process industry

Mariajayaprakash¹,

Senthilvelan²,

Gnanadass³

2015

Applied Soft Computing

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A fuzzy data envelopment analysis approach for FMEA

Cited by 122 publications

References 5 publications

Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean

Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean

Multi-objective data envelopment analysis model in fuzzy dynamic environment with missing values

Optimization of process parameters through fuzzy logic and genetic algorithm – A case study in a process industry

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