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FMEA ANALYSIS IN MECHANICAL INSTALLATION PROJECT BASED ON BEST WORST AND NEUTROSOPHIC AHP INTEGRATED MODEL

Year 2020, , 363 - 382, 26.01.2020
https://doi.org/10.17755/esosder.569291

Abstract

References

  • Abdel-Basset, M., Mohamed, M., & Sangaiah, A. K. (2018a). Neutrosophic AHP-Delphi Group, decision making model, based on trapezoidal neutrosophic numbers. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1427-1443.
  • Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). A hybrid neutrosophic group ANP-TOPSIS framework for supplier selection problems. Symmetry, 10(6), 226.
  • Abdel-Basset, M., Mohamed, M., Zhou, Y., & Hezam, I. (2017). Multi-criteria group decision making based on neutrosophic analytic hierarchy process. Journal of Intelligent & Fuzzy Systems, 33(6), 4055-4066.
  • Abdelgawad, M., & Fayek, A. R. (2010). Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. Journal of Construction Engineering and Management, 136(9), 1028-1036.
  • Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99-106.
  • Akyuz, E., & Celik, E. (2018). A quantitative risk analysis by using interval type-2 fuzzy FMEA approach: the case of oil spill. Maritime Policy & Management, 45(8), 979-994.
  • Alava, M. V., Figueroa, S. P. D., Alcivar, H. M. B., & Vázquez, M. L. (2018). Single Valued Neutrosophic Numbers and Analytic Hierarchy Process for Project Selection. Neutrosophic Sets & Systems, 21.
  • Alonso, J. A., & Lamata, M. T. (2006). Consistency in the analytic hierarchy process: a new approach. International journal of uncertainty, fuzziness and knowledge-based systems, 14(04), 445-459.
  • Amigun, B., Petrie, D., & Görgens, J. (2011). Economic risk assessment of advanced process technologies for bioethanol production in South Africa: Monte Carlo analysis. Renewable Energy, 36(11), 3178-3186.
  • Carbone, T. A., & Tippett, D. D. (2004). Project risk management using the project risk FMEA. Engineering Management Journal, 16(4), 28-35.
  • Carlsson, B. (2004). Initial risk analysis of potential failure modes. In Performance and Durability Assessment (pp. 147-157).
  • Chang, K. H., Chang, Y. C., & Tsai, I. T. (2013). Enhancing FMEA assessment by integrating grey relational analysis and the decision making trial and evaluation laboratory approach. Engineering Failure Analysis, 31, 211-224.
  • Fattahi, R., & Khalilzadeh, M. (2018). Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Safety Science, 102, 290-300.
  • Fink, O., Zio, E., Weidmann, U. (2014). Predicting component reliability and level of degradation with complex-valued neural networks. Reliability Engineering & System Safety, 121(1), pp. 198-206.
  • Gamal, A., Ismail, M., & Smarandache, F. A Scientific Decision Framework for Supplier Selection under Neutrosophic Moora Environment. Peer Reviewers, 33.
  • Gul, M. (2018). Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: The case of a gun and rifle barrel external surface oxidation and coloring unit. International journal of occupational safety and ergonomics, (just-accepted), 1-26.
  • Gul, M., & Guneri, A. F. (2016). A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. Journal of Loss Prevention in the Process Industries, 40, 89-100.
  • Gul, M., Ak, M. F., & Guneri, A. F. (2017). Occupational health and safety risk assessment in hospitals: A case study using two-stage fuzzy multi-criteria approach. Human and Ecological Risk Assessment: An International Journal, 23(2), 187-202.
  • Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31.
  • Hui M., E. C., Fai Lau, O. M., & Lo, K. K. (2009). A fuzzy decision‐making approach for portfolio management with direct real estate investment. International Journal of Strategic Property Management, 13(2), 191-204.
  • Jannadi, O. A., & Almishari, S. (2003). Risk assessment in construction. Journal of construction engineering and management, 129(5), 492-500.
  • Jiang, W., Xie, C., Zhuang, M., & Tang, Y. (2017). Failure mode and effects analysis based on a novel fuzzy evidential method. Applied Soft Computing, 57, 672-683.
  • Johnston, G. (2000). Reliability for technology, engineering, and management, by paul kales. Technimetrics . 42(2), pp. 207-207.
  • Kadoić, N., Ređep, N. B., & Divjak, B. (2017, January). Decision Making with the Analytic Network Process. In The 14th International Symposium on Operational Research in Slovenia.
  • Leu, S. S., & Chang, C. M. (2013). Bayesian-network-based safety risk assessment for steel construction projects. Accident Analysis & Prevention, 54, 122-133.
  • Linder, E., Patil, G. P., & Vaughan, D. S. (1987). Application of event tree risk analysis to fisheries management. Ecological Modelling, 36(1-2), 15-28.
  • Lindhe, A., Rosén, L., Norberg, T., & Bergstedt, O. (2009). Fault tree analysis for integrated and probabilistic risk analysis of drinking water systems. Water research, 43(6), 1641-1653.
  • Liu, H. C., Fan, X. J., Li, P., & Chen, Y. Z. (2014). Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment. Engineering Applications of Artificial Intelligence, 34, 168-177.
  • 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.
  • Mentes, A., & Helvacioglu, I. H. (2011). An application of fuzzy fault tree analysis for spread mooring systems. Ocean Engineering, 38(2-3), 285-294.
  • Miao, X., Yu, B., Xi, B., & Tang, Y. H. (2010). Modeling of bilevel games and incentives for sustainable critical infrastructure system. Technological and Economic Development of Economy, 16(3), 365-379.
  • Misra, K. B., & Weber, G. G. (1990). Use of fuzzy set theory for level-I studies in probabilistic risk assessment. Fuzzy Sets and Systems, 37(2), 139-160.
  • Mohammadi, A., & Tavakolan, M. (2013, June). Construction project risk assessment using combined fuzzy and FMEA. In IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint (pp. 232-237). IEEE.
  • Mou, Q., Xu, Z., & Liao, H. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224-239.
  • Nawaz, F., Asadabadi, M. R., Janjua, N. K., Hussain, O. K., Chang, E., & Saberi, M. (2018). An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowledge-Based Systems, 159, 120-131.
  • Ouédraogo, A., Groso, A., & Meyer, T. (2011). Risk analysis in research environment–part II: weighting lab criticity index using the analytic hierarchy process. Safety science, 49(6), 785-793.
  • Peeters, J. F. W., Basten, R. J., & Tinga, T. (2018). Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner. Reliability engineering & system safety, 172, 36-44.
  • Pinto, A. (2014). QRAM a qualitative occupational safety risk assessment model for the construction industry that incorporate uncertainties by the use of fuzzy sets. Safety Science, 63, 57-76.
  • Pramanik, S., Dalapati, S., & Roy, T. K. (2018). Neutrosophic multi-attribute group decision making strategy for logistics center location selection. Neutrosophic Operational Research, 3, 13-32.
  • Razaque, A., Bach, C., & Alotaibi, A. (2012). Fostering project scheduling and controlling risk management. arXiv preprint arXiv:1210.2021.
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  • Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
  • Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577-588.
  • Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42(23), 9152-9164.
  • Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal Operations Research, 48(1), 9-26.
  • Salimi, N., & Rezaei, J. (2016). Measuring efficiency of university-industry Ph. D. projects using best worst method. Scientometrics, 109(3), 1911-1938.
  • Schuhmacher, M., Meneses, M., Xifró, A., & Domingo, J. L. (2001). The use of Monte-Carlo simulation techniques for risk assessment: study of a municipal waste incinerator. Chemosphere, 43(4-7), 787-799.
  • Shojaei, P., Haeri, S. A. S., & Mohammadi, S. (2018). Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. Journal of Air Transport Management, 68, 4-13.
  • Singh M, & Sarkar D. (2017), Project Risk Analysis for Elevated Metro Rail Projects using Fuzzy Failure Mode and Effect Analysis (FMEA), International Journal of Engineering Technology Science and Research,4,(11).
  • Smarandache, F., & Vlăduțescu, Ş. (2013). Communication vs. Information, an Axiomatic Neutrosophic Solution. Infinite Study.
  • Song, W., Ming, X., Wu, Z., & Zhu, B. (2014). A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Quality and Reliability Engineering International, 30(4), 473-486.
  • Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. ASQ Quality Press.
  • Su, X., Deng, Y., Mahadevan, S., & Bao, Q. (2012). An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades. Engineering Failure Analysis, 26, 164-174.
  • Thomas A. Carbone & Donald D. Tippett (2004) Project Risk Management Using the Project Risk FMEA, Engineering Management Journal, 16:4, 28-35.
  • Turskis, Z., Zavadskas, E. K., & Peldschus, F. (2009). Multi-criteria optimization system for decision making in construction design and management. Engineering economics, 61(1).
  • Ustinovichius, L., Barvidas, A., Vishnevskaja, A., & Ashikhmin, I. V. (2009). Multicriteria verbal analysis for the decision of construction problems. Technological and Economic Development of Economy, 15(2), 326-340.
  • Vahdani, B., Salimi, M., & Charkhchian, M. (2015). A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process. The International Journal of Advanced Manufacturing Technology, 77(1-4), 357-368.
  • Vílchez, J. A., Espejo, V., & Casal, J. (2011). Generic event trees and probabilities for the release of different types of hazardous materials. Journal of Loss Prevention in the Process Industries, 24(3), 281-287.
  • Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications, 36(2), 1195-1207.
  • Zavadskas, E. K., Turskis, Z., & Tamošaitiene, J. (2010). Risk assessment of construction projects. Journal of civil engineering and management, 16(1), 33-46.
  • Zeng, S. X., Tam, C. M., & Tam, V. W. (2015). Integrating safety, environmental and quality risks for project management using a FMEA method. Engineering Economics, 66(1).
  • Zeng, Sai X, Tam, Chun M., Vivian W. Y. Tam, (2010), Integrating Safety, Environmental and Quality Risks for Project Management Using a FMEA Method,Inzinerine Ekonomika-Engineering Economics, 21, (1).
  • Zhang, Z., & Chu, X. (2011). Risk prioritization in failure mode and effects analysis under uncertainty. Expert Systems with Applications, 38(1), 206-214.
  • 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.
  • Zhi-Qiang, Hou; YA-MEI, Zeng. Research on risk assessment technology of the major hazard in harbor engineering. Procedia engineering, 2016, 137: 843-848.
  • Zhou, Q., & Thai, V. V. (2016). Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction. Safety science, 83, 74-79.

MEKANİK TESİSAT PROJELERİNDE BEST WORST VE NÖTROSOFİK AHP ENTEGRE MODELİYLE HATA TÜRÜ VE ETKİLERİ ANALİZİ

Year 2020, , 363 - 382, 26.01.2020
https://doi.org/10.17755/esosder.569291

Abstract



Hata türü ve etkileri analizi (FMEA), sistemlerdeki, tasarımlardaki,
ürünlerdeki, projelerdeki veya projelerdeki güvenliği iyileştirmeyi,
tanımlamayı, değerlendirmeyi ve en aza indirmeyi hedefleyen bir risk
değerlendirme aracıdır. Risk önceliği numarası (RPN), FMEA bazlı risk değerlendirme
uygulamaları için temel değerlendirme kriteridir. RPN'in oluşum (O), şiddeti
(S) ve tespiti (D) gibi risk faktörlerine dayanarak uygun şekilde
hesaplanmalıdır. Bununla birlikte, geleneksel RPN hesaplama yöntemi, risk
faktörlerinin ağırlığını göz önünde bulundurmamak gibi birçok nedenden dolayı
ağır biçimde eleştirilmiştir. Geleneksel FMEA yönteminin bu dezavantajının
üstesinden gelmek için, bu makale, mekanik tesisat projeleri için
Best
Worst method
(BWM) ve Nötrosofik analitik hiyerarşi süreci (NAHP) ile
entegre modelini içeren FMEA modelini önermektedir. FMEA parametresi S, O ve
D'nin ağırlıkları, BWM yöntemiyle hesaplanır ve bu da, tutarlı bir şekilde
ikili karşılaştırma ve güvenilir sonuçlar sağlar. Daha sonra dokuz arıza modu,
NAHP metodu ile S, O ve D parametreleri açısından değerlendirilir. Arıza
modlarının risk önceliklerini belirlemek için risk öncelik endeksleri
hesaplanır. Son olarak, belirlenen arıza modları için önleyici tedbirler
önerilmiştir.




References

  • Abdel-Basset, M., Mohamed, M., & Sangaiah, A. K. (2018a). Neutrosophic AHP-Delphi Group, decision making model, based on trapezoidal neutrosophic numbers. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1427-1443.
  • Abdel-Basset, M., Mohamed, M., & Smarandache, F. (2018). A hybrid neutrosophic group ANP-TOPSIS framework for supplier selection problems. Symmetry, 10(6), 226.
  • Abdel-Basset, M., Mohamed, M., Zhou, Y., & Hezam, I. (2017). Multi-criteria group decision making based on neutrosophic analytic hierarchy process. Journal of Intelligent & Fuzzy Systems, 33(6), 4055-4066.
  • Abdelgawad, M., & Fayek, A. R. (2010). Risk management in the construction industry using combined fuzzy FMEA and fuzzy AHP. Journal of Construction Engineering and Management, 136(9), 1028-1036.
  • Ahmadi, H. B., Kusi-Sarpong, S., & Rezaei, J. (2017). Assessing the social sustainability of supply chains using Best Worst Method. Resources, Conservation and Recycling, 126, 99-106.
  • Akyuz, E., & Celik, E. (2018). A quantitative risk analysis by using interval type-2 fuzzy FMEA approach: the case of oil spill. Maritime Policy & Management, 45(8), 979-994.
  • Alava, M. V., Figueroa, S. P. D., Alcivar, H. M. B., & Vázquez, M. L. (2018). Single Valued Neutrosophic Numbers and Analytic Hierarchy Process for Project Selection. Neutrosophic Sets & Systems, 21.
  • Alonso, J. A., & Lamata, M. T. (2006). Consistency in the analytic hierarchy process: a new approach. International journal of uncertainty, fuzziness and knowledge-based systems, 14(04), 445-459.
  • Amigun, B., Petrie, D., & Görgens, J. (2011). Economic risk assessment of advanced process technologies for bioethanol production in South Africa: Monte Carlo analysis. Renewable Energy, 36(11), 3178-3186.
  • Carbone, T. A., & Tippett, D. D. (2004). Project risk management using the project risk FMEA. Engineering Management Journal, 16(4), 28-35.
  • Carlsson, B. (2004). Initial risk analysis of potential failure modes. In Performance and Durability Assessment (pp. 147-157).
  • Chang, K. H., Chang, Y. C., & Tsai, I. T. (2013). Enhancing FMEA assessment by integrating grey relational analysis and the decision making trial and evaluation laboratory approach. Engineering Failure Analysis, 31, 211-224.
  • Fattahi, R., & Khalilzadeh, M. (2018). Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Safety Science, 102, 290-300.
  • Fink, O., Zio, E., Weidmann, U. (2014). Predicting component reliability and level of degradation with complex-valued neural networks. Reliability Engineering & System Safety, 121(1), pp. 198-206.
  • Gamal, A., Ismail, M., & Smarandache, F. A Scientific Decision Framework for Supplier Selection under Neutrosophic Moora Environment. Peer Reviewers, 33.
  • Gul, M. (2018). Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: The case of a gun and rifle barrel external surface oxidation and coloring unit. International journal of occupational safety and ergonomics, (just-accepted), 1-26.
  • Gul, M., & Guneri, A. F. (2016). A fuzzy multi criteria risk assessment based on decision matrix technique: a case study for aluminum industry. Journal of Loss Prevention in the Process Industries, 40, 89-100.
  • Gul, M., Ak, M. F., & Guneri, A. F. (2017). Occupational health and safety risk assessment in hospitals: A case study using two-stage fuzzy multi-criteria approach. Human and Ecological Risk Assessment: An International Journal, 23(2), 187-202.
  • Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121, 23-31.
  • Hui M., E. C., Fai Lau, O. M., & Lo, K. K. (2009). A fuzzy decision‐making approach for portfolio management with direct real estate investment. International Journal of Strategic Property Management, 13(2), 191-204.
  • Jannadi, O. A., & Almishari, S. (2003). Risk assessment in construction. Journal of construction engineering and management, 129(5), 492-500.
  • Jiang, W., Xie, C., Zhuang, M., & Tang, Y. (2017). Failure mode and effects analysis based on a novel fuzzy evidential method. Applied Soft Computing, 57, 672-683.
  • Johnston, G. (2000). Reliability for technology, engineering, and management, by paul kales. Technimetrics . 42(2), pp. 207-207.
  • Kadoić, N., Ređep, N. B., & Divjak, B. (2017, January). Decision Making with the Analytic Network Process. In The 14th International Symposium on Operational Research in Slovenia.
  • Leu, S. S., & Chang, C. M. (2013). Bayesian-network-based safety risk assessment for steel construction projects. Accident Analysis & Prevention, 54, 122-133.
  • Linder, E., Patil, G. P., & Vaughan, D. S. (1987). Application of event tree risk analysis to fisheries management. Ecological Modelling, 36(1-2), 15-28.
  • Lindhe, A., Rosén, L., Norberg, T., & Bergstedt, O. (2009). Fault tree analysis for integrated and probabilistic risk analysis of drinking water systems. Water research, 43(6), 1641-1653.
  • Liu, H. C., Fan, X. J., Li, P., & Chen, Y. Z. (2014). Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment. Engineering Applications of Artificial Intelligence, 34, 168-177.
  • 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.
  • Mentes, A., & Helvacioglu, I. H. (2011). An application of fuzzy fault tree analysis for spread mooring systems. Ocean Engineering, 38(2-3), 285-294.
  • Miao, X., Yu, B., Xi, B., & Tang, Y. H. (2010). Modeling of bilevel games and incentives for sustainable critical infrastructure system. Technological and Economic Development of Economy, 16(3), 365-379.
  • Misra, K. B., & Weber, G. G. (1990). Use of fuzzy set theory for level-I studies in probabilistic risk assessment. Fuzzy Sets and Systems, 37(2), 139-160.
  • Mohammadi, A., & Tavakolan, M. (2013, June). Construction project risk assessment using combined fuzzy and FMEA. In IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013 Joint (pp. 232-237). IEEE.
  • Mou, Q., Xu, Z., & Liao, H. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224-239.
  • Nawaz, F., Asadabadi, M. R., Janjua, N. K., Hussain, O. K., Chang, E., & Saberi, M. (2018). An MCDM method for cloud service selection using a Markov chain and the best-worst method. Knowledge-Based Systems, 159, 120-131.
  • Ouédraogo, A., Groso, A., & Meyer, T. (2011). Risk analysis in research environment–part II: weighting lab criticity index using the analytic hierarchy process. Safety science, 49(6), 785-793.
  • Peeters, J. F. W., Basten, R. J., & Tinga, T. (2018). Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner. Reliability engineering & system safety, 172, 36-44.
  • Pinto, A. (2014). QRAM a qualitative occupational safety risk assessment model for the construction industry that incorporate uncertainties by the use of fuzzy sets. Safety Science, 63, 57-76.
  • Pramanik, S., Dalapati, S., & Roy, T. K. (2018). Neutrosophic multi-attribute group decision making strategy for logistics center location selection. Neutrosophic Operational Research, 3, 13-32.
  • Razaque, A., Bach, C., & Alotaibi, A. (2012). Fostering project scheduling and controlling risk management. arXiv preprint arXiv:1210.2021.
  • Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  • Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130.
  • Rezaei, J., Nispeling, T., Sarkis, J., & Tavasszy, L. (2016). A supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. Journal of Cleaner Production, 135, 577-588.
  • Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42(23), 9152-9164.
  • Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal Operations Research, 48(1), 9-26.
  • Salimi, N., & Rezaei, J. (2016). Measuring efficiency of university-industry Ph. D. projects using best worst method. Scientometrics, 109(3), 1911-1938.
  • Schuhmacher, M., Meneses, M., Xifró, A., & Domingo, J. L. (2001). The use of Monte-Carlo simulation techniques for risk assessment: study of a municipal waste incinerator. Chemosphere, 43(4-7), 787-799.
  • Shojaei, P., Haeri, S. A. S., & Mohammadi, S. (2018). Airports evaluation and ranking model using Taguchi loss function, best-worst method and VIKOR technique. Journal of Air Transport Management, 68, 4-13.
  • Singh M, & Sarkar D. (2017), Project Risk Analysis for Elevated Metro Rail Projects using Fuzzy Failure Mode and Effect Analysis (FMEA), International Journal of Engineering Technology Science and Research,4,(11).
  • Smarandache, F., & Vlăduțescu, Ş. (2013). Communication vs. Information, an Axiomatic Neutrosophic Solution. Infinite Study.
  • Song, W., Ming, X., Wu, Z., & Zhu, B. (2014). A rough TOPSIS approach for failure mode and effects analysis in uncertain environments. Quality and Reliability Engineering International, 30(4), 473-486.
  • Stamatis, D. H. (2003). Failure mode and effect analysis: FMEA from theory to execution. ASQ Quality Press.
  • Su, X., Deng, Y., Mahadevan, S., & Bao, Q. (2012). An improved method for risk evaluation in failure modes and effects analysis of aircraft engine rotor blades. Engineering Failure Analysis, 26, 164-174.
  • Thomas A. Carbone & Donald D. Tippett (2004) Project Risk Management Using the Project Risk FMEA, Engineering Management Journal, 16:4, 28-35.
  • Turskis, Z., Zavadskas, E. K., & Peldschus, F. (2009). Multi-criteria optimization system for decision making in construction design and management. Engineering economics, 61(1).
  • Ustinovichius, L., Barvidas, A., Vishnevskaja, A., & Ashikhmin, I. V. (2009). Multicriteria verbal analysis for the decision of construction problems. Technological and Economic Development of Economy, 15(2), 326-340.
  • Vahdani, B., Salimi, M., & Charkhchian, M. (2015). A new FMEA method by integrating fuzzy belief structure and TOPSIS to improve risk evaluation process. The International Journal of Advanced Manufacturing Technology, 77(1-4), 357-368.
  • Vílchez, J. A., Espejo, V., & Casal, J. (2011). Generic event trees and probabilities for the release of different types of hazardous materials. Journal of Loss Prevention in the Process Industries, 24(3), 281-287.
  • Wang, Y. M., Chin, K. S., Poon, G. K. K., & Yang, J. B. (2009). Risk evaluation in failure mode and effects analysis using fuzzy weighted geometric mean. Expert systems with applications, 36(2), 1195-1207.
  • Zavadskas, E. K., Turskis, Z., & Tamošaitiene, J. (2010). Risk assessment of construction projects. Journal of civil engineering and management, 16(1), 33-46.
  • Zeng, S. X., Tam, C. M., & Tam, V. W. (2015). Integrating safety, environmental and quality risks for project management using a FMEA method. Engineering Economics, 66(1).
  • Zeng, Sai X, Tam, Chun M., Vivian W. Y. Tam, (2010), Integrating Safety, Environmental and Quality Risks for Project Management Using a FMEA Method,Inzinerine Ekonomika-Engineering Economics, 21, (1).
  • Zhang, Z., & Chu, X. (2011). Risk prioritization in failure mode and effects analysis under uncertainty. Expert Systems with Applications, 38(1), 206-214.
  • 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.
  • Zhi-Qiang, Hou; YA-MEI, Zeng. Research on risk assessment technology of the major hazard in harbor engineering. Procedia engineering, 2016, 137: 843-848.
  • Zhou, Q., & Thai, V. V. (2016). Fuzzy and grey theories in failure mode and effect analysis for tanker equipment failure prediction. Safety science, 83, 74-79.
There are 66 citations in total.

Details

Primary Language English
Subjects Operation
Journal Section Articles
Authors

Melih Yücesan 0000-0001-6148-4959

Publication Date January 26, 2020
Submission Date May 23, 2019
Published in Issue Year 2020

Cite

APA Yücesan, M. (2020). FMEA ANALYSIS IN MECHANICAL INSTALLATION PROJECT BASED ON BEST WORST AND NEUTROSOPHIC AHP INTEGRATED MODEL. Elektronik Sosyal Bilimler Dergisi, 19(73), 363-382. https://doi.org/10.17755/esosder.569291

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Elektronik Sosyal Bilimler Dergisi (Electronic Journal of Social Sciences), Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.

ESBD Elektronik Sosyal Bilimler Dergisi (Electronic Journal of Social Sciences), Türk Patent ve Marka Kurumu tarafından tescil edilmiştir. Marka No:2011/119849.