Fine-Kinney-Based Occupational Risk Assessment using Pythagorean Fuzzy AHP-COPRAS for the Lifting Equipment in the Energy Distribution and Investment Sector

Year 2024, , 854 - 873, 01.06.2024

Suleyman Recep Satıcı Süleyman Mete

https://doi.org/10.35378/gujs.1227756

Cited By: 1

Abstract

The initial risk assessment, especially when using a risk score system, is the main step in a risk assessment process that comes after determining the scope of the risks and assessment. Risk assessment frequently employs the Fine-Kinney method, a comprehensive strategy for quantitative assessments that helps keep risks under check. A risk score (RS) is defined using the standard version of Fine-Kinney by mathematically multiplication of probability (P), exposure (E), and consequence (C) parameters. The Fine-Kinney-based risk evaluation approach has the disadvantage of not accounting for the relationships among the risk parameters' interaction and determining the risk precedence of work-related hazards. Hence, to decrease the negative effects of increasing risks, a new hazard evaluation system for occupational health and safety (OHS) is necessary. In this paper, a novel approach is proposed for integrating Fine–Kinney-based occupational hazard evaluation and AHP-COPRAS for the energy distribution and investment sector under the Pythagorean fuzzy environment. Lifting equipment in energy distribution and investment sector case study is used to demonstrate the practicality and efficacy of the suggested integrated approach. To verify the novel method to risk assessment, a comparative study and sensitivity analysis are also provided. As a result, using the benefit of Pythagorean fuzzy sets, which more effectively express uncertainty, the integrated approach yields more logical conclusions for assessing work-related hazards in the energy distribution and investment sector.

Keywords

Risk assessment, Fine-Kinney, Pythagorean fuzzy COPRAS, Energy Distribution, Lifting Equipment

References

• [1] Ceylan, H., “Türkiye’deki Elektrik Üretim, İletim ve Dağıtım Tesislerinde Meydana Gelen İş Kazalarının Analizi”, Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 4 (2): 30–42, (2012).
• [2] Aslan, İ., & Çelik, Y., “Elektrik Dağıtım Sektöründe Çalışanların İş Sağlığı ve Güvenliği İncelemesi: Muş, Bitlis ve Van İlleri Uygulaması”, Çalışma İlişkileri Dergisi, 1: 130–145, (2022).
• [3] Yönetmelik, “Resmî Gazete” 28512. https://www.resmigazete.gov.tr/eskiler/2012/12/20121229-13.htm, (2012, December 29)
• [4] Oz, N. E., Mete, S., Serin, F., & Gul, M., “Risk assessment for clearing and grading process of a natural gas pipeline project: An extended TOPSIS model with Pythagorean fuzzy sets for prioritizing hazards”, Human and Ecological Risk Assessment: An International Journal, 25(6): 1615-1632, (2019).
• [5] 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).
• [6] Gul M., A review of occupational health and safety risk assessment approaches based on multi-criteria decision-making methods and their fuzzy versions, Human Ecol Risk Assess 24(7): 1723–1760, (2018).
• [7] Mete, S., Oz, N. E., Gul, M., Serin, F., & Celik, E., “A Risk Assessment Approach Using Both Stochastic Data and Subjective Judgments”, In Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making: Proceedings of the INFUS 2019 Conference, Istanbul, (2019).
• [8] Yager, R. R., “Pythagorean membership grades in multicriteria decision making”, IEEE Transactions on Fuzzy Systems, 22(4): 958–965, (2014).
• [9] Gul, M., Mete, S., Serin, F., & Celik, E., “Fine–Kinney-Based Occupational Risk Assessment Using Interval-Valued Pythagorean Fuzzy VIKOR”, In Fine–Kinney-Based Fuzzy Multi-criteria Occupational Risk Assessment, 45-68, (2021).
• [10] Liu, P., “Multi‐attribute decision‐making method research based on interval vague set and TOPSIS method”, Technological and economic development of economy, 15(3): 453-463, (2009).
• [11] Kiritsis, D., Bufardi, A. & Xirouchakis, P., “Multi-criteria decision aid for product end of life options selection”, IEEE International Symposium on Electronics and The Environment, 48–53, (2003).
• [12] Yager, R. R., “Properties and applications of Pythagorean fuzzy sets”, In Imprecision and Uncertainty in Information Representation and Processing, 119-136, (2016).
• [13] Ma, Z., & Xu, Z., “Symmetric Pythagorean fuzzy weighted geometric/averaging operators and their application in multicriteria decision‐making problems”, International Journal of Intelligent Systems, 31(12): 1198-1219, (2016).
• [14] Peng, X., & Dai, J., “Approaches to Pythagorean fuzzy stochastic multi‐criteria decision making based on prospect theory and regret theory with new distance measure and score function”, International Journal of Intelligent Systems, 32(11): 1187-1214, (2017).
• [15] Kahraman, C., Onar, S. C., & Oztaysi, B., “Present worth analysis using pythagorean fuzzy sets”, In Advances in Fuzzy Logic and Technology, 2: 336-342, (2017).
• [16] Chen, T. Y., “Remoteness index-based Pythagorean fuzzy VIKOR methods with a generalized distance measure for multiple criteria decision analysis”, Information Fusion, 41: 129-150, (2018).
• [17] Garg, H., “Linguistic Pythagorean fuzzy sets and its applications in multi attribute decision‐making process”, International Journal of Intelligent Systems, 33(6): 1234-1263, (2018).
• [18] Xian, S., Yin, Y., Fu, M., & Yu, F., “A ranking function based on principal‐value Pythagorean fuzzy set in multicriteria decision making”, International Journal of Intelligent Systems, 33(8): 1717-1730, (2018).
• [19] Rahman, K., Abdullah, S., Ali, A., & Amin, F., “Approaches to multi-attribute group decision making based on induced interval-valued Pythagorean fuzzy Einstein hybrid aggregation operators”, Bulletin of the Brazilian Mathematical Society, New Series, 50(4): 845-869, (2019).
• [20] Peng, X., & Selvachandran, G., “Pythagorean fuzzy set: state of the art and future directions”, Artificial Intelligence Review, 52: 1873–1927, (2019).
• [21] Valipour, A., Yahaya, N., Md Noor, N., Antuchevičienė, J., & Tamošaitienė, J., “Hybrid SWARA-COPRAS method for risk assessment in deep foundation excavation project: An Iranian case study”, Journal of Civil Engineering and Management, 23(4): 524–532, (2017).
• [22] Yazdani, M., Alidoosti, A., & Zavadskas, E. K., “Risk Analysis of Critical Infrastructures Using Fuzzy Copras”, Economic Research-Ekonomska Istraživanja, 24(4), 27–40, (2015).
• [23] Popovic, G., Stanujkic, D., & Stojanovic, S., “Investment project selection by applying COPRAS method and imprecise data”, Serbian Journal of Management, 7(2): 257–269, (2012).
• [24] Büyüközkan, G., & Göçer, F., “A novel approach integrating ahp and copras under pythagorean fuzzy sets for digital supply chain partner selection”, IEEE Transactions on Engineering Management, 68(5): 1486–1503, (2021).
• [25] Hezer, S., Gelmez, E., & Özceylan, E., “Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment”, Journal of Infection and Public Health, 14(6): 775–786, (2021).
• [26] Lu, J., Zhang, S., Wu, J., & Wei, Y., “COPRAS method for multiple attribute group decision making under picture fuzzy environment and their application to green supplier selection” Technological and Economic Development of Economy, 27(2): 369-385, (2021).
• [27] Mishra, A. R., Liu, P., & Rani, P., “COPRAS method based on interval-valued hesitant Fermatean fuzzy sets and its application in selecting desalination technology”, Applied Soft Computing, 119: 108570, (2022).
• [28] Hezam, I. M., Mishra, A. R., Rani, P., Saha, A., Smarandache, F., & Pamucar, D., “An integrated decision support framework using single-valued neutrosophic-MASWIP-COPRAS for sustainability assessment of bioenergy production technologies”, Expert Systems with Applications, 211: 118674, (2022).
• [29] 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).
• [30] Rani P., Mishra, A. R., & Mardani, A., “An extended Pythagorean fuzzy complex proportional assessment approach with new entropy and score function: Application in pharmacological therapy selection for type 2 diabetes”, Applied Soft Computing, 94: 106441, (2020).
• [31] Kinney, G. F., & Wiruth, A. D., “Practical Risk Analysis for Safety Management”, Naval Weapons Center China Lake CA, (1976).
• [32] Birgören, B., “Fine Kinney Risk Analizi Yönteminde Risk Analizi Yönteminde Risk Faktörlerinin Hesaplama Zorlukları ve Çözüm Önerileri”, International Journal of Research and Development, 9(1), (2017).
• [33] Yıldız, A., Ayyıldız, E., Gümüş, A. T., & Özkan, C., “Ülkelerin Yaşam Kalitelerine Göre Değerlendirilmesi İçin Hibrit Pisagor Bulanık Ahp-Topsis Metodolojisi: Avrupa Birliği Örneği”, Avrupa Bilim ve Teknoloji Dergisi, 17: 1383–1391, (2019).
• [34] Zhang, X., & Xu, Z., “Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets”, International Journal of Intelligent Systems, 29(12): 1061–1078, (2014).
• [35] Dağdeviren, M., Akay, D., & Kurt, M., “İş Değerlendirme Sürecinde Analitik Hiyerarşi Prosesi ve Uygulaması”, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 19(2): 131–138, (2013).
• [36] Liu, P. D., & Jin, F., “The trapezoid fuzzy linguistic Bonferroni mean operators and their application to multiple attribute decision making”, Scientia Iranica, 19(6): 1947–1959, (2012).
• [37] Ahmad, N., Hasan, M.G., & Barbhuiya, R.K., “Identification and prioritization of strategies to tackle COVID-19 outbreak: A group-BWM based MCDM approach”, Applied Soft Computing, 111: 107642, (2021).
• [38] Zavadskas, E.K., & Kaklauskas, A., “The new method of multicriteria evaluation of projects”, Deutsch-Litauisch-Polnisches Kolloquim zum Baubetriebswesen. Hochschule fur Technik, Wirtschaft und Kultur in Leipzig, 3: 3–8, (1996).
• [39] Pérez-Domínguez, L., Rodríguez-Picón, L. A., Alvarado-Iniesta, A., Luviano Cruz, D., & Xu, Z., “MOORA under Pythagorean Fuzzy Set for Multiple Criteria Decision Making”, Complexity, 2018: 1-10, (2018).
• [40] Sarıçalı, G., & Kundakcı, N., “Ahp ve Copras Yöntemleri ile Otel Alternatiflerinin Değerlendirilmesi”, International Review of Economics and Management, 4(1): 45–66, (2016).