Application of Fuzzy Risk Analysis for Selecting Critical Processes in Implementation of SPC with a Case Study

Khorshidi, Hadi Akbarzadeh; Gunawan, Indra; Nikfalazar, Sanaz

doi:10.1007/s10726-015-9439-5

Cited by 12 publications

(8 citation statements)

References 21 publications

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“…The optimal weights for criteria are obtained by solving problem in Eq. (12). We use the optimal weights to update the decision cloud matrix.…”

Section: Stage 2: Decision Matrix Construction 1) Construct the Decismentioning

confidence: 99%

“…[ Table 4] Now, we can determine the weights of criteria using the information in the decision cloud matrix by solving the bi-level optimisation model formulated in Eq. (12). The optimal weights are shown in Table 5.…”

Section: Implementation Of the Proposed Mcgdmmentioning

confidence: 99%

“…This concept has attracted much interest and many techniques have been developed to deal with opinions which are expressed by words [10]. In these techniques, the words are typically encoded into quantity using triangular fuzzy numbers [11], generalized trapezoidal fuzzy numbers [12,13], type-2 fuzzy sets [14], hesitant fuzzy sets [15] or cloud models [16,17]. Then, quantitative analyses are applied, and the results are decoded into recommendations.…”

Section: Introductionmentioning

confidence: 99%

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Multicriteria group decision-making under uncertainty using interval data and cloud models

Khorshidi

Aickelin

2020

Journal of the Operational Research Society

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In this study, we propose a multicriteria group decision-making (MCGDM) algorithm under uncertainty where data is collected as intervals. The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input. The intervals give flexibility to experts in assessing alternatives against criteria and provide an opportunity to gain maximum information. We also propose a novel method to aggregate experts' judgements using cloud models. We introduce an experimental approach to check the validity of the aggregation method. After that, we use the aggregation method for an MCGDM problem. Here, we find the optimal weights for each criterion by proposing a bi-level optimisation model. Then, we extend the technique for order of preference by similarity to ideal solution (TOPSIS) for data based on cloud models to prioritise alternatives. As a result, the algorithm can gain information from decisionmakers with different levels of uncertainty and examine alternatives with no more information from decisionmakers. The proposed MCGDM algorithm is implemented on a case study of a cybersecurity problem to illustrate its feasibility and effectiveness. The results verify the robustness and validity of the proposed MCGDM using sensitivity analysis and comparison with other existing algorithms.

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“…The optimal weights for criteria are obtained by solving problem in Eq. (12). We use the optimal weights to update the decision cloud matrix.…”

Section: Stage 2: Decision Matrix Construction 1) Construct the Decismentioning

confidence: 99%

Section: Implementation Of the Proposed Mcgdmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multicriteria group decision-making under uncertainty using interval data and cloud models

Khorshidi

Aickelin

2020

Journal of the Operational Research Society

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“…The critical processes are selected for implementing statistical process control (SPC) via occurrence likelihood and severity of their failure modes [31]. In fuzzy risk analysis, two factors of severity and failure probability are usually used to evaluate the risk of components [32].…”

Section: B System Behavior Modellingmentioning

confidence: 99%

Data-Driven System Reliability and Failure Behavior Modeling Using FMECA

Khorshidi

Gunawan

Ibrahim

2016

IEEE Trans. Ind. Inf.

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Authors and/or their employers shall have the right to post the accepted version of IEEE-copyrighted articles on their own personal servers or the servers of their institutions or employers without permission from IEEE, provided that the posted version includes a prominently displayed IEEE copyright notice (as shown in 8.1.9.B, above) and, when published, a full citation to the original IEEE publication, including a Digital Object Identifier (DOI). Authors shall not post the final, published versions of their articles.

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“…In general, fuzzy risk analysis is investigated based on either similarity measures or ranking. To deal with complicated problems, fuzzy risk analysis has been applied based on both similarity measures and ranking of fuzzy numbers [55]. The similarity measures are commonplace method which have attracted many researchers to measure the degree of similarity in fuzzy risk analysis.…”

Section: Introductionmentioning

confidence: 99%

Semantic Risk Analysis Based on Single-Valued Neutrosophic Sets

Gou

Wang

2019

IEEE Access

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Fuzzy risk analysis is diffusely applied in risk assessment of components by the semantic model. Due to the fuzzy characteristic in the process of fuzzy risk analysis, analysis parameters are imprecise and vague. Therefore, the determination of the risk of failure is challenging part of fuzzy risk analysis with existing methods. Hence, in this paper, a semantic risk analysis method based on the technique for order performance by similarity to ideal solution (TOPSIS) under a single-valued neutrosophic set (SVNS) is presented. First, a five-member linguistic term set is introduced and these linguistic terms are expressed in terms of the generalized trapezoidal fuzzy numbers. Then, the linguistic term is transformed into the SVNS and generated SVNS is fused by single-valued neutrosophic prioritized weighted average (SVNPWA) operator. On this basis, the TOPSIS approach is used to obtain the final rank to ascertain future risk. Finally, a fuzzy risk analysis example is conducted to illustrate the effectiveness of the proposed method. Further, the out-performance of the proposed method is illustrated in comparisons to the existing methods. INDEX TERMS Fuzzy risk analysis, linguistic term, SVNS, SVNPWA operator, TOPSIS.

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Application of Fuzzy Risk Analysis for Selecting Critical Processes in Implementation of SPC with a Case Study

Cited by 12 publications

References 21 publications

Multicriteria group decision-making under uncertainty using interval data and cloud models

Multicriteria group decision-making under uncertainty using interval data and cloud models

Data-Driven System Reliability and Failure Behavior Modeling Using FMECA

Semantic Risk Analysis Based on Single-Valued Neutrosophic Sets

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