Abstract
In this chapter, we improved Fine–Kinney occupational risk assessment approach with interval type-2 fuzzy QUALIFLEX (IT2FQUALIFLEX). QUALIFLEX is an outranking multi-attribute decision-making method proposed by an extension of the Paelinck’s (Pap Reg Sci Assoc 36:59–74,[1]), generalized Jacquet-Lagreze’s permutation method. Similar to other outranking solution-based approaches, it considers the solution which is a comparison of hazards. In this chapter, we adapted the interval type-2 fuzzy sets (IT2FSs) into QUALIFLEX as it reflects the uncertainty well in decision-making. IT2FQUALIFLEX algorithm under the Fine–Kinney concept provides a useful and solid approach to the occupational health and safety risk assessment. In addition to proposing this new approach, a case study is performed in the chrome plating unit. A validation is also performed in this study. Finally, the proposed approach is implemented in Python.
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References
Paelinck, J. H. P. (1976). Qualitative multiple criteria analysis, environmental protection and multiregional development. Papers of the Regional Science Association, 36, 59–74.
Alinezhad, A., & Khalili, J. (2019). QUALIFLEX method. In New methods and applications in multiple attribute decision making (MADM) (pp. 41–46). Springer, Cham.
Chen, T. Y., Chang, C. H., & Lu, J. F. R. (2013). The extended QUALIFLEX method for multiple criteria decision analysis based on interval type-2 fuzzy sets and applications to medical decision making. European Journal of Operational Research, 226(3), 615–625.
Lee, L. W., Chen, S. M. (2008). Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets. In Proceedings of the seventh international conference on machine learning and cybernetic (pp. 3260–3265). Taipei. Retrieved July 12–15, 2008.
Chen, S. M., & Lee, L. W. (2010). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Systems with Applications, 37(4), 2790–2798.
Gul, M., Guven, B., & Guneri, A. F. (2018). A new Fine–Kinney-based risk assessment framework using FAHP-FVIKOR incorporation. Journal of Loss Prevention in the Process Industries, 53, 3–16.
Celik, E., Aydin, N., & Gumus, A. T. (2014). A multiattribute customer satisfaction evaluation approach for rail transit network: A real case study for Istanbul, Turkey. Transport Policy, 36, 283–293.
Wang, J. C., Tsao, C. Y., & Chen, T. Y. (2015). A likelihood-based QUALIFLEX method with interval type-2 fuzzy sets for multiple criteria decision analysis. Soft Computing, 19(8), 2225–2243.
Zhang, X. (2016). Multicriteria Pythagorean fuzzy decision analysis: A hierarchical QUALIFLEX approach with the closeness index-based ranking methods. Information Sciences, 330, 104–124.
Chen, T. Y. (2014). Interval-valued intuitionistic fuzzy QUALIFLEX method with a likelihood-based comparison approach for multiple criteria decision analysis. Information Sciences, 261, 149–169.
Li, J., & Wang, J. Q. (2017). An extended QUALIFLEX method under probability hesitant fuzzy environment for selecting green suppliers. International Journal of Fuzzy Systems, 19(6), 1866–1879.
Zhang, X., & Xu, Z. (2015). Hesitant fuzzy QUALIFLEX approach with a signed distance-based comparison method for multiple criteria decision analysis. Expert Systems with Applications, 42(2), 873–884.
Zhang, X., Xu, Z., & Liu, M. (2016). Hesitant trapezoidal fuzzy QUALIFLEX method and its application in the evaluation of green supply chain initiatives. Sustainability, 8(9), 952.
Chen, T. Y. (2013). Data construction process and QUALIFLEX-based method for multiple-criteria group decision making with interval-valued intuitionistic fuzzy sets. International Journal of Information Technology & Decision Making, 12(03), 425–467.
Demirel, H., Akyuz, E., Celik, E., & Alarcin, F. (2019). An interval type-2 fuzzy QUALIFLEX approach to measure performance effectiveness of ballast water treatment (BWT) system on-board ship. Ships and Offshore Structures, 14(7), 675–683.
Gumus, A. T., Aydin, N., Celik, E. (2014). Passenger satisfaction evaluation for rail transit lines in Istanbul using qualiflex approach based on interval type-2 trapezoidal fuzzy numbers. In CIE 2014—44th international conference on computers and industrial engineering and IMSS 2014—9th international symposium on intelligent manufacturing and service systems, joint international symposium on the social impacts of developments in information, manufacturing and service systems—proceedings (pp. 343–357). Istanbul, Turkey.
Celik, E., Gul, M., Aydin, N., Gumus, A. T., & Guneri, A. F. (2015). A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowledge-Based Systems, 85, 329–341.
Mendel, J. M., John, R. I., & Liu, F. L. (2006). Interval type-2 fuzzy logical systems made simple. IEEE Transactions on Fuzzy Systems, 14(6), 808–821.
Zhang, Z. (2017). Multi-criteria decision-making using interval-valued hesitant fuzzy QUALIFLEX methods based on a likelihood-based comparison approach. Neural Computing and Applications, 28(7), 1835–1854.
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Gul, M., Mete, S., Serin, F., Celik, E. (2021). Fine–Kinney-Based Occupational Risk Assessment Using Interval Type-2 Fuzzy QUALIFLEX. In: Fine–Kinney-Based Fuzzy Multi-criteria Occupational Risk Assessment. Studies in Fuzziness and Soft Computing, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-030-52148-6_8
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DOI: https://doi.org/10.1007/978-3-030-52148-6_8
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