KÜRESEL BULANIK KÜMELER İLE GÜVENİLİRLİK ANALİZİ

Abstract

Geri dönüşüm süreci, sürdürülebilir bir çevre için atık yönetiminde önemli bir adımdır. Ancak, geri dönüşüm sürecindeki bazı adımlar insan sağlığı için risk oluşturabilir. Depolamada yetersizlik nedeniyle açık alanda uzun süre kalan oksitler çevreyi tehdit edebilir. Makinelerde oluşan insan veya yazılım kaynaklı hatalar nedeniyle zehirli atıklar çevreye yayılabilir. Bu nedenle, geri dönüşüm tesislerinde oluşabilecek birçok problemi öngörebilmek ve doğru çalışma sürecini tasarlayabilmek için sistem güvenilirliği şarttır. Bu çalışmada, geri dönüşüm tesislerinin sistem güvenilirliği için bir yöntem önerilmiştir. Güvenilirliği ölçmek için alanında uzman kişilerce ölçütler ve sistemin bu ölçütlere uygunluğu belirlenir. Bu bilgilere bağlı olarak güvenilirlik göstergesi hesaplanır. Performans önem göstergesi hesaplanarak kritik durumda olan ve güvenilirliği etkileyen en riskli gruplar tespit edilir. Önerilen yöntemde ölçüt değerlendirmelerinde küresel bulanık sayılardan yararlanılmaktadır. Bulanık ifadelerle oluşturulan performans ve güvenilirlik göstergeleri kullanılarak sistemin güvenilirlik seviyesi belirlenmiştir. Böylece, sistem güvenilirliğini etkileyen öncelikli sorunlar tespit edilmiştir. Uygulama için literatürde var olan bir çalışmadan yararlanılmış, analizin geçerliliğini göstermek için elde edilen sonuçlar karşılaştırılmıştır.

Keywords

• Küresel Bulanık Sayılar
• Geri Dönüşüm Tesisi Güvenilirlik Değerlendirmesi
• Sürdürülebilir Çevre
• Bulanık Mantık Güvenilirlik Analizi

RELIABILITY ANALYSIS WITH SPHERICAL FUZZY SETS

Abstract

The recycling process is an important step in waste management for a sustainable environment. However, some steps in the recycling process can pose a risk to human health. Oxides that remain in the open area for a long time due to insufficient storage may threaten the environment. Toxic wastes can spread to the environment due to human or software errors that occurs in the machines. Therefore, system reliability is essential to predict many problems that may arise and to design the correct working process in recycling facilities. In this study, an alternative method is proposed for the system reliability of recycling facilities. In order to measure reliability, the criteria and compliance of the system with these criteria are determined by the experts. Depending on their information, the reliability index is calculated. By calculating the performance importance index, the most risky groups that are in critical condition and affect reliability are identified. Spherical fuzzy numbers are used in the proposed method to evaluate criteria. The reliability level of the system is determined by using performance and reliability indices created with fuzzy expressions. Thus, main problems affecting system reliability are identified. In application, a study in the literature is used and the results are compared to show the validity of the analysis.

Keywords

• Spherical Fuzzy Set
• Fuzzy Logic Reliability Analysis
• Recycling Facility Reliability Evaluation
• Sustainable Environment

References

1. Akram, M., Alsulami, S., Khan, A., Karaaslan, F., 2020. Multi-Criteria Group Decision-Making Using Spherical Fuzzy Prioritized Weighted Aggregation Operators, International Journal of Computational Intelligence Systems, Vol. 13(1), 1429-1446.
2. Ashraf, S. Abdullah, S., Qiyas, M., Khan, A., 2019. The Application of GRA Method Base on Choquet Integral Using Spherical Fuzzy Information in Decision Making Problems, Journal of New Theory 28 , 84-97.
3. Atanassov, K.T., 1986. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96.
4. Atanassov, K.T., 1989. More on intuitionistic fuzzy sets. Fuzzy Sets Syst. 33(1), 37–45.
5. Buckley, J.J., 1985. Ranking Alternatives using Fuzzy Numbers, Fuzzy Sets and Systems 15(1), 21-31.
6. Chandna, R., Ram, M., 2014. Fuzzy reliability modeling in the system failure rates merit context, International Journal of System Assurance Engineering and Management , 5(3) ,245–251.
7. Chen S.M., Lee, L.W., 2010. Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method,Expert Systems with Applications, 37, 2790-2798.
8. Cheng, C.H.,. Mon, D.L., 1993. Fuzzy system reliability analysis by interval of confidence, Fuzzy Sets and Systems, Vol. 56, No. 1, 29-35, (1993).

9. De-zi, Z., Na, C., 2015. Aeroengine reliability prediction based on fuzzy and interval number, Procedia Engineering, Vol. 99, 1284-1288.
10. Gao, P., Xie, L., 2015. Fuzzy dynamic reliability models of parallel mechanical systems considering strength degradation path dependence and failure dependence”, Mathematical Problems in Engineering, Vol. 2015, 1-9.
11. Garibaldi, J.M., Ozen, T., 2007. Uncertain fuzzy reasoning: a case study in modeling expert decision making IEEE Trans. Fuzzy Syst. 15(1), 16–30.
12. Guesgen, H.W., Albrecht, J., 2000. Imprecise reasoning in geographic information systems, Fuzzy Sets Syst 113:121–131.
13. Gündogdu, F.K., Kahraman, C., 2019. Spherical fuzzy sets and spherical fuzzy TOPSIS method, J. Intell. Fuzzy Syst. 36, 1–16.
14. Gündogdu, F.K., Kahraman, C., 2019. Spherical Fuzzy Analytic Hierarchy Process (AHP) and Its Application to Industrial Robot
15. Selection, In: Kahraman C., Cebi S., Cevik Onar S., Oztaysi B., Tolga A., Sari I. (eds) Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making. INFUS 2019. Advances in Intelligent Systems and Computing, Vol. 1029. Springer- Cham.
16. Gündogdu, F.K., Kahraman, C., 2019. Spherical Fuzzy Sets and Spherical Fuzzy TOPSIS Method, Journal of Intelligent & Fuzzy Systems, vol. 36, no. 1, pp. 337-352.
17. Jiang, Q. , Chen, C.H., 2003. A numerical algorithm of fuzzy reliability, Reliab Eng Syst Saf 80:299–307.
18. Kahraman, C., 2006. Fuzzy applications in industrial engineering, Springer, Vol. 201.
19. Kai-Yuan, C., Chuan-Yuan W., Ming-Lian, Z. 1991. Fuzzy reliability modeling of gracefully degradable computing systems,
20. Reliability Engineering & System Safety, Vol. 33, No. 1, 141-157.
21. Kutlu Gundogdu, F., & Kahraman, C. (2019). Extension of WASPAS with Spherical Fuzzy Sets. Informatica, 30(2), 269-292.
22. Mendel, J.M., John, R.I , Liu, F.L. 2006. Interval type-2 fuzzy logical systems made simple”, IEEE Transactions on Fuzzy Systems, 14, (6), 808-821.
23. Noore, A., Cross, P.L, 2005. Modeling the reliability of large distributed non-homogeneous networks, Inform Proc Lett 93:57–61.
24. Onar, S.C., Oztaysi, B., Kahraman, C., 2014. Strategic decision selection using hesitant fuzzy TOPSIS and interval type-2 fuzzy AHP: a case study,. International Journal of Computational intelligence systems; 5, 1002-1021.
25. Ramli, N, Mohamad, D., 2009. On the Jaccard Index Similarity Measure in Ranking Fuzzy Numbers, MATEMATIKA, Vol.25, Number 2, 157–165.
26. Ross, T.J., 2009. Fuzzy logic with engineering applications, John Wiley & Sons.
27. Smarandache, F., 1999. A Unifying Field in Logics: Neutrosophy, Neutrosophic Probability, Set and Logic. American Research Press, Rehoboth.
28. Şahin, B., Soylu, A., 2020. Intuitionistic fuzzy analytical network process models for maritime supply chain, Applied Soft Computing, Vol. 96, 106614.
29. Sahin, B., Senol, Y., 2015. A Novel Process Model for Marine Accident Analysis by using Generic Fuzzy-AHP Algorithm. Journal of Navigation, 68(1), 162-183.
30. Torra, V., 2010. Hesitant fuzzy sets. Int. J. Intell. Syst. 25(6), 529–539.
31. Tu, J., Cheng, R., Tao, Q., 2015. Reliability analysis method of safety-critical avionics system based on dynamic fault tree under fuzzy uncertainty, Eksploatacja i Niezawodność, Vol. 17, No. 1, 156-163.
32. Tyagi, S.K., 2014. Reliability analysis of a powerloom plant using interval valued intuitionistic fuzzy sets, Applied Mathematics, Vol. 5, No. 13, 2008-2015.
33. Utkin, L., 1994. Knowledge based fuzzy reliability assessment, Microelectronics Reliability, Vol. 34, No. 5, 863-874.
34. Utkin, L.V., 1994. Fuzzy reliability of repairable systems in the possibility context, Microelectronics Reliability, Vol. 34, No. 12, 1865-1876.
35. Yager, R.R., 1986. On the theory of bags. Int. J. Gen. Syst. 13(1), 23–37.
36. Yager, R.R., 2013. Pythagorean fuzzy subsets. In: Proceedings of the Joint IFSA World Congress NAFIPS Annual Meeting, pp. 57–61.
37. Zadeh, L.A., 1965. Fuzzy sets. Inf. Control Vol.8, No.3, 338–353.
38. Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning—I, Information Sciences, Vol. 8, No. 3, 199-249.
39. Zeng, S., Munir, M., Mahmood, T., Naeem, M., 2020. Some T-Spherical Fuzzy Einstein Interactive Aggregation Operators and Their Application to Selection of Photovoltaic Cells, Mathematical Problems in Engineering, https://doi.org/10.1155/2020/1904362.