Mohsen Omidvar, Adel Mazlomi, Iraj Mohammadfam, Abbas Rahimi Foroushani, Fereshteh Nirumand,
Volume 6, Issue 3 (9-2016)
Abstract
Introduction: Resilience engineering (RE), as a new approach in the system safety domain, is intended to preserve the performance of socio-technical systems in various conditions; and accentuates the positive activities instead of the failure modes. The aim of this study was to develop a new framework for safety assessment on the basis of RE, using the Fuzzy Analytical Hierarchy Process (AHP) method.
Material and Method: Current study is an analytical cross-sectional survey performed in a petrochemical industry. Initially, six RE indicators were selected, including top management commitment, just culture, learning culture, awareness, flexibility and emergency preparedness and accordingly an assessment framework was established. Then, the selected RE indicators were evaluated and validated by experts in a specialized panel. Following, an indicator was proposed named “resilience early warning indicator”. Finally, the RE indicator score of the total process was determined using the fuzzy evaluating vector.
Result: Findings revealed that top management commitment and learning indicators have the most and the least effects on the RE level of the process, respectively. Besides, the flexibility (C3) indicator was located in orange early warning zone (OEWZ) while other indicators were positioned in the no early warning zone (NEWZ). Furthermore, the overall resilience level of the process was evaluated as level III (NEWZ).
Conclusion: Management commitment and emergency preparedness are two main indicators of RE and can carry out the most important effect for remaining the RE in the NEWZ level.
Mohsen Omidvar, Fereshteh Nirumand,
Volume 7, Issue 1 (4-2017)
Abstract
Introduction: FMEA method is one of the most used techniques in risk assessment and prioritization. But, due to several reasons, its application has been limited to the real-world settings. The aim of this study was to deal with these restrictions using the combined fuzzy (in terms of the Z-numbers) and grey (in terms of the grey relational analysis) theories.
Material and Method: The current study is an analytical cross-sectional survey that was performed to prioritize the failure modes of the overhead cranes. Initially, an FMEA team including 4 specialists was established. Then, the opinions of the team members were gathered in terms of the Z-numbers and the weights of the risk factors (O، S, and D) were determined using the fuzzy AHP method. Finally, the failure modes were prioritized using the GRP method.
Result: From 13 cases of the identified failure modes, the conventional FMEA was assigned equal priority to the 7 cases and as a result 9 risk priorities were determined. But, in the proposed method, because of the elimination of the restrictions of conventional FMEA, 13 risk priorities were assigned to the failure modes.
Conclusion: Relying upon the fuzzy AHP, Z-numbers and GRP method, the proposed method dealt with the equal weights of the risk factors, fuzziness of the data (expert’s judgments) and the prioritization of the failure modes. The proposed method has more capabilities in relation to the conventional FMEA for prioritization of the failure modes.
Farideh Golbabaei, Mohsen Omidvar, Fereshteh Nirumand,
Volume 8, Issue 4 (12-2018)
Abstract
Introduction: Working in hot and harsh weather conditions can cause heat related diseases and in some cases, even can lead to death. Risk assessment of heat stress in these environments is of particular importance. As there are many factors that could affect the heat stress, therefore, an index should be applied that could properly reflect the effect of all of these factors.
Material and Method: Initially a five-member expert team was established. Then, the weight of each variable was determined by the fuzzy analytical hierarchy process (FAHP) method. In next step, five work stations of the casting process evaluated applying fuzzy TOPSIS (FTOPSIS) method and the risk of heat stress prioritized in these stations. Lastly, the Pearson’s correlation coefficient was used to determine correlation between the results of proposed method with WBGT index.
Result: The weights of three main variables including task characteristics, working environment, and worker characteristics was determined as 0.279, 0.526, and 0.195. The risk priority of the five work stations including, stocking, melting furnace, pouring and casting, polishing, and warehousing was established as S1= 4, S2= 2, S3= 1, S4= 3, and S5= 5. The Pearson’s correlation coefficient between the similarity index (CCi) and WBGT was 0.97.
Conclusion: From three main variables that can affect the heat stress, “Working Environment” has main impact in the risk assessment process; therefore, the most efforts must be focused on controlling this variable. The proposed method in this study has the capability of concurrent quantitative and qualitative assessment of factors that could affect the heat stress and can minimize the uncertainties in the risk assessment process relying upon the fuzzy sets.
