An integrated fuzzy approach based failure mode and effects analysis for a risk assessment

Year 2022, Volume: 11 Issue: 2, 678 - 693, 30.06.2022

Burak Efe

https://doi.org/10.17798/bitlisfen.1088988

Abstract

This paper provides to cope with the limitations of traditional FMEA by using an integrated fuzzy multi-criteria decision making method, which considers fuzzy extension of AHP (Analytic Hierarchy Process) and fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and a linear programming. The proposed method is shown for an application to failure mode and effects analysis (FMEA) based risk assessment of a construction firm. Firstly, fuzzy extension of AHP approach is utilized to define the weights of criteria in risk evaluation. Secondly, fuzzy TOPSIS approach is used to determine the most important failure mode in the construction firm. This work handles a sensitivity analysis and a comparison with the other methods. FMEA related papers in the literature presents only ranking of failure modes by using various methods. This study aims to handle the limited resources such as budget and time in a linear programming to establish a suitable occupational health and safety policy.

Keywords

Risk evaluation, mathematical model, FMEA, fuzzy TOPSIS, fuzzy extension of AHP

References

• [1] Bowles, J.B., Peláez, C.E. (1995). Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis. Reliability Engineering & System Safety, 50(2), 203–213.
• [2] Sankar, N.R., Prabhu, B.S. (2001). Modified approach for prioritization of failures in a system failure mode and effects analysis. International Journal of Quality & Reliability Management, 18(3), 324–336.
• [3] Efe, B., Kurt, M., Efe, Ö. F. (2017). An Integrated Intuitionistic Fuzzy Set And Mathematical Programming Approach For An Occupational Health And Safety Policy. Gazi University Journal of Science, 30(2), 73-95.
• [4] Liu, H.C., Liu, L., Lin, Q.L. Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology (2013) IEEE Transactions on Reliability, 62 (1), 23-36.
• [5] Liu H. C., You J. X., You X. Y., Shan M. M. (2015). A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method, Applied Soft Computing 28, 579–588.
• [6] Jee, T.L., Tay, K.M., Lim, C.P., A New Two-Stage Fuzzy Inference System-Based Approach to Prioritize Failures in Failure Mode and Effect Analysis (2015) IEEE Transactions on Reliability, 64 (3), 869-877.
• [7] Efe, B., Yerlikaya, M.A., Efe, Ö.F. (2016). İş Güvenliğinde Bulanık Promethee Yöntemiyle Hata Türleri ve Etkilerinin Analizi: Bir İnşaat Firmasında Uygulama. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(2), 126-137.
• [8] Zhou, Q., Thai, V.V., Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction, Safety Science, (2016), 83, 74-79.
• [9] Liu, H. C., You, J. X., Li, P., & Su, Q. (2016). Failure mode and effect analysis under uncertainty: An integrated multiple criteria decision making approach. IEEE Transactions on Reliability, 65(3), 1380-1392.
• [10] Mohsen, O., Fereshteh, N. (2017). An extended VIKOR method based on entropy measure for the failure modes risk assessment – A case study of the geothermal power plant (GPP). Safety Science, 92, 160-172.
• [11] Tian, Z.P., Wang, J.Q., Zhang, H.Y., 2018. An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. App. S. Comp. 72, 636–646.
• [12] Fattahi, R., Khalilzadeh, M., 2018. Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Saf. Sci. 102, 290–300.
• [13] Efe, B. (2019). Analysis of operational safety risks in shipbuilding using failure mode and effect analysis approach. Ocean Engineering. 187, 106214.
• [14] Yazdani, M., Abdi, M.R., Kumar, N., Keshavarz-Ghorabaee, M., Chan, F.T., 2019. Improved decision model for evaluating risks in construction projects. J. Cons. Eng. Man. 145 (5), 04019024.
• [15] Yazdi, M., Nedjati, A., Zarei, E., & Abbassi, R. (2020). A reliable risk analysis approach using an extension of best-worst method based on democratic-autocratic decision-making style. Journal of Cleaner Production, 256, 120418.
• [16] Zhu, J., Shuai, B., Li, G., Chin, K. S., & Wang, R. (2020). Failure mode and effect analysis using regret theory and PROMETHEE under linguistic neutrosophic context. Journal of Loss Prevention in the Process Industries, 104048.
• [17] Olcer, A.I., Odabasi, A.Y., 2005. A new fuzzy multiple attributive group decision making methodology and its application to propulsion/manoeuvring system selection problem. European Journal of Operational Research, 166, 93–114.
• [18] Saaty, T. L., 1980. The analytic hierarchy process. New York: McGraw-Hill.
• [19] Chang, D. Y., 1996. Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research 95(3) 649–655.
• [20] Efe, B. (2016). An integrated fuzzy multi criteria group decision making approach for ERP system selection. Applied Soft Computing, 38, 106-117.
• [21] Shaw, K., Shankar, R., Yadav, S. S., Thakur, L. S., 2012. Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications 39, 8182-8192.
• [22] Deng, Y., Chan, F. T. S., 2011. A new fuzzy dempster MCDM method and its application in supplier selection, Expert Systems with Applications, 38(8), 9854–9861.
• [23] Wang, J. J., Yang, D. L. (2007). Using a hybrid multi-criteria decision aid method for information systems outsourcing. Computers & Operations Research, 34(12), 3691 – 3700.
• [24] Xu, Z., 2009. An automatic approach to reaching consensus in multiple attribute group decision making. Computers & Industrial Engineering, 56, 1369–1374.
• [25] Chen, S.M., 1998. Aggregating fuzzy opinions in the group decision-making environment. Cybernetics and Systems 29, 363–376.
• [26] Hwang, C. L., Yoon, K., 1981. Multiple attributes decision making methods and applications. Berlin: Springer.
• [27] Junior F. R. L., Osiro L., Carpinetti L. C. R., 2014. A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing 21, 194–209.