A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process

Vahdani, Behnam; Salimi, M.; Charkhchian, M.

doi:10.1007/s00170-014-6466-3

Cited by 126 publications

(73 citation statements)

References 25 publications

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“…In addition, it seems necessary to design and deploy an improved maintenance program for solving the problems related to line and transportation system. In this table, the results obtained by solving the problem using FBS-TOPSIS method of Jiang et al (2011) andVahdani et al (2014) is also presented. As it is clear from Table 5 results, except for 6th and 7th ranks, all other alternatives obtained similar ranks in both methods.…”

Section: Case Studymentioning

confidence: 97%

“…In fact, the more important risk is one that has higher severity; its occurrence is high, or its detection is more difficult. The classic FMEA has some weaknesses as noted by Vahdani et al (2014):…”

Section: Case Studymentioning

confidence: 98%

“…Jiang et al (2011) applied the FBS model to extend the TOP-SIS method for group decision-making. Vahdani et al (2014) also applied the above TOPSIS and FBS method in the Failure Mode and Effect Analysis (FMEA) for ranking the risk causes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Belief Structure Based Vikor Method: An Application for Ranking Delay Causes of Tehran Metro System by Fmea Criteria

et al. 2016

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Public transport is a critical part of civilization in this decade. The amount of money invested and the criticality of transferring people in an acceptable time and without any conflict made it a challenging problem for managers, especially in metropolises. Absolutely, making effective decisions in this area requires considering different aspects. Waiting time is a key criterion in apprising quality of public transport. In this paper, a real world case study of ranking causes of delay in Tehran (Iran) metro system is solved by developing multi attribute group decision-making VIšeKriterijumska Optimizacija I KOmpromisno Rešenje (in Serbian, VIKOR) method under uncertainty, where this uncertainty is captured by Fuzzy Belief Structures (FBS). The obtained results are then compared with a previously proposed Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method with FBSs. The results show that human related issues, along with the problems related to line and transportation system are the most important causes of delay. The obtained results of the problem seem acceptable for decision makers.

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Section: Case Studymentioning

confidence: 97%

Section: Case Studymentioning

confidence: 98%

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Fuzzy Belief Structure Based Vikor Method: An Application for Ranking Delay Causes of Tehran Metro System by Fmea Criteria

et al. 2016

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“…For a recent review on fuzzy MCDM techniques, reader may refer to Mardani, Jusoh, and Zavadskas (2015). Some researchers have in fact merged multi-criteria techniques and fuzzy logic to accommodate the imprecision of the evaluations: fuzzy technique for order preference by similarity to ideal solution (TOPSIS) (Braglia, Frosolini, & Montanari, 2003;Hadi-Vencheh & Aghajani, 2013;Liu et al, 2011;Liu, Liu, Liu, & Mao, 2012;Vahdani, Salimi, & Charkhchian, 2015); VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) with fuzzy logic (Liu et al, 2012); fuzzy AHP (Hu, Hsu, Kuo, & Wu, 2009;Kutlu & Ekmekçioglu, 2012); fuzzy logic with grey theory (Chang, Wei, & Lee, 1999); or simply applied fuzzy logic on the risk factors (Petrović et al, 2014). Mandal and Maiti (2014) adopted the similarity measure of fuzzy numbers in order to overcome the drawback of standard de-fuzzification approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

FlowSort-GDSS – A novel group multi-criteria decision support system for sorting problems with application to FMEA

Lolli

Ishizaka

Gamberini

et al. 2015

Expert Systems with Applications

117

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a b s t r a c tFailure mode and effects analysis (FMEA) is a well-known approach for correlating the failure modes of a system to their effects, with the objective of assessing their criticality. The criticality of a failure mode is traditionally established by its risk priority number (RPN), which is the product of the scores assigned to the three risk factors, which are likeness of occurrence, the chance of being undetected and the severity of the effects. Taking a simple ''unweighted'' product has major shortcomings. One of them is to provide just a number, which does not sort failures modes into priority classes. Moreover, to make the decision more robust, the FMEA is better tackled by multiple decision-makers. Unfortunately, the literature lacks group decision support systems (GDSS) for sorting failures in the field of the FMEA.In this paper, a novel multi-criteria decision making (MCDM) method named FlowSort-GDSS is proposed to sort the failure modes into priority classes by involving multiple decision-makers. The essence of this method lies in the pair-wise comparison between the failure modes and the reference profiles established by the decision-makers on the risk factors. Finally a case study is presented to illustrate the advantages of this new robust method in sorting failures.

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“…Several MCDM methods have been applied to FMECA, with a trend towards incorporating them with fuzzy logic [4], e.g. fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) ( [5]; [6]; [7]; [8]); VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje) with fuzzy logic [9]; fuzzy AHP ( [10]; [11]); fuzzy logic with grey theory [12]; or simply fuzzy logic applied to the risk factors [13]. The adoption of subjective criteria to either rank or sort a set of alternatives results in a group decision problem arising.…”

Section: Introductionmentioning

confidence: 99%

Fmeca-Based Optimization Approaches Under an Evidential Reasoning Framework

Lolli¹,

Gamberini²,

Balugani³

et al. 2018

dtetr

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One of the major shortcomings of traditional failure modes, effects and criticality analysis is the absence of any interconnection between failure ranking and a procedure for selecting the most critical maintenance/improvement tasks to be carried out. This limits the potential of FMECA for implementation in real environments. In order to bridge this gap, three different 0-1 knapsack models have been formulated. The first aims to select the failures in order to maximise cost savings. The second enriches the selection problem by also taking into account the probabilities of solving the failures with a set of maintenance tasks. The third aims to select the maintenance tasks to maximise the expected profit. In particular, the last two models make use of an evidential reasoning framework to deal with the epistemic uncertainty related to these probabilities. A dataset from a manufacturer of lift winches has been used to validate this proposal, as well as to comment on the need for group decision support systems that are capable of converting the FMECA ranking into maintenance tasks in real environments.

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A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process

Cited by 126 publications

References 25 publications

Fuzzy Belief Structure Based Vikor Method: An Application for Ranking Delay Causes of Tehran Metro System by Fmea Criteria

Fuzzy Belief Structure Based Vikor Method: An Application for Ranking Delay Causes of Tehran Metro System by Fmea Criteria

FlowSort-GDSS – A novel group multi-criteria decision support system for sorting problems with application to FMEA

Fmeca-Based Optimization Approaches Under an Evidential Reasoning Framework

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