Risk Prioritization in Failure Mode and Effects Analysis with Extended SWARA and MOORA Methods Based on Z-Numbers Theory

Ghoushchi, Saeid Jafarzadeh; Gharibi, Kazhal; Osgooei, Elnaz; Rahman, Mohd Nizam Ab; Khazaeili, Mohammad

doi:10.15388/20-infor439

Cited by 35 publications

(17 citation statements)

References 59 publications

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“…The Z‐number theory developed by Zadeh 23 is an ideal instrument to describe the fuzzy preferences of decision‐makers and their judgmental reliability by an ordered pair ( A , B ), where A reflects the fuzzy restriction of an uncertain object and B indicates the reliability of A . The Z‐number theory has been also employed for the FMEA improvement, such as the references 14,30 . However, the traditional Z‐number theory only employs numeric numbers to present the values of the elements A and B , which ignores the randomness of experts’ opinions and the preferred and non‐preferred degrees of the uncertain object.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Z-number theory has been also employed for the FMEA improvement, such as the references. 14,30 However, the traditional Z-number theory only employs numeric numbers to present the values of the elements A and B, which ignores the randomness of experts' opinions and the preferred and non-preferred degrees of the uncertain object. For the manipulation of randomness and vagueness of personal preferences, the cloud model is a powerful instrument and can flexibly and effectively manipulate the fuzziness and randomness of uncertain preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The initial assessment matrix of relative weights of risk factors using Z-numbers Motivated by reference, 30 the codebooks associated with linguistic variables of judgmental reliability are exhibited in Table 5. According to Definition 2 and Definition 3, experts use IVIFNs to express their opinions on the risk levels of failure modes.…”

Section: Ta B L Ementioning

confidence: 99%

“…Step 6: Identify the combinative importance of risk factors This paper set θ 2 = 0.5 based on the references. 51 Therefore, based on Equation (30), the synthetic weight of risk factors is identified as ω = (0.2495, 0.1322, 0.1106, 0.0958, 0.1348, 0.1103, 0.1669).…”

Section: Ta B L Ementioning

confidence: 99%

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An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Xiao

Huang

Zhang

2022

Quality & Reliability Eng

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As a systematic risk assessment instrument, failure mode, and effects analysis (FMEA) has been extensively applied for enhancing systems’ quality. However, the classical FMEA technique has limitations in many application scenarios. Although fuzzy logic‐based risk assessment techniques are introduced to enhance conventional FMEA, they still have limitations, for example, needing prior presumptions, neglecting decision‐makers’ bounded rationality, and lacking the capability to reflect the judgmental reliability and randomness of experts’ evaluations simultaneously. To cover these limitations, an extended FMEA method is presented in this paper by using Z‐fuzzy clouds and generalized TODIM (An acronym in Portuguese of interactive and multiple attribute decision‐making). First, a novel concept of Z‐fuzzy clouds is proposed to handle various factors of uncertainties by combining the advantages of the Z‐number theory, the interval‐valued intuitionistic fuzzy sets theory, and the cloud model theory, and the basic operations and Minkowski‐type distance measure of Z‐fuzzy clouds are also defined and discussed. Furthermore, more risk factors are added into the conventional three risk factors and to further build a multiple dimension risk factor system for better evaluating failures. Additionally, the experts’ bounded rationality and the risk factors’ interrelationships are considered by two developed weighting models. Finally, a real study case, sensitivity analysis, and several comparisons are performed to validate the effectiveness of the proposed model. Results demonstrate that the proposed model is more rational compared with the previous several methods.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Ta B L Ementioning

confidence: 99%

Section: Ta B L Ementioning

confidence: 99%

See 2 more Smart Citations

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Xiao

Huang

Zhang

2022

Quality & Reliability Eng

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“…In order to meet the real-time distributed storage network education big data, and be good at log analysis, it can further analyze the user behavior data generated during the learning process of students and teachers [34][35][36]. Hadoop is a distributed system infrastructure developed by the Apache Foundation.…”

Section: ) Hadoop Clustermentioning

confidence: 99%

Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

Sharma

2021

IJCAI

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Big data technology plays an important role in optimizing the education intelligence by enhancing the learning experience through novel assessment strategies and predictive teaching. This work aims at improving the learning efficiency of users and increase the effective learning quality of the smart classroom concept using the big data technology. A regional network education information collection platform based on big data technology has been developed, which can collect student learning data for subsequent analysis and processing. The software architecture of this platform is mainly divided into basic layer, platform layer and access layer. The physical structure mainly includes web servers and Hadoop clusters. The big data acquisition platform of this education has excellent acquisition performance, and the data acquisition of different data item field length is larger than the expected index. The outcomes obtained reveals that the data item field is less than 20 and the acquisition amount is 145% of the expected result. When the data item field is between 20 and 40, the acquisition amount is 137% of the expected result. The collection is 116% of the expected result, when the data item field is between 40 and 50. When the data item field is between 50 and 60, the collection is 103% of the expected result. The comparison of the regular teaching method with the smart classroom based concept revealed that the big data platform is 87% better than the normal regular teaching methods in terms of study material as well as meaningful teacher-student interaction. The research on education big data can provide better education services for educational activities, drive the reform of teaching mode and optimize the teaching methods of education enabling the smart classroom concept.Povzetek: Predstavljena je regionalna platforma za upravljanje pametne učilnice na osnovi tehnologije velikih podatkov.

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Failure mode and effect analysis using the hesitant intuitionistic fuzzy hybrid GRP approach with ordered comprehensive weights

et al. 2022

Quality & Reliability Eng

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Failure mode and effect analysis (FMEA), a multidisciplinary reliability analysis tool based on team evaluations, has been widely used in various industries. There are three critical issues in FMEA: the conversion of linguistic evaluations, the weights of risk factors, and the ranking mechanism of failure modes. Scholars have used various fuzzy theories and multi-attribute decision-making (MADM) methods to improve traditional FMEA, but there are still deficiencies. In this paper, the hesitant intuitionistic fuzzy set (HIFS), a concept that combines the intuitionistic fuzzy set (IFS) and the hesitant fuzzy set (HFS), is introduced into FMEA to convert linguistic evaluations. Some operators based on HIFS are proposed to process the converted data. Among them, a hesitant intuitionistic fuzzy comprehensive weighted Hamming distance (HIFCWHD) operator is proposed to compute the ordered comprehensive weight, effectively weakening the effect of extreme scores on results. The gray relational projection (GRP) method is adopted to determine the risk priority order of the failure modes. Finally, we give an illustrative case to demonstrate the effectiveness of the proposed FMEA method.

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Risk Prioritization in Failure Mode and Effects Analysis with Extended SWARA and MOORA Methods Based on Z-Numbers Theory

Cited by 35 publications

References 59 publications

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

Failure mode and effect analysis using the hesitant intuitionistic fuzzy hybrid GRP approach with ordered comprehensive weights

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