An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA

Boral, Soumava; Howard, Ian; Chaturvedi, Sanjay K.; McKee, Kristoffer K.; Naikan, V. N. A.

doi:10.1016/j.engfailanal.2019.104195

Cited by 146 publications

(77 citation statements)

References 47 publications

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“…The MAIRCA method was first published in papers [16,17]. Since then it has been used in a large number of papers in its initial version [18,19,20,21,22,23] or as a modified MAIRCA method -in fuzzy and rough environments [13,24,25,26,27,28,29,30]. Modified IR-MAIRCA method has seven steps [13,27].…”

Section: Ir-mairca Methodsmentioning

confidence: 99%

A Hybrid Lbwa - Ir-Mairca Multi-Criteria Decision-Making Model for Determination of Constructive Elements of Weapons

et al. 2020

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The paper demonstrates a model whose goal is to define the construction elements of weapons necessary to meet minimum requirements of users. The complexity of the problem, conditioned by different construction elements of weapons and specific situations of their use, is the reason for using methods of multi-criteria decision-making. In the paper we used the hybrid LBWA – IR-MAIRCA model. With this model, one can conduct an analysis of characteristics of the existing weapons, based on which we define the construction elements for modifying the existing and manufacturing new weapons. Regarding a large number of different types of weapons, the paper is limited to the analysis of close-quarters combat weapons. The LBWA method was used to calculate weight coefficients of the criteria. The MAIRCA method, which was modified by interval rough numbers, was used to select the best close-quarters combat weapon that has the best characteristics in accordance with the requests of the users. Based on the analysis, the users have the option to clearly and precisely define requests for improvement of the existing, and manufacturing new weapons.

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Section: Ir-mairca Methodsmentioning

confidence: 99%

A Hybrid Lbwa - Ir-Mairca Multi-Criteria Decision-Making Model for Determination of Constructive Elements of Weapons

et al. 2020

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“…To this end, Buckley's fuzzy AHP method with triangular fuzzy numbers is utilized. The Buckley model is one of the most common approaches in studies applying to fuzzy AHP in many different areas [56]- [63]. The steps of the Buckley's fuzzy AHP method can be explained as follows [64], [65]:…”

Section: Comparative Analysismentioning

confidence: 99%

Prioritizing the Factors for Customer-Oriented New Product Design in Industry 4.0

Erdoğan

Bilişik

2020

Sakarya University Journal of Science

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Customer-oriented new product design is one of the most important processes in the production environment to improve product quality and reliability and maximize their productivity. It is also necessary to consider customer expectations in this process for an effective design. In this paper, we present a methodology which is called Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) for prioritizing criteria which should be considered for an efficient customer-oriented new product design in Industry 4.0 transition primarily. We use Pythagorean Fuzzy Sets (PFSs) to allow experts to make more flexible evaluations and handle the uncertain and vague information in a wider way. We determine five main and eighteen sub-criteria that affect the new product design process and after applying PF-AHP, we find that the most important main-criterion determined as "Production" and sub-criterion determined as "Return on Investment".

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“…Faulty product is one of the main problems faced by companies. This problem does not only result in financial loss but also causes loss of prestige (Boral et al 2020). To continue the activities of the companies in a healthy and to make profit in today's overwhelming competitive environment, they must increase their production quality and decrease the number of faulty products [39].…”

Section: Introductionmentioning

confidence: 99%

A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method

Yücesan

Gül

Çelik

2021

Complex Intell. Syst.

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Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity (S), occurrence (O), and detection (D), and to find the preference values of the failure modes according to parameters S and D. On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter O in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.

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An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA

Cited by 146 publications

References 47 publications

A Hybrid Lbwa - Ir-Mairca Multi-Criteria Decision-Making Model for Determination of Constructive Elements of Weapons

A Hybrid Lbwa - Ir-Mairca Multi-Criteria Decision-Making Model for Determination of Constructive Elements of Weapons

Prioritizing the Factors for Customer-Oriented New Product Design in Industry 4.0

A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method

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