Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA

Rezaee, Mustafa Jahangoshai; Yousefi, Samuel; Eshkevari, Milad; Valipour, Mahsa; Saberi, Morteza

doi:10.1007/s00477-019-01754-3

Cited by 99 publications

(33 citation statements)

References 56 publications

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“…Jamshidi et al presented an FIS-based approach on relative risk score methodology in risk assessment for the pipeline [48]. Rezaee et al offered a hybrid approach based on the linguistic FMEA, FIS, and Fuzzy Data Envelopment Analysis model to overcome the shortcomings in calculating the traditional risk priority number and prioritize health, safety, and environmental risks [49]. When the studies conducted by applying the FIS approach in different fields for risk assessment are examined, no studies using the hybrid approach adopted for the textile industry have been found.…”

Section: Literature Reviewmentioning

confidence: 99%

Creating proactive behavior for the risk assessment by considering expert evaluation: a case of textile manufacturing plant

Karaşan

Erdoğan

2021

Complex Intell. Syst.

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Applying risk assessment approaches to improve quality in enterprises is of great importance especially for sectors that are labor-intensive and thus frequently encountered failures. One of the methods frequently used to take precautions against failures caused by high variability in this type of sector is failure mode and effects analysis (FMEA). In this study, a hybrid FMEA approach is proposed so as to take measures against failures in the textile sector where there are high-quality differences due to its structure and failures frequently occurred. Since the different combinations of risk parameters’ scores may produce the same risk degree based on the function of the FMEA’s basis, misleading results for the risk analysis in the practical risk management can be occurred. Moreover, the risk priority number (RPN) function has a limitation in the weight determining process, since it assigns the equal weight for each risk parameter in the classical FMEA. To overcome these shortcomings in the RPN calculation for the risks in the FMEA approach, a multi-criteria decision-making (MCDM) approach is applied under the framework of fuzzy logic. Through that, in this study, we aimed to prove an expert system based on the rules that specifically focusing on the risk sources of the woven fabric industry. To create such a rule-based system, inputs are generated using fuzzy AHP and modified fuzzy TOPSIS. A case study is carried out with the method proposed in a textile mill, and it is determined which risks arising from failures are higher. For the validation of the results, a comparative analysis is conducted. Moreover, for the robustness of the decisions, one-at-a-time sensitivity analysis with respect to different scenarios are applied. As a result of the analyses, it is shown that our proposed model can be used as an efficient proactive risk calculator for the managers or researchers to make useful inferences, judgments, and decisions of the production processes for eliminating the shortcomings of the traditional FMEA.

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Section: Literature Reviewmentioning

confidence: 99%

Creating proactive behavior for the risk assessment by considering expert evaluation: a case of textile manufacturing plant

Karaşan

Erdoğan

2021

Complex Intell. Syst.

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“…Nevertheless, the traditional FMEA has been widely used in numerous fields including manufacturing [31,32], marine [33,34], aerospace [35,36], healthcare [37][38][39] and electronics [40][41][42]. The application of crisp values in the traditional FMEA has been strongly criticized by researchers [43][44][45]. Because the traditional FMEA assumes the same importance degree for all risk factors and obtains the same RPN values for failure modes even with the different risk factors.…”

Section: Fuzzy Fmea (Phase 2)mentioning

confidence: 99%

Risk Prioritization for Failure Modes in Mining Railcars

Rahimdel

Ghodrati

2021

Sustainability

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Railway transportation systems are generally used to transport minerals from large-scale mines. Any failure in the railcar components may cause delays or even catastrophic derailment accidents. Failure mode and effect analysis (FMEA) is an effective tool for the risk assessment of mechanical systems. This method is an appropriate approach to identify the critical failure modes and provide proper control measures to reduce the level of risk. This research aims to propose an approach to identify and prioritize the failure modes based on their importance degree. To achieve this, the analytical hierarchy process (AHP) is used along with the FMEA. To compensate for the scarcities of the conventional FMEA in using the linguistic variables, the proposed approach is developed under the fuzzy environment. The proposed approach was applied in a case study, a rolling stock operated in an iron ore mine located in Sweden. The results of this study are helpful to identify not only the most important failure modes but also the most serious and critical ones.

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“…Two FIS are widely accepted and employed in the literature; the Mamdani and the Takagi-Sugeno [29]. In this paper, the Mamdani method and the max-min inference are selected as they perform better in extracting experts' opinions on risk factors, and thus it is more suitable for RIDM problems [30]. In the Mamdani method, if-then rules and the implication method are used to obtain a fuzzy output, which has to be defuzzified in a later stage for its treatment in further mathematical equations.…”

Section: Qualitative Risks Definitionmentioning

confidence: 99%

Energy-Investment Decision-Making for Industry: Quantitative and Qualitative Risks Integrated Analysis

et al. 2021

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Industrial SMEs may take the decision to invest in energy efficient equipment to reduce energy costs by replacing or upgrading their obsolete equipment or due to external socio-political and legislative pressures. When upgrading their energy equipment, it may be beneficial to consider the adoption of new energy strategies rising from the ongoing energy transition to support green transformation and decarbonisation. To face this energy-investment decision-making problem, a set of different economic and environmental criteria have to be evaluated together with their associated risks. Although energy-investment problems have been treated in the literature, the incorporation of both quantitative and qualitative risks for decision-making in SMEs has not been studied yet. In this paper, this research gap is addressed, creating a framework that considers non-risk criteria and quantitative and qualitative risks into energy-investment decision-making problems. Both types of risks are evaluated according to their probability and impact on the company’s objectives and, additionally for qualitative risks, a fuzzy inference system is employed to account for judgmental subjectivity. All the criteria are incorporated into a single cost–benefit analysis function, which is optimised along the energy assets’ lifetime to reach the best long-term energy investment decisions. The proposed methodology is applied to a specific industrial SME as a case study, showing the benefits of considering these risks in the decision-making problem. Nonetheless, the methodology is expandable with minor changes to other entities facing the challenge to invest in energy equipment or, as well, other tangible assets.

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Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA

Cited by 99 publications

References 56 publications

Creating proactive behavior for the risk assessment by considering expert evaluation: a case of textile manufacturing plant

Creating proactive behavior for the risk assessment by considering expert evaluation: a case of textile manufacturing plant

Risk Prioritization for Failure Modes in Mining Railcars

Energy-Investment Decision-Making for Industry: Quantitative and Qualitative Risks Integrated Analysis

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