Document Type : Research Paper


Department of Industrial Management, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.


A system's approach depends on the low malfunction of the equipment and processes of that system, and maintenance plays an essential role in achieving this goal. In addition, over time, the equipment quality decreases, and a quality transfer from controlled to uncontrolled mode may occur, characterized by an increase in the rate of return of the product and the tendency to fail. One of the methods that researchers have widely used in analyzing the risk of net operations is the analysis of the effect and failure modes to identify critical failure modes and focus planning and net resources on them. In analyzing the effect and failure modes, one of the essential steps is prioritizing the equipment to determine the critical equipment, as well as determining the fundamental failure modes and prioritizing them to plan the net operation purposefully. This paper aims to dynamically rank equipment in intuitionistic fuzzy environments with interval values ​​to identify and prioritize critical equipment and present a mathematical model for combining optimization of preventive maintenance intervals and control parameters. For this purpose, a model is presented that calculates the dynamic weights of each piece of equipment according to the conditions of each piece of equipment in the indicators of failure probability, failure consequence, and lack of fault detection power. Therefore, dynamic ranking is provided for the equipment. In this research, for dynamic prioritization of equipment, the method of analysis of the ratio of intuitionistic fuzzy gradual weighting with quantitative values ​​(IVIF-SWARA) was presented. Then, a mathematical model was presented for the identified critical equipment. The proposed model can determine the optimal value of each of the four decision variables, i.e., sample size, inspection rotation time, control limit coefficient, and preventive repair intervals of each of the critical equipment of the Northern Oil Pipeline and Telecommunication Company and the total expected cost of integration per unit. Minimize time. The results show that the proposed model is much more flexible in calculating equipment's weight and dynamic rating and provides more logical rating results.


Main Subjects

[1]   Sinha, Y., & Steel, J. A. (2015). A progressive study into offshore wind farm maintenance optimisation using risk based failure analysis. Renewable and sustainable energy reviews, 42, 735–742.
[2]   Wang, L. E., Liu, H. C., & Quan, M. Y. (2016). Evaluating the risk of failure modes with a hybrid MCDM model under interval-valued intuitionistic fuzzy environments. Computers and industrial engineering, 102, 175–185. DOI:10.1016/j.cie.2016.11.003
[3]   Yeter, B., Garbatov, Y., & Guedes Soares, C. (2020). Risk-based maintenance planning of offshore wind turbine farms. Reliability engineering and system safety, 202, 107062. DOI:10.1016/j.ress.2020.107062
[4]   Okoro, U., Kolios, A., & Cui, L. (2017). Multi-criteria risk assessment approach for components risk ranking-the case study of an offshore wave energy converter. International journal of marine energy, 17, 21–39.
[5]   Dickerson, D. E., & Ackerman, P. J. (2016). Risk-based maintenance management of U.S. public school facilities. Procedia engineering, 145, 685–692. DOI:10.1016/j.proeng.2016.04.069
[6]   Ghasemi, P., & Babaeinesami, A. (2020). Simulation of fire stations resources considering the downtime of machines: a case study. Journal of industrial engineering and management studies, 7(1), 161–176.
[7]   Pourghader Chobar, A., Adibi, M. A., & Kazemi, A. (2021). A novel multi-objective model for hub location problem considering dynamic demand and environmental issues. Journal of industrial engineering and management studies, 8(1), 1–31.
[8]   Lotfi, R., Kargar, B., Gharehbaghi, A., & Weber, G. W. (2022). Viable medical waste chain network design by considering risk and robustness. Environmental science and pollution research, 29(53), 79702–79717.
[9]   Lotfi, R., Safavi, S., Gharehbaghi, A., Ghaboulian Zare, S., Hazrati, R., & Weber, G. W. (2021). Viable supply chain network design by considering blockchain technology and cryptocurrency. Mathematical problems in engineering, 2021, 1–18. DOI:10.1155/2021/7347389
[10] Lotfi, R., Kargar, B., Hoseini, S. H., Nazari, S., Safavi, S., & Weber, G. W. (2021). Resilience and sustainable supply chain network design by considering renewable energy. International journal of energy research, 45(12), 17749–17766. DOI:10.1002/er.6943
[11] Zhao, H., You, J. X., & Liu, H. C. (2017). Failure mode and effect analysis using MULTIMOORA method with continuous weighted entropy under interval-valued intuitionistic fuzzy environment. Soft computing, 21(18), 5355–5367. DOI:10.1007/s00500-016-2118-x
[12] Liu, P. (2017). Multiple attribute group decision making method based on interval-valued intuitionistic fuzzy power Heronian aggregation operators. Computers & industrial engineering, 108, 199–212.
[13] Baykasoğlu, A., & Gölcük, İ. (2020). Comprehensive fuzzy FMEA model: a case study of ERP implementation risks. Operational research, 20(2), 795–826. DOI:10.1007/s12351-017-0338-1
[14] Huang, J., Li, Z., & Liu, H. C. (2017). New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method. Reliability engineering and system safety, 167, 302–309.
[15] Nazeri, A., & Naderikia, R. (2017). A new fuzzy approach to identify the critical risk factors in maintenance management. International journal of advanced manufacturing technology, 92(9–12), 3749–3783.
[16] Mirghafouri, S. H., Asadian Ardakani, F., & Azizi, F. (2014). Developing a method for risk analysis in tile and ceramic industry using failure mode and effects analysis by data envelopment analysis. Interdisciplinary journal of management studies (formerly known as Iranian journal of management studies), 7(2), 343-363. (In Persian).
[17] Mkandawire, B. O. B., Ijumba, N., & Saha, A. (2015). Transformer risk modelling by stochastic augmentation of reliability-centred maintenance. Electric power systems research, 119, 471–477.
[18] Sayyadi Tooranloo, H., & Ayatollah, A. sadat. (2016). A model for failure mode and effects analysis based on intuitionistic fuzzy approach. Applied soft computing journal, 49, 238–247. DOI:10.1016/j.asoc.2016.07.047
[19] Safari, F. M., Etebari, F., & Chobar, A. P. (2021). Modeling and optimization of a tri-objective transportation-location-routing problem considering route reliability: using MOGWO, MOPSO, MOWCA, and NSGA-II. Journal of optimization in industrial engineering, 14(2), 99–114. DOI:10.22094/JOIE.2020.1893849.1730
[20] Garg, H., Agarwal, N., & Tripathi, A. (2017). Some improved interactive aggregation operators under interval-valued intuitionistic fuzzy environment and their application to decision making process. Scientia Iranica, 24(5), 2581–2604. DOI:10.24200/sci.2017.4386
[21] Lad, B. K., & Kulkarni, M. S. (2012). Optimal maintenance schedule decisions for machine tools considering the user’s cost structure. International journal of production research, 50(20), 5859–5871.
[22] Mojabi, P., & LoVetri, J. (2009). Overview and classification of some regularization techniques for the Gauss-Newton inversion method applied to inverse scattering problems. IEEE transactions on antennas and propagation, 57(9), 2658–2665. DOI:10.1109/TAP.2009.2027161