Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS

Song, Wenyan; Ming, Xinguo; Wu, Zhihua; Zhu, Bao Ting

doi:10.1080/0951192x.2013.785027

Cited by 116 publications

(60 citation statements)

References 33 publications

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“…However, when the proposed approach is applied, the relative significances of FM 1 and FM 5 are different, i.e. 1 5 Q Q >   , and FM 1 has a higher risk than FM 5 . A similar situation can be found for the failure modes FM 3 , FM 7 and FM 9 .…”

Section: Comparative Discussionmentioning

confidence: 99%

“…The application steps of FMEA generally include: analyze the given process, product or service, list of all potential failure modes, evaluate their frequency, severity and detection, prioritize the identified failure modes, and develop corrective actions to eliminate or reduce the critical failure modes 1,3 . Nowadays, with its simplicity and visibility characteristics, FMEA has been widely applied in various areas, such as the aerospace, automotive, nuclear, and healthcare industries [4][5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

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Failure Mode and Effect Analysis using Soft Set Theory and COPRAS Method

Wang¹,

You²,

Liu³

et al. 2017

IJCIS

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Failure mode and effect analysis (FMEA) is a risk management technique frequently applied to enhance the system performance and safety. In recent years, many researchers have shown an intense interest in improving FMEA due to inherent weaknesses associated with the classical risk priority number (RPN) method. In this study, we develop a new risk ranking model for FMEA based on soft set theory and COPRAS method, which can deal with the limitations and enhance the performance of the conventional FMEA. First, trapezoidal fuzzy soft set is adopted to manage FMEA team members' linguistic assessments on failure modes. Then, a modified COPRAS method is utilized for determining the ranking order of the failure modes recognized in FMEA. Especially, we treat the risk factors as interdependent and employ the Choquet integral to obtain the aggregate risk of failures in the new FMEA approach. Finally, a practical FMEA problem is analyzed via the proposed approach to demonstrate its applicability and effectiveness. The result shows that the FMEA model developed in this study outperforms the traditional RPN method and provides a more reasonable risk assessment of failure modes.

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Section: Comparative Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Failure Mode and Effect Analysis using Soft Set Theory and COPRAS Method

Wang¹,

You²,

Liu³

et al. 2017

IJCIS

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“…Using entropy for determining weight is based on the idea that a criterion is more important if it creates a greater dissemination in the evaluations of the alternatives. The superiority of applying the entropy method for obtaining the weight of a criterion is that it avoids the subjectivity of DMs in determining the weight but includes the objective assessment instead, and it delivers an advantage when DMs conflict concerning the values of weights (Song, Minga, Wua, & Zhua, 2013). In this study, the objective weight is obtained by using the entropy method.…”

Section: Mhe Selection: the Criteria Literaturementioning

confidence: 98%

A hybrid approach for selecting material handling equipment in a warehouse

Saputro

Rouyendegh

2015

International Journal of Management Science and Engineering Man

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Warehouse operations are closely related to material handling activities. Loading, unloading, transporting and picking material constitute a huge part of the activities. In order to handle material properly as well as to contribute value to the material, the operator and the environment, utilizing Material Handling Equipment (MHE) is required. The selection of proper MHEs requires great focus since its consideration is linked to mutli-criteria and multi-objective decision making problems. Here, a hybrid method is proposed to address the MHE selection problem. An approach that integrates the entropy based hierarchical fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Multi-Objective Mixed Integer Linear Programming (MOMILP) is used for seeking the best alternative. The evaluation of alternatives is performed based on both subjective and objective criteria. Subjective weights are derived from a fuzzy Analytic Hierarchy Process (AHP). To deal with objective criteria, the entropy method is adopted to determine the weights, and the integrated weights are also calculated. The alternatives are rated by using fuzzy TOPSIS. For final execution of the selection, an MOMILP model is developed incorporating two goals, namely to minimize the disadvantage of material handling operation and to minimize the total cost of material handling. The AUGMented E-CONtraint method (AUGMECON) is used to solve the model. A case study is given to illustrate the method. The results show the effectiveness of the hybrid method in complex decision making.

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“…The final results are shown in Table 11. To demonstrate the effectiveness of the IF-TOPSISEF method for MADM problems, we compare the results of the example by analyzing the case with some similar computational approaches including the fuzzy TOPSIS model by Braglia1 et al (2003), the integrated weight-based fuzzy TOPSIS (IWF-TOPSIS) by Song et al (2013), the intuitionistic fuzzy hybrid TOPSIS (IFH-TOPSIS) approach by Liu et al (2015) and the risk priority number (RPN) method. The final ranking results are shown in Table 12.…”

Section: Tablementioning

confidence: 99%

Extended TOPSIS model for solving multi-attribute decision making problems in engineering

Aikhuele¹,

Turan²

2017

10.5267/j.dsl

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In this paper, we propose an exponential-related function (ER) and develop an intuitionistic fuzzy TOPSIS model based on the function (IF-TOPSISEF) to solve multi-attribute decision making (MADM) problems in which the performance ratings are expressed in intuitionistic fuzzy sets (IFSs). The main advantage of this new approach is that the exponential-related function is able to represent the aggregated effect of the positive and negative evaluations in the performance ratings of alternatives based on the intuitionistic fuzzy set (IFS) data. It also serves as a mean for the computations of the separation measures of each alternative from the intuitionistic fuzzy positive and negative ideal solutions to determine the relative closeness coefficients. To demonstrate the effectiveness of the proposed method, the proposed IF-TOPSISEF is applied for the evaluation of the concept designs of a part in an HDD machine (The drill pipe slider), and for some hypothetical examples.Growing Science Ltd. All rights reserved. 7

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Failure modes and effects analysis using integrated weight-based fuzzy TOPSIS

Cited by 116 publications

References 33 publications

Failure Mode and Effect Analysis using Soft Set Theory and COPRAS Method

Failure Mode and Effect Analysis using Soft Set Theory and COPRAS Method

A hybrid approach for selecting material handling equipment in a warehouse

Extended TOPSIS model for solving multi-attribute decision making problems in engineering

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