Bulanık FUCOM ve bulanık çizge teorisi-matris yaklaşımı ile iş güvenliği risklerinin önceliklendirilmesi

Yıl 2023, Cilt: 38 Sayı: 1, 57 - 70, 21.06.2022

İlker Gölcük , Esra Duygu Durmaz , Ramazan Şahin

https://doi.org/10.17341/gazimmfd.970514

Cited By: 7

Öz

Bu çalışmada iş güvenliği risklerinin değerlendirilmesi için yeni bir hata türü ve etkileri analizi (HTEA) modeli önerilmiştir. Klasik HTEA’ya göre risk öncelik sayıları (RÖS), olabilirlik, şiddet ve fark edilebilirlik puanlarının çarpılmasıyla hesaplanır. Bununla birlikte, klasik HTEA tarafından oluşturulan RÖS değerleri literatürde ağır eleştirilere konu olmuştur. Bu çalışma klasik HTEA’nın dezavantajlarını aşmak için yeni bir Çok Kriterli Karar Verme (ÇKKV) modeli önermektedir. Önerilen risk değerlendirme modeli, tam tutarlılık yöntemini (FUCOM) ve çizge teorisi-matris yaklaşımını (ÇTMY) bulanık ortamda birleştirmektedir. Bulanık FUCOM yöntemi kullanılarak hata türlerinin risk puanları ve risk faktörlerinin ağırlıkları elde edilmiştir. Öte yandan, her hata modunun RÖS değeri bulanık ÇTMY kullanılarak hesaplanmıştır. Bulanık ÇTMY, risk faktörleri arasındaki tüm olası etkileşimleri göz önünde bulundurmakta ve böylece daha doğru sıralamalar üretmektedir. Karar vericilerin bulanık değerlendirmeleri en küçük kareler mesafe yöntemi kullanılarak toplanmıştır. Önerilen model, gerçek hayattaki bir vaka çalışmasında uygulanmış ve hata modları sıralanmıştır.

Anahtar Kelimeler

Hata türü ve etkileri analizi, iş güvenliği riskleri, bulanık mantık, FUCOM, çizge teorisi-matris yaklaşımı

Kaynakça

• 1. Fattahi R., Khalilzadeh M., Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment, Safety Science, 102, 290-300, 2018.
• 2. Gölcük İ., Interval type-2 fuzzy inference-based failure mode and effect analysis model in a group decision-making setting, Kybernetes, ahead-of-print (ahead-of-print), 2021.
• 3. Peláez C.E., Bowles J.B., Using fuzzy cognitive maps as a system model for failure modes and effects analysis, Information Sciences, 88 (1), 177-199, 1996.
• 4. Liu H.C., Liu L., Lin Q.L., Fuzzy failure mode and effects analysis using fuzzy evidential reasoning and belief rule-based methodology, IEEE Transactions on Reliability, 62 (1), 23-36, 2013.
• 5. Chang C.L., Liu P.H., Wei C.C., Failure mode and effects analysis using grey theory, Integrated Manufacturing Systems, 12 (3), 211-216, 2001.
• 6. Pillay A., Wang J., Modified failure mode and effects analysis using approximate reasoning, Reliability Engineering & System Safety, 79 (1), 69-85, 2003.
• 7. Baykasoğlu A., Gölcük İ., Comprehensive fuzzy FMEA model: a case study of ERP implementation risks, Operational Research, 20, 795–826, 2020.
• 8. Huang J., You J.X., Liu H.C., Song M.S., Failure mode and effect analysis improvement: A systematic literature review and future research agenda, Reliability Engineering and System Safety, 199, 2020.
• 9. Zadeh L.A., Fuzzy sets, Information and Control, 8 (3), 338-353, 1965.
• 10. Tzeng G.-H., Huang J.-J., Multiple attribute decision making: methods and applications: CRC press, 2011.
• 11. Zeng S.X., Tam C.M., Tam V.W.Y., Integrating safety, environmental and quality risks for project management using a FMEA method, Engineering Economics, (1), 44-52, 2010.
• 12. Lux A., Mawo De Bikond J., Etienne A., Quillerou-Grivot E., FMEA and consideration of real work situations for safer design of production systems, International Journal of Occupational Safety and Ergonomics, 22 (4), 557-564, 2016.
• 13. Bao J., Johansson J., Zhang J., An occupational disease assessment of the mining industry's occupational health and safety management system based on FMEA and an improved AHP model, Sustainability (Switzerland), 9 (1), 2017.
• 14. Ozdemir Y., Gul M., Celik E., Assessment of occupational hazards and associated risks in fuzzy environment: A case study of a university chemical laboratory, Human and Ecological Risk Assessment, 23 (4), 895-924, 2017.
• 15. Ilbahar E., Karaşan A., Cebi S., Kahraman C., A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system, Safety Science, 103, 124-136, 2018.
• 16. Karasan A., Ilbahar E., Cebi S., Kahraman C., A new risk assessment approach: Safety and Critical Effect Analysis (SCEA) and its extension with Pythagorean fuzzy sets, Safety Science, 108, 173-187, 2018.
• 17. Mete S., Assessing occupational risks in pipeline construction using FMEA-based AHP-MOORA integrated approach under Pythagorean fuzzy environment, Human and Ecological Risk Assessment, 25 (7), 1645-1660, 2019.
• 18. Dabbagh R., Yousefi S., A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis, Journal of Safety Research, 71, 111-123, 2019.
• 19. Dahooie J.H., Vanaki A.S., Firoozfar H.R., Zavadskas E.K., Čereška A., An extension of the failure mode and effect analysis with hesitant fuzzy sets to assess the occupational hazards in the construction industry, International Journal of Environmental Research and Public Health, 17 (4), 2020.
• 20. Rezaei J., Best-worst multi-criteria decision-making method, Omega, 53, 49-57, 2015.
• 21. Gölcük İ., An interval type-2 fuzzy reasoning model for digital transformation project risk assessment, Expert Systems with Applications, 159, 113579, 2020.
• 22. Baykasoğlu A., Gölcük İ., Revisiting ranking accuracy within WASPAS method, Kybernetes, 49 (3), 885-895, 2020.
• 23. Zavadskas E.K., Turskis Z., Antucheviciene J., Zakarevicius A., Optimization of weighted aggregated sum product assessment, Elektronika ir elektrotechnika, 122 (6), 3-6, 2012.
• 24. Spreafico C., Russo D., Rizzi C., A state-of-the-art review of FMEA/FMECA including patents, Computer Science Review, 25, 19-28, 2017.
• 25. Liu H.C., Zhang L.J., Ping Y.J., Wang L., Failure mode and effects analysis for proactive healthcare risk evaluation: A systematic literature review, Journal of Evaluation in Clinical Practice, 26 (4), 1320-1337, 2020.
• 26. Pamucar D., Ecer F., Prioritizing the weights of the evaluation criteria under fuzziness: The fuzzy full consistency method–FUCOM-F, Facta Universitatis, series: Mechanical Engineering, 18 (3), 419-437, 2020.
• 27. Pamučar D., Stević Ž., Sremac S., A New Model for Determining Weight Coefficients of Criteria in MCDM Models: Full Consistency Method (FUCOM), Symmetry, 10 (9), 393, 2018.
• 28. Ecer F., FUCOM sübjektif ağırlıklandırma yöntemi ile rüzgâr çiftliği yer seçimini etkileyen faktörlerin analizi An analysis of the factors affecting wind farm site selection through FUCOM subjective weighting method, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27 (1), 24-34, 2021.
• 29. Rao R.V., Padmanabhan K.K., Selection, identification and comparison of industrial robots using digraph and matrix methods, Robotics and Computer-Integrated Manufacturing, 22 (4), 373-383, 2006.
• 30. Baykasoğlu A., “Graph Theory” and “Matrix Method” Based Approach for Business Process Modeling/Simulation Software Selection, Journal of the Faculty of Engineering and Architecture of Gazi University, 28 (3), 555-566, 2013.
• 31. Marcus M., Minc H., Permanents, The American Mathematical Monthly, 72 (6), 577-591, 1965.
• 32. Nourani Y., Andresen B., Exploration of NP-hard enumeration problems by simulated annealing — the spectrum values of permanents, Theoretical Computer Science, 215 (1), 51-68, 1999.
• 33. Baykasoğlu A., A review and analysis of “graph theoretical-matrix permanent” approach to decision making with example applications, Artificial intelligence review, 42 (4), 573-605, 2014.
• 34. Ryser H.J., Combinatorial Mathematics: Mathematical Association of America, 1963.
• 35. Wang Y.-M., Parkan C., Two new approaches for assessing the weights of fuzzy opinions in group decision analysis, Information Sciences, 176 (23), 3538-3555, 2006.