A Fuzzy Decision Support System for Risk Prioritization in Fine Kinney-based Occupational Risk Analysis
DOI:
https://doi.org/10.31181/jscda31202545Keywords:
Occupational risk, HEA, Spherical fuzzy set, MABAC method, Ordered Weighted Averaging operatorAbstract
Analyzing Occupational Health and Safety (OHS) risks requires prioritizing risk, a vital step in hazard and effect analysis (HEA). Currently, there is no existing approach to determine the priority of risk in HEA-based occupational risk analysis that incorporates interactive risk factors and spherical fuzzy risk information. This paper presents a novel approach to address the constraints by introducing a fresh framework for evaluating job-related hazards using HEA by combining the spherical fuzzy set (SFS), the multi-attributive border approximation area comparison (MABAC) technique, and the Ordered Weighted Averaging (OWA) operator, a more sophisticated approach is achieved within this framework. The SFS is utilized in this framework to depict the more ambiguous and unsure data given by specialists, offering a more efficient approach to handling the fuzzy risk information, encompassing the non-membership degree and hesitation. In addition, an advanced MABAC method is used to prioritize occupational risk. Afterward, we provide a practical instance of utilizing the MABAC technical-oriented risk prioritization approach for evaluating potential occupational dangers in risk analysis.
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Dabbagh, R., & Yousefi, S. (2019). A hybrid decision-making approach based on FCM and MOORA for occupational health and safety risk analysis. Journal of Safety Research, 71, 111-123. https://doi.org/10.1016/j.jsr.2019.09.021
Badida, P., Balasubramaniam, Y., & Jayaprakash, J. (2019). Risk evaluation of oil and natural gas pipelines due to natural hazards using fuzzy fault tree analysis. Journal of Natural Gas Science and Engineering, 66, 284-292. https://doi.org/10.1016/j.jngse.2019.04.010
Mete, S. (2019). Assessing occupational risks in pipeline construction using FMEA-based AHP-MOORA integrated approach under Pythagorean fuzzy environment. Human and Ecological Risk Assessment: An International Journal, 25(7), 1645-1660. https://doi.org/10.1080/10807039.2018.1546115
Ilbahar, E., Karaşan, A., Cebi, S., & Kahraman, C. (2018). A novel approach to risk assessment for occupational health and safety using Pythagorean fuzzy AHP & fuzzy inference system. Safety Science, 103, 124-136. https://doi.org/10.1016/j.ssci.2017.10.025
Wang, W., Liu, X., & Qin, Y. (2018). A fuzzy Fine-Kinney-based risk evaluation approach with extended MULTIMOORA method based on Choquet integral. Computers & Industrial Engineering, 125, 111-123. https://doi.org/10.1016/j.cie.2018.08.019
Karasan, A., Ilbahar, E., Cebi, S., & Kahraman, C. (2018). A new risk assessment approach: Safety and Critical Effect Analysis (SCEA) and its extension with Pythagorean fuzzy sets. Safety Science, 108, 173-187. https://doi.org/10.1016/j.ssci.2018.04.031
Gul, M., Yucesan, M., & Ak, M. F. (2022). Control measure prioritization in Fine − Kinney-based risk assessment: a Bayesian BWM-Fuzzy VIKOR combined approach in an oil station. Environmental Science and Pollution Research, 29(39),59385-59402. https://doi.org/10.1007/s11356-022-19454-x
Li, H., Liu, S., & Wang, W. (2022). The probabilistic linguistic term sets based ORESTE method for risk evaluation in Fine-Kinney model with interactive risk factors. Journal of Intelligent & Fuzzy Systems, 43(3), 3493-3512. https://doi.org/10.3233/JIFS-213326
Satici, S. R., & Süleyman, M. (2023). Fine-Kinney-based occupational risk assessment using Pythagorean fuzzy AHP-COPRAS for the lifting equipment in the energy distribution and investment sector. Gazi University Journal of Science, 1-1. https://doi.org/10.35378/gujs.1227756
Ünver, M., Olgun, M., & Türkarslan, E. (2023). Cosine and cotangent similarity measures based on Choquet integral for spherical fuzzy sets and applications to pattern recognition. Journal of Computational and Cognitive Engineering, 1(1), 21-31. https://doi.org/10.47852/bonviewJCCE2022010105
Zhang, H., & Wei, G. (2023). Location selection of electric vehicles charging stations by using the spherical fuzzy CPT–CoCoSo and D-CRITIC method. Computational and Applied Mathematics, 42, 60. https://doi.org/10.1007/s40314-022-02183-9
Liu, P., & Zhang, P. (2021). A normal wiggly hesitant fuzzy MABAC method based on CCSD and prospect theory for multiple attribute decision making. International Journal of Intelligent Systems, 36(1), 447-477. https://doi.org/10.1002/int.22306
Chen, Y., Ran, Y., Wang, Z., Li, X., Yang, X., & Zhang, G. (2020). An extended MULTIMOORA method based on OWGA operator and Choquet integral for risk prioritization identification of failure modes. Engineering Applications of Artificial Intelligence, 91, 103605. https://doi.org/10.1016/j.engappai.2020.103605
Fattahi, R., & Khalilzadeh, M. (2018). Risk evaluation using a novel hybrid method based on FMEA, extended MULTIMOORA, and AHP methods under fuzzy environment. Safety Science, 102, 290-300. https://doi.org/10.1016/j.ssci.2017.10.018
Wang, W., Liu, X., Qin, J., & Liu, S. (2019). An extended generalized TODIM for risk evaluation and prioritization of failure modes considering risk indicators interaction. IISE Transactions, 51(11), 1236-1250. https://doi.org/10.1080/24725854.2018.1539889
Jin, C., Ran, Y., & Zhang, G. (2021). Interval-valued q-rung orthopair fuzzy FMEA application to improve risk evaluation process of tool changing manipulator. Applied Soft Computing, 104, 107192. https://doi.org/10.1016/j.asoc.2021.107192
Nedeljković, M., Puška, A., Doljanica, S., Virijević Jovanović, S., Brzaković, P., Stević, Ž., & Marinkovic, D. (2021). Evaluation of rapeseed varieties using novel integrated fuzzy PIPRECIA–Fuzzy MABAC model. PLOS ONE, 16(2), e0246857. https://doi.org/10.1371/journal.pone.0246857
Jiang, Z., Wei, G., & Guo, Y. (2022). Picture fuzzy MABAC method based on prospect theory for multiple attribute group decision making and its application to suppliers selection. Journal of Intelligent & Fuzzy Systems, 42(4), 3405-3415. https://doi.org/10.3233/JIFS-211359
Torkayesh, A. E., Tirkolaee, E. B., Bahrini, A., Pamucar, D., & Khakbaz, A. (2023). A systematic literature review of MABAC method and applications: An outlook for sustainability and circularity. Informatica, 34(2), 415-448. https://doi.org/10.15388/23-INFOR511
Akram, M., Naz, S., Feng, F., Ali, G., & Shafiq, A. (2023). Extended MABAC method based on 2-tuple linguistic T-spherical fuzzy sets and Heronian mean operators: An application to alternative fuel selection. Mathematics, 8(5), 10619-10653. http://dx.doi.org/%2010.3934/math.2023539
Mandal, U., & Seikh, M. R. (2023). Interval-valued spherical fuzzy MABAC method based on Dombi aggregation operators with unknown attribute weights to select plastic waste management process. Applied Soft Computing, 145, 110516. https://doi.org/10.1016/j.asoc.2023.110516
Gul, M., & Celik, E. (2018). Fuzzy rule-based Fine–Kinney risk assessment approach for rail transportation systems. Human and Ecological Risk Assessment: An International Journal, 24(7), 1786-1812. https://doi.org/10.1080/10807039.2017.1422975
Krishankumar, R., Subrajaa, L. S., Ravichandran, K. S., Kar, S., & Saeid, A. B. (2019). A framework for multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic term set. International Journal of Fuzzy Systems, 21, 1130-1143. https://doi.org/10.1007/s40815-019-00618-w
Karasan, A. (2019). A novel hesitant intuitionistic fuzzy linguistic AHP method and its application to prioritization of investment alternatives. International Journal of the Analytic Hierarchy Process, 11(1), 127-142. https://doi.org/10.13033/ijahp.v11i1.610
Seiti, H., Hafezalkotob, A., & Martínez, L. (2021). R-sets, comprehensive fuzzy sets risk modeling for risk-based information fusion and decision-making. IEEE Transactions on Fuzzy Systems, 29(2), 385-399. https://doi.org/10.1109/TFUZZ.2019.2955061
Ngan, S. L., How, B. S., Teng, S. Y., Leong, W. D., Loy, A. C. M., Yatim, P., Promentilla, M.A.B.,& Lam, H. L. (2020). A hybrid approach to prioritize risk mitigation strategies for biomass polygeneration systems. Renewable and Sustainable Energy Reviews, 121, 109679. https://doi.org/10.1016/j.rser.2019.109679
Gul, M., & Ak, M. F. (2020). Assessment of occupational risks from human health and environmental perspectives: a new integrated approach and its application using fuzzy BWM and fuzzy MAIRCA. Stochastic Environmental Research and Risk Assessment, 34(8), 1231-1262. https://doi.org/10.1007/s00477-020-01816-x
Ramavandi, B., Darabi, A. H., & Omidvar, M. (2021). Risk assessment of hot and humid environments through an integrated fuzzy AHP-VIKOR method. Stochastic Environmental Research and Risk Assessment, 35(12), 2425-2438. https://doi.org/10.1007/s00477-021-01995-1
W. Wang, W. Jiang, X. Han, S. Liu. (2022). An extended gained and lost dominance score method based risk prioritization for Fine-Kinney model with interval type-2 fuzzy information. Human and Ecological Risk Assessment: An International Journal, 28, 154-183. https://doi.org/10.1080/10807039.2021.2023807
Wang, W., Wang, Y., Fan, S., Han, X., Wu, Q., & Pamucar, D. (2023). A complex spherical fuzzy CRADIS method based Fine-Kinney framework for occupational risk evaluation in natural gas pipeline construction. Journal of Petroleum Science and Engineering, 220, 111246. https://doi.org/10.1016/j.petrol.2022.111246
Wang, W., Han, X., Ding, W., Wu, Q., Chen, X., & Deveci, M. (2023). A Fermatean fuzzy Fine–Kinney for occupational risk evaluation using extensible MARCOS with prospect theory. Engineering Applications of Artificial Intelligence, 117, 105518. https://doi.org/10.1016/j.engappai.2022.105518
Fang, C., Chen, Y., Wang, Y., Wang, W., & Yu, Q. (2023). (2023). A Fermatean fuzzy GLDS approach for ranking potential risk in the Fine-Kinney framework. Journal of Intelligent & Fuzzy Systems, 45(2), 3149-3163. https://doi.org/10.3233/JIFS-230423
Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016-3028. https://doi.org/10.1016/j.eswa.2014.11.057
Verma, R. (2021). On intuitionistic fuzzy order- α divergence and entropy measures with MABAC method for multiple attribute group decision-making. Journal of Intelligent & Fuzzy Systems, 40(1), 1191-1217. https://doi.org/10.3233/JIFS-201540
M. Zhao, G. Wei, X. Chen, Y. Wei. (2021). Intuitionistic fuzzy MABAC method based on cumulative prospect theory for multiple attribute group decision making. International Journal of Intelligent Systems, 36, 6337-6359. https://doi.org/10.1002/int.22552.
Jana, C., Garg, H., & Pal, M. (2023). Multi-attribute decision making for power Dombi operators under Pythagorean fuzzy information with MABAC method. Journal of Ambient Intelligence and Humanized Computing, 14(8), 10761-10778. https://doi.org/10.1007/s12652-022-04348-0
Wang, Z., Cai, Q., Lu, J., & Wei, G. (2023). MABAC method based on cumulative prospect theory for MCGDM with dual probabilistic linguistic term set and applications to sustainable supplier selection. Journal of Intelligent & Fuzzy Systems, 45(3), 3587-3608. https://doi.org /10.3233/JIFS-230410
Deveci, M., Erdogan, N., Cali, U., Stekli, J., & Zhong, S. (2021). Type-2 neutrosophic number based multi-attributive border approximation area comparison (MABAC) approach for offshore wind farm site selection in USA. Engineering Applications of Artificial Intelligence, 103, 104311. https://doi.org/10.1016/j.engappai.2021.104311
Chattopadhyay, R., Das, P. P., & Chakraborty, S. (2022). Development of a rough-MABAC-DoE-based metamodel for supplier selection in an iron and steel industry. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 20-40. https://doi.org/10.31181/oresta190222046c
Tan, J., Liu, Y., Senapati, T., Garg, H., & Rong, Y. (2023). An extended MABAC method based on prospect theory with unknown weight information under Fermatean fuzzy environment for risk investment assessment in B&R. Journal of Ambient Intelligence and Humanized Computing, 14(10), 13067-13096. https://doi.org/10.1007/s12652-022-03769-1
Kutlu Gündoğdu, F., & Kahraman, C. (2019). A novel VIKOR method using spherical fuzzy sets and its application to warehouse site selection. Journal of Intelligent & Fuzzy Systems, 37(1), 1197-1211. https://doi.org/10.3233/JIFS-182651
Gul, M., & Ak, M. F. (2021). A modified failure modes and effects analysis using interval-valued spherical fuzzy extension of TOPSIS method: case study in a marble manufacturing facility. Soft Computing, 25(8), 6157-6178. https://doi.org/10.1007/s00500-021-05605-8
Zhou, X., Chen, C., Tian, H., Wang, L., Yang, Z., & Yang, H. (2021). Time-varying FMEA method based on interval-valued spherical fuzzy theory. Quality and Reliability Engineering International, 37(8), 3713-3729. https://doi.org/10.1002/qre.2943
Mathew, M., Chakrabortty, R. K., & Ryan, M. J. (2020). A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Engineering Applications of Artificial Intelligence, 96, 103988. https://doi.org/10.1016/j.engappai.2020.103988
Mahmood, T., Ullah, K., Khan, Q., & Jan, N. (2019). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing and Applications, 31, 7041-7053. https://doi.org/10.1007/s00521-018-3521-2
Ju, Y., Liang, Y., Luo, C., Dong, P., Gonzalez, E. D. S., & Wang, A. (2021). T-spherical fuzzy TODIM method for multi-criteria group decision-making problem with incomplete weight information. Soft Computing, 25, 2981-3001. https://doi.org/10.1007/s00500-020-05357-x
Chang, K. H., & Cheng, C. H. (2011). Evaluating the risk of failure using the fuzzy OWA and DEMATEL method. Journal of Intelligent Manufacturing, 22, 113-129. https://doi.org/10.1007/s10845-009-0266-x
Jianghong, ZHU., Guofang, LI., Rui, WANG., & Yanlai, LI. (2017). FMEA based assessment of risk that metro train stops abnormally. China Safety Science Journal, 27(2), 145-150. https://doi.org/10.16265/j.cnki.issn1003-3033.2017.02.026
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