Developing a Tω (the weakest t-norm) fuzzy GERT for evaluating uncertain process reliability in semiconductor manufacturing

Lin, Kuo-Ping; Wu, Ming-Jia; Hung, Kuo-Chen; Kuo, Yiyo

doi:10.1016/j.asoc.2011.05.043

Cited by 26 publications

(15 citation statements)

References 25 publications

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“…Another advantage of T ω norm based approximate fuzzy arithmetic operations is that they give smaller fuzzy accumulation within uncertain environment [34]. These advantages inspired researchers to use T ω norm for successfully decreasing growing phenomena of fuzziness in reliability assessment of complex systems.…”

Section: T-norm and The Weakest T-norm (T ω )mentioning

confidence: 99%

“…Lin et al [34] gave T ω based approximate fuzzy arithmetic operations defined on TFNs. Following same logical approach, this section introduces T ω based approximate fuzzy arithmetic operations defined for TPFNs.…”

Section: T ω Based Fuzzy Arithmetic Operations On Tpfnsmentioning

confidence: 99%

“…A t-norm is a binary function, t : [0, 1] × [0, 1] → [0, 1] which satisfies the axioms of (i) commutativity, (ii) associativity, (iii) monotonicity and (iv) boundary condition [34].…”

Section: T-norm and The Weakest T-norm (T ω )mentioning

confidence: 99%

“…To quantify uncertainty, present study uses TPFN for representing basic events' uncertain failure probabilities. For computing system top event fuzzy failure probability, T ω based approximate fuzzy arithmetic operations given by Lin et al [34] for TFN have been extended for TPFN and then applied. Here, T ω based approximate fuzzy arithmetic operations have been used because they give lesser fuzzy accumulation and consequently based on more accurate computed results, appropriate action plans could be chosen to reduce the frequency of medical errors in healthcare systems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

Komal

2015

Applied Soft Computing

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Please cite this article in press as: Komal, Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty, Appl. Soft Comput. J. (2015), http://dx.a b s t r a c t Nowadays healthcare becoming more important aspect for everybody. Healthcare institutions now giving more attention to their patients' safety by reducing the frequency of medical errors and trying to provide all kinds of best facilities to them. Clinical processes can be understood as a series of interactions between patients, providers, and technologies. Therefore, there are some chances exist for medical errors due to the involvement of human beings and machines. A number of tools exist to prospectively analyze processes in healthcare which generally needs precise numerical data. In general, available or extracted data is not precise and sufficient to assess the clinical processes upto a desired degree of accuracy due to various practical and economic reasons. Thus, collected data may have some sort of uncertainties and quantification of these uncertainties should be done very carefully before analysing further. In this paper, a new fuzzy fault tree approach has been presented for patient safety risk modeling in healthcare. This approach applies fault-tree, trapezoidal fuzzy numbers, ˛-cut set, and the weakest-t-norm (T ω ) based approximate arithmetic operations to obtain fuzzy failure probability of the system. The effectiveness of the developed approach is illustrated with two different kinds of problems taken from literature related to healthcare. Also, Tanaka et al.'s approach has been used to rank the critical basic events of the considered problems. Computed results have been compared with results obtained from other existing techniques.

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Section: T-norm and The Weakest T-norm (T ω )mentioning

confidence: 99%

Section: T ω Based Fuzzy Arithmetic Operations On Tpfnsmentioning

confidence: 99%

“…A t-norm is a binary function, t : [0, 1] × [0, 1] → [0, 1] which satisfies the axioms of (i) commutativity, (ii) associativity, (iii) monotonicity and (iv) boundary condition [34].…”

Section: T-norm and The Weakest T-norm (T ω )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

Komal

2015

Applied Soft Computing

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“…Some parameterized families of t-norms turn out to be solutions of well-known nonlinear functional equations [15]. In many problems with uncertainty as in the theory of probabilistic metric spaces [1,2,4], multi-valued logics [16,17], general measures [12,18] often we work with many operations different from the usual addition and multiplication of reals. Some of them are triangular norms, triangular conorms, pseudo-additions, pseudo-multiplications, etc.…”

Section: Introductionmentioning

confidence: 99%

On nonlinear functional spaces based on triangular conorms

Qiu

Wei-quan

2013

Journal of Mathematical Analysis and Applications

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a b s t r a c tIn this paper, we discuss the nonlinear functional spaces based on triangular conorms. Particularly, we discuss the properties of the upper-closures of the regular subspaces of the nonlinear functional space based on a continuous triangular conorm. Furthermore, we prove that with respect to a strict triangular conorm, a subset of the nonlinear functional space is an upper-complete normal subspace if and only if the family of all sets whose characteristic functionals are contained in the given subset of the nonlinear functional space is a sigma-algebra.

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An energy and time prediction model for remanufacturing process using graphical evaluation and review technique (GERT) with multivariant uncertainties

Zhao

Xue

et al. 2021

Environ Sci Pollut Res

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Developing a Tω (the weakest t-norm) fuzzy GERT for evaluating uncertain process reliability in semiconductor manufacturing

Cited by 26 publications

References 25 publications

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

Fuzzy fault tree analysis for patient safety risk modeling in healthcare under uncertainty

On nonlinear functional spaces based on triangular conorms

An energy and time prediction model for remanufacturing process using graphical evaluation and review technique (GERT) with multivariant uncertainties

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