ÜRETİMDE BAKIM STRATEJİSİ SEÇİMİ İÇİN SEZGİSEL BULANIK TOPSIS YAKLAŞIMI

Abstract

Otomotiv endüstrisinde verimlilik, güvenlik ve maliyet optimizasyonunu sağlamak için bir bakım stratejisinin seçimi çok önemlidir. Ancak, çeşitli çelişkili kriterler ve uzman kararlarında bulunan belirsizlikler nedeniyle karar alma süreci karmaşık olabilir. Bu çalışma, bu zorlukları ele almak için İdeal Çözüme Benzerliğe Göre Sıra Tercihi için Sezgisel Bulanık Tekniği'ni (IF-TOPSIS) kullanan yapılandırılmış bir karar alma çerçevesi sunmaktadır. Bu yöntem, altı temel kritere (güvenlik, maliyet, operasyonel faktörler, güvenilirlik, risk ve katma değer) dayalı olarak dört bakım alternatifinin (Reaktif, İlkel, Önleyici ve Tahmini Bakım) kapsamlı bir şekilde değerlendirilmesini sağlar. Uzman değerlendirmeleri, üyelik, üye olmama ve tereddüt derecelerini yakalamak için sezgisel bulanık kümeler kullanılarak modellenmiştir. Her alternatifin ideal çözüme göreli yakınlığını değerlendirmek için IF-TOPSIS algoritması kullanılmıştır. Bulgular, performans, risk ve maliyeti dengeleme yeteneğini göstererek en etkili bakım stratejisini doğrulamıştır. Ayrıca, bir duyarlılık analizi, modelin çeşitli kısıt koşulları altında kararlılığını doğrulamıştır. IF-TOPSIS çerçevesi, belirsizlik ve öznelliğin hakim olduğu endüstriyel ortamlarda bakım planlaması ve strateji formülasyonu için güvenilir ve kapsamlı bir karar destek aracı olarak onaylanmıştır. Bu araştırmanın, bakım stratejilerinin seçilmesinde sezgisel bulanık çok kriterli karar verme (ÇKKV) yaklaşımlarının pratik etkinliğini göstererek literatüre ve endüstriye katkı sağlaması beklenmektedir.

Keywords

• IF-TOPSIS
• Bakım Stratejisi
• ÇKKV
• Üretim Maliyetleri
• Duyarlılık Analizi

INTUITIONISTIC FUZZY TOPSIS APPROACH FOR MAINTENANCE STRATEGY SELECTION IN PRODUCTION

Abstract

The selection of a maintenance strategy is crucial for ensuring efficiency, safety, and cost optimization in the automotive industry. However, the decision-making process can be complex due to various conflicting criteria and the uncertainties inherent in expert judgments. This study presents a structured decision-making framework that utilizes the Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IF-TOPSIS) to address these challenges. This method allows for a thorough evaluation of four maintenance alternatives—Reactive, Primitive, Preventive, and Predictive Maintenance—based on six key criteria: safety, cost, operational factors, reliability, risk, and added value. Expert assessments were modeled using intuitionistic fuzzy sets to capture the degrees of membership, non-membership, and hesitation. The IF-TOPSIS algorithm was employed to evaluate the relative closeness of each alternative to the ideal solution. The findings confirmed the most effective maintenance strategy by demonstrating its ability to balance performance, risk, and cost. Additionally, a sensitivity analysis verified the model’s stability under various constraint conditions. The IF-TOPSIS framework has been confirmed as a reliable and comprehensive decision support tool for maintenance planning and strategy formulation in industrial settings characterized by uncertainty and subjectivity. This research is expected to enhance the literature and the industry by demonstrating the practical effectiveness of intuitive fuzzy multi-criteria decision-making (MCDM) approaches in selecting maintenance strategies.

Keywords

• IF-TOPSIS
• Maintenance Strategy
• MCDM
• Production Costs
• Sensitivity Analysis

References

1. Abdulgader, F.S., Eid, R. & Rouyendegh, B.D. (2018). Development of decision support model for selecting a maintenance plan using a fuzzy MCDM approach: A theoretical framework. Applied Computational Intelligence and Soft Computing, 1-14.
2. Avakh Darestani, S., Palizban, T. & Imannezhad, R. (2022). Maintenance strategy selection: A combined goal programming approach and BWM-TOPSIS for paper production industry. Journal of Quality in Maintenance Engineering, 28 (1), 14-36.
3. Basri, E. I., Abdul Razak, I. H., Ab-Samat, H., & Kamaruddin, S. (2017). Preventive maintenance (PM) planning: a review. Journal of quality in maintenance engineering, 23(2), 114-143.
4. Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with applications, 39(17), 13051-13069.
5. Bevilacqua, M. & Braglia, M. (2000). Analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering and System Safety. 70(1), 71-83.
6. Chan, F.T.S. & Prakash, A. (2012). Maintenance policy selection in manufacturing firms using the fuzzy MCDM approach. International Journal of Production Research, 50(23), 7044-7056.
7. Chopra, A., Sachdeva, A. & Bhardwaj, A. (2020). Selection of appropriate maintenance strategy using fuzzy VIKOR technique: application in paper industry. International Journal of Quality and Reliability Management, 39(5), 1226-1248.
8. Çolak, M., & Kaya, İ. (2017). Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey. Renewable and Sustainable Energy Reviews, 80, 840-853.

9. Emovon, I., Norman, R. A., & Murphy, A. J. (2018). Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. Journal of Intelligent Manufacturing, 29, 519-531.
10. Fouladgar, M.M., Yazdani-Chamzini, A., Lashgari, A., Zavadskas, E.K. & Turskis, Z. (2012). Prieziuros strategijos parinkimas taikant AHP ir COPRAS metodus neapibr_ eztose situacijose. International Journal of Strategic Property Management, 16(1), 85-104.
11. Gopal, N. & Panchal, D. (2023). Risk analysis of cheese packaging machine using FMEA and FCODAS approach. Industrial Reliability and Safety Engineering: Applications and Practices. 91, 91-104.
12. Hwang, C.-L. & Yoon, K. (1981). Multiple attributes decision making methods and applications. Multiple Attribute Decision Making, 58-191. Khan, F.I. & Haddara, M.R. (2004). Risk-based maintenance of ethylene oxide production facilities. Journal of Hazardous Materials. 108(3), 147-159.
13. Kushwaha, D. K., Panchal, D., & Sachdeva, A. (2023). An integrated framework based on intuitionistic fuzzy FMEA, COPRAS and TOPSIS for risk assessment in process industry. International Journal of Industrial and Systems Engineering, 45(2), 214-243.
14. Mishra RC, Pathak K. (2002). Maintenance engineering and management. Prentice Hall of India Private Limited, New Delhi.
15. Otay, I., Onar, S. Ç., Öztayşi, B., & Kahraman, C. (2024). Evaluation of sustainable energy systems in smart cities using a Multi-Expert Pythagorean fuzzy BWM & TOPSIS methodology. Expert Systems with Applications, 250, 123874.
16. Özcan EC. & Küçükyarar U. (2016). Assessment of potential southern gas corridor projects with a combined methodology. Proceedings of the 23rd world energy congress, 105–121.
17. Panchal, D., & Kushwaha, D. K. (2025). Intuitionistic fuzzy approaches-based structured framework for optimal maintenance policy decision in sugar mill. Journal of Quality in Maintenance Engineering, 31(1), 196-222.
18. Patil, A., Soni, G., Prakash, A., & Karwasra, K. (2022). Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches. International Journal of Quality & Reliability Management, 39(3), 675-703.
19. Pophaley, M., & Vyas, R. K. (2010). Choice criteria for maintenance strategy in automotive industries. International Journal of Management Science and Engineering Management, 5(6), 446-452.
20. Ravindran, A., & Sadagopan, S. (1987). Decision making under conflicting criteria—A case study. IEEE transactions on engineering management, (3), 172-177.
21. Sabaei, D., Erkoyuncu, J., & Roy, R. (2015). A review of multi-criteria decision-making methods for enhanced maintenance delivery. Procedia CIRP, 37, 30-35.
22. Selim, H., Yunusoglu, M.G. & Yilmaz Balaman, S¸ . (2016). A dynamic maintenance planning framework based on fuzzy TOPSIS and FMEA: application in an international food company. Quality and Reliability Engineering International. 32(3), 795-804.
23. Shyjith, K., Ilangkumaran, M. & Kumanan, S. (2008). Multi-criteria decision-making approach to evaluate optimum maintenance strategy in textile industry. Journal of Quality in Maintenance Engineering. 14(4), 375-386.
24. Ustinovichius, L., Zavadskas, E. K., & Podvezko, V. (2007). Application of a quantitative multiple criteria decision making (MCDM-1) approach to the analysis of investments in construction. Control and Cybernetics, 36(1), 251-268.
25. Xu, Z.H., (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 15 (6), 1179-1187.