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Application of A Risk Analysis with Fuzzy Multi-Criteria Decision Making Methods

Yıl 2022, Cilt: 34 Sayı: 3, 403 - 414, 30.09.2022
https://doi.org/10.7240/jeps.1125094

Öz

Occupational Health and Safety (OHS) activities, are aimed that the employees in the workplaces are in a complete mental and physical well-being. One of these activities is the risk analysis to be applied in the workplaces. The hazards existing in the workplaces and the potential risks that may be caused by these hazards are determined through risk analysis, and necessary precautions are taken to prevent or minimize occupational accidents. There are various risk analysis methods used in the literature. Fine-Kinney method, which has a widespread usage from the smallest to the largest workplace in our country, is one of these risk analysis methods. In this study, it is aimed to develop a more useful and sensitive hybrid risk analysis method by integrating the traditional Fine-Kinney method with fuzzy logic-based multi-criteria decision making (MCDM) methods. For this purpose, the relevant MCDM methods were used as fuzzy logic-based, so a more meaningful and useful method was developed by using linguistic terms that are more suitable for decision makers. In this context, probability, exposure and severity criteria of the Fine-Kinney method were preferred as criteria for this study. These criteria were weighted by the occupational safety experts with the f-SWARA MCDM method. As a result of the weighting process, 0.198, 0.276 and 0.526 values were obtained for probability, exposure and severity criteria, respectively. Then, by integrating the weighted criteria values into the f-VIKOR method, analyzes were performed and the priority order of the hazards was determined. At the end of the study, the results of the analysis made with the traditional method and the results of the analysis of the hybrid f-SWARA&f-VIKOR method proposed within the scope of the study were compared. In this study, it has been obtained that the weighting of the criteria used in risk analysis is a significant situation, and the use of fuzzy logic-based MCDM methods in risk analysis has a major contribution in minimizing human-induced errors.

Kaynakça

  • [1] Kokangül A, Polat U, ve Dağsuyu C. 2017. A new approximation for risk assessment using the AHP and Fine Kinney methodologies. Safety Science. 91:24–32. doi:10.1016/j,ssci,2016.07.015
  • [2] Hacıbektaşoğlu SE, 2018. İnşaat sektöründe yaşanan iş kazalarının analizi ve bu kazalara neden olan etkenlerin incelenmesi. Stratejik ve Sosyal Araştırmalar Dergisi. 2(3):159-177. doi: 10,30692/sisad,452112
  • [3] Yağımlı M ve Hacıbektaşoğlu SE. 2018. Türkiye’de inşaat sektöründe yaşanan iş kazaları ve ölümlü iş kazası sayılarının tahmini. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi. 9(22):142-156.
  • [4] Gul M ve Celik E. 2018. Fuzzy rule-based Fine–Kinney risk assessment approach for rail transportation systems. Human and Ecological Risk Assessment. 24(7):1786-1812. doi:10,1080/10807039,2017,1422975
  • [5] Hacibektasoglu SE, Mertoglu B ve Tozan H. 2021. Application of a novel hybrid f-SC risk analysis method in the paint industry. Sustainability. 13(24), 13605. https://doi.org/10.3390/su132413605
  • [6] Ahmed MT, Omotunde H. 2012. Theories and strategies of good decision making. International Journal of Scientific & Technology Research. 1:51–54.
  • [7] Sirakaya E, Woodside AG. 2005. Building and testing theories of decision making by travellers. Tourism management. 26:815–832.
  • [8] Gigerenzer G, Gaissmaier W. 2015. Decision making: Nonrational theories. In: International encyclopedia of the social & behavioral sciences. Elsevier. 911–916. https://doi.org/10.1016/B978-0-08-097086-8.26017-0
  • [9] Kou G, Peng Y, Wang G. 2014. Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences. 275:1–12. https://doi.org/10.1016/j.ins.2014.02.137
  • [10] Kinney GF ve Wiruth AD. 1976. Practical risk analysis for safety management [Final Report].
  • [11] Oturakçı M ve Dağsuyu C. 2017. Fuzzy Fine‐Kinney approach in risk assessment and an application. Karaelmas Journal of Occupational Health and Safety. 1(1):17-25.
  • [12] Aker A ve Özçelik TÖ. 2020. Risk assessment with 5x5 Matrix and Fine-Kinney method in metal industry. Karaelmas Journal of Occupational Health and Safety. 4(1):65-75.
  • [13] Keršuliene V, Zavadskas EK ve Turskis Z. 2010. Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management. 11(2): 243-258.
  • [14] Keršuliene V ve Turskis Z. 2011. Integrated fuzzy multiple criteria decision making model for architect selection. Technological and Economic Development of Economy. 17(4): 645–666.
  • [15] Mishra AR, Rani P, Pandey K, Mardani A, Streimikis J, Streimikiene D ve Alrasheedi M. 2020. Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process. Sustainability. 12:4155. doi:10,3390/su12104155
  • [16] Yucenur GN, Caylak S, Gönül G ve Postalcioglu M. 2020. An integrated solution with SWARA&COPRAS methods in renewable energy production: City selection for biogas facility. Renewable Energy. 145:2587-2597. doi:10,1016/j,renene,2019,08,011
  • [17] Zavadskas EK, Hasan Aghdaie M, Hashemkhani Zolfani S. 2013. Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods. Engineering Economics. 24:5–17. https://doi.org/10.5755/j01.ee.24.1.2822
  • [18] Hashemkhani Zolfani S, Yazdani M, Zavadskas EK. 2018. An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Computing. 22:7399–7405. https://doi.org/10.1007/s00500-018-3092-2
  • [19] Kouchaksaraei RH, Zolfani SH, Golabchi M. 2015. Glasshouse locating based on SWARA-COPRAS approach. International Journal of Strategic Property Management. 19:111–122. https://doi.org/10.3846/1648715X.2015.1004565
  • [20] Alimardani M, Hashemkhani Zolfani S, Aghdaie MH, Tamošaitienė J (2013) A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technological and economic development of economy 19:533–548
  • [21] Opricovic S, Tzeng G-H. 2004. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research. 156:445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
  • [22] Gul M, Ak MF, Guneri AF. 2019. Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry. Journal of Safety Research. 69:135–153
  • [23] Opricovic S ve Tzeng GH. 2004. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research. 156(2):445-455.
  • [24] Chen LY ve Wang T. 2009. Optimizing Partners choice in IS/IT outsourcing process: The strategic decision of fuzzy VIKOR. International Journal of Produciton Economics. 120:233-242.
  • [25] Opricovic S. 2011. Fuzzy VIKOR with an application to water resources planning. Expert Syst, Appl. 38(10):12983–12990.
  • [26] Tzeng GH ve Huang JJ. 2011. Multiple Attribute Decision Making: Methods and Applications. Taylor and Francis Group. New York, NY.
  • [27] Kim Y ve Chung ES. 2013. Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea. Applied Mathematical Modelling. 37, 9419–9430.
  • [28] Wei J ve Lin X. 2008. The Multiple Attribute Decision-Making VIKOR Method and Its Application, In Wireless Communications. WiCOM’08, 4th International Conference. Networking and Mobile Computing. 1-4.
  • [29] Gul M, Guven B ve Guneri AF. 2018. A new Fine-Kinney-based risk assessment framework using FAHP-FVIKOR incorporation. Journal of Loss Prevention in the Process Industries. 53:3-16. doi:10.1016/j.jlp.2017.08.014

Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması

Yıl 2022, Cilt: 34 Sayı: 3, 403 - 414, 30.09.2022
https://doi.org/10.7240/jeps.1125094

Öz

İş Sağlığı ve Güvenliği (İSG) faaliyetleriyle işyerlerinde çalışanların ruhen ve bedenen tam bir iyilik halinde olması hedeflenmektedir. Bu faaliyetlerin en önemlilerinden birisi işyerlerinde uygulanacak olan risk analizleridir. Risk analizleriyle işyerlerinde mevcut olan tehlikeler ve bu tehlikelerin neden olabileceği potansiyel riskler belirlenerek iş kazalarının yaşanmaması ya da minimize edilmesi için gerekli önlemler alınmaktadır. Literatürde kullanılmakta olan çeşitli risk analiz yöntemleri bulunmaktadır. Ülkemizde en küçük işyerinden en büyük işyerine kadar yaygın bir kullanım düzeyine sahip olan Fine-Kinney yöntemi bu risk analiz yöntemlerinden birisidir. Bu çalışmada geleneksel Fine-Kinney yönteminin bulanık mantık tabanlı çok kriterli karar verme (ÇKKV) yöntemlerine entegrasyonu ile daha kullanışlı ve hassas hibrit bir risk analizi yöntemi geliştirilmesi hedeflenmiştir. Bu amaç için ilgili ÇKKV yöntemleri bulanık mantık tabanlı olarak kullanılmış olup bu sayede karar verici uzmanlar için daha uygun olan sözel terimlerin kullanımıyla daha anlamlı ve kullanışlı bir yöntem geliştirilmiştir. Bu kapsamda öncelikle çalışma için kriter olarak Fine-Kinney yönteminin olasılık, frekans ve şiddet kriterleri tercih edilmiştir. Bu kriterler iş güvenliği uzmanları tarafından f-SWARA ÇKKV yöntemi ile ağırlıklandırılmıştır. Yapılan ağırlıklandırma işlemi sonucunda olasılık, frekans ve şiddet kriterleri için sırasıyla 0,198, 0,276 ve 0,526 değerleri elde edilmiştir. Daha sonra ağırlıklı kriter değerlerinin f-VIKOR yöntemine entegrasyonu ile analizler gerçekleştirilerek tehlikelerin öncelik sıraları belirlenmiştir. Çalışma sonunda geleneksel yöntemle yapılan analiz sonuçlarıyla önerilen hibrit f-SWARA&f-VIKOR yöntemi analizi sonuçları karşılaştırılmıştır. Bu çalışmayla risk analizlerinde kullanılan kriterlerin ağırlıklandırılmasının önemli bir durum olduğu ayrıca risk analizlerinde bulanık mantık tabanlı ÇKKV yöntemlerinin kullanımının insan kaynaklı hataları minimize etmede büyük katkısı olduğu tespit edilmiştir.

Kaynakça

  • [1] Kokangül A, Polat U, ve Dağsuyu C. 2017. A new approximation for risk assessment using the AHP and Fine Kinney methodologies. Safety Science. 91:24–32. doi:10.1016/j,ssci,2016.07.015
  • [2] Hacıbektaşoğlu SE, 2018. İnşaat sektöründe yaşanan iş kazalarının analizi ve bu kazalara neden olan etkenlerin incelenmesi. Stratejik ve Sosyal Araştırmalar Dergisi. 2(3):159-177. doi: 10,30692/sisad,452112
  • [3] Yağımlı M ve Hacıbektaşoğlu SE. 2018. Türkiye’de inşaat sektöründe yaşanan iş kazaları ve ölümlü iş kazası sayılarının tahmini. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi. 9(22):142-156.
  • [4] Gul M ve Celik E. 2018. Fuzzy rule-based Fine–Kinney risk assessment approach for rail transportation systems. Human and Ecological Risk Assessment. 24(7):1786-1812. doi:10,1080/10807039,2017,1422975
  • [5] Hacibektasoglu SE, Mertoglu B ve Tozan H. 2021. Application of a novel hybrid f-SC risk analysis method in the paint industry. Sustainability. 13(24), 13605. https://doi.org/10.3390/su132413605
  • [6] Ahmed MT, Omotunde H. 2012. Theories and strategies of good decision making. International Journal of Scientific & Technology Research. 1:51–54.
  • [7] Sirakaya E, Woodside AG. 2005. Building and testing theories of decision making by travellers. Tourism management. 26:815–832.
  • [8] Gigerenzer G, Gaissmaier W. 2015. Decision making: Nonrational theories. In: International encyclopedia of the social & behavioral sciences. Elsevier. 911–916. https://doi.org/10.1016/B978-0-08-097086-8.26017-0
  • [9] Kou G, Peng Y, Wang G. 2014. Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences. 275:1–12. https://doi.org/10.1016/j.ins.2014.02.137
  • [10] Kinney GF ve Wiruth AD. 1976. Practical risk analysis for safety management [Final Report].
  • [11] Oturakçı M ve Dağsuyu C. 2017. Fuzzy Fine‐Kinney approach in risk assessment and an application. Karaelmas Journal of Occupational Health and Safety. 1(1):17-25.
  • [12] Aker A ve Özçelik TÖ. 2020. Risk assessment with 5x5 Matrix and Fine-Kinney method in metal industry. Karaelmas Journal of Occupational Health and Safety. 4(1):65-75.
  • [13] Keršuliene V, Zavadskas EK ve Turskis Z. 2010. Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management. 11(2): 243-258.
  • [14] Keršuliene V ve Turskis Z. 2011. Integrated fuzzy multiple criteria decision making model for architect selection. Technological and Economic Development of Economy. 17(4): 645–666.
  • [15] Mishra AR, Rani P, Pandey K, Mardani A, Streimikis J, Streimikiene D ve Alrasheedi M. 2020. Novel multi-criteria intuitionistic fuzzy SWARA–COPRAS approach for sustainability evaluation of the bioenergy production process. Sustainability. 12:4155. doi:10,3390/su12104155
  • [16] Yucenur GN, Caylak S, Gönül G ve Postalcioglu M. 2020. An integrated solution with SWARA&COPRAS methods in renewable energy production: City selection for biogas facility. Renewable Energy. 145:2587-2597. doi:10,1016/j,renene,2019,08,011
  • [17] Zavadskas EK, Hasan Aghdaie M, Hashemkhani Zolfani S. 2013. Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods. Engineering Economics. 24:5–17. https://doi.org/10.5755/j01.ee.24.1.2822
  • [18] Hashemkhani Zolfani S, Yazdani M, Zavadskas EK. 2018. An extended stepwise weight assessment ratio analysis (SWARA) method for improving criteria prioritization process. Soft Computing. 22:7399–7405. https://doi.org/10.1007/s00500-018-3092-2
  • [19] Kouchaksaraei RH, Zolfani SH, Golabchi M. 2015. Glasshouse locating based on SWARA-COPRAS approach. International Journal of Strategic Property Management. 19:111–122. https://doi.org/10.3846/1648715X.2015.1004565
  • [20] Alimardani M, Hashemkhani Zolfani S, Aghdaie MH, Tamošaitienė J (2013) A novel hybrid SWARA and VIKOR methodology for supplier selection in an agile environment. Technological and economic development of economy 19:533–548
  • [21] Opricovic S, Tzeng G-H. 2004. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research. 156:445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
  • [22] Gul M, Ak MF, Guneri AF. 2019. Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry. Journal of Safety Research. 69:135–153
  • [23] Opricovic S ve Tzeng GH. 2004. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research. 156(2):445-455.
  • [24] Chen LY ve Wang T. 2009. Optimizing Partners choice in IS/IT outsourcing process: The strategic decision of fuzzy VIKOR. International Journal of Produciton Economics. 120:233-242.
  • [25] Opricovic S. 2011. Fuzzy VIKOR with an application to water resources planning. Expert Syst, Appl. 38(10):12983–12990.
  • [26] Tzeng GH ve Huang JJ. 2011. Multiple Attribute Decision Making: Methods and Applications. Taylor and Francis Group. New York, NY.
  • [27] Kim Y ve Chung ES. 2013. Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea. Applied Mathematical Modelling. 37, 9419–9430.
  • [28] Wei J ve Lin X. 2008. The Multiple Attribute Decision-Making VIKOR Method and Its Application, In Wireless Communications. WiCOM’08, 4th International Conference. Networking and Mobile Computing. 1-4.
  • [29] Gul M, Guven B ve Guneri AF. 2018. A new Fine-Kinney-based risk assessment framework using FAHP-FVIKOR incorporation. Journal of Loss Prevention in the Process Industries. 53:3-16. doi:10.1016/j.jlp.2017.08.014
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Süleyman Enes Hacıbektaşoğlu 0000-0002-8997-8480

Bülent Mertoğlu 0000-0001-6827-3791

Hakan Tozan 0000-0002-0479-6937

Erken Görünüm Tarihi 30 Eylül 2022
Yayımlanma Tarihi 30 Eylül 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 34 Sayı: 3

Kaynak Göster

APA Hacıbektaşoğlu, S. E., Mertoğlu, B., & Tozan, H. (2022). Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması. International Journal of Advances in Engineering and Pure Sciences, 34(3), 403-414. https://doi.org/10.7240/jeps.1125094
AMA Hacıbektaşoğlu SE, Mertoğlu B, Tozan H. Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması. JEPS. Eylül 2022;34(3):403-414. doi:10.7240/jeps.1125094
Chicago Hacıbektaşoğlu, Süleyman Enes, Bülent Mertoğlu, ve Hakan Tozan. “Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması”. International Journal of Advances in Engineering and Pure Sciences 34, sy. 3 (Eylül 2022): 403-14. https://doi.org/10.7240/jeps.1125094.
EndNote Hacıbektaşoğlu SE, Mertoğlu B, Tozan H (01 Eylül 2022) Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması. International Journal of Advances in Engineering and Pure Sciences 34 3 403–414.
IEEE S. E. Hacıbektaşoğlu, B. Mertoğlu, ve H. Tozan, “Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması”, JEPS, c. 34, sy. 3, ss. 403–414, 2022, doi: 10.7240/jeps.1125094.
ISNAD Hacıbektaşoğlu, Süleyman Enes vd. “Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması”. International Journal of Advances in Engineering and Pure Sciences 34/3 (Eylül 2022), 403-414. https://doi.org/10.7240/jeps.1125094.
JAMA Hacıbektaşoğlu SE, Mertoğlu B, Tozan H. Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması. JEPS. 2022;34:403–414.
MLA Hacıbektaşoğlu, Süleyman Enes vd. “Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması”. International Journal of Advances in Engineering and Pure Sciences, c. 34, sy. 3, 2022, ss. 403-14, doi:10.7240/jeps.1125094.
Vancouver Hacıbektaşoğlu SE, Mertoğlu B, Tozan H. Bulanık Çok Kriterli Karar Verme Yöntemleriyle Bir Risk Analizi Uygulaması. JEPS. 2022;34(3):403-14.