Abstract
Neutrosophic sets are initially recommended by Smarandache (First international conference on neutrosophy, neutrosophic logic, set, probability, and statistics. University of New Mexico, Gallup, NM, pp 338–353, 2002 [1]). These sets reflect uncertainty and vagueness in real-world problems better than classical fuzzy set theory. It takes into consideration three decision-making situations called indeterminacy, truthiness, and falsity. In Zadeh traditional fuzzy set theory, there is just membership function fuzzy set degree. But, in neutrosophic environment, it considers three membership functions. Unlike intuitionistic fuzzy sets, an indeterminacy degree is considered. In this chapter, we applied a special form of neutrosophic set as single-valued neutrosophic set (SVNs) with the technique for order preference by similarity to ideal solution (TOPSIS) under the concept of Fine–Kinney occupational risk assessment. Since the mere TOPSIS has failed to handle imprecise and vague information which usually exist in real-world problems, we follow the integration of SVNs and TOPSIS. To demonstrate the applicability of the novel approach, a case study of risk assessment of a wind turbine in times of operation was provided. Comparative analysis with some similar approaches and sensitivity analysis by changing the weights of Fine–Kinney parameters are carried out. Finally, the Python implementation of the proposed approach is executed to be useful for those concerned in the future.
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Smarandache, F. (2002). Neutrosophy and neutrosophic logic. In First international conference on neutrosophy, neutrosophic logic, set, probability, and statistics (pp. 338–353). Gallup, NM: University of New Mexico.
Biswas, P., Pramanik, S., & Giri, B. C. (2016). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications, 27, 727–737.
Liu, P., & Wang, Y. (2014). Multiple attribute decision-making method based on single valued neutrosophic normalized weighted Bonferroni mean. Neural Computing and Applications, 25, 2001–2010.
Majumdar, P., & Samanta, S. K. (2014). On similarity and entropy of neutrosophic sets. Journal of Intelligent & Fuzzy Systems, 26, 1245–1252.
Yoon, K., & Hwang, C. (1981). TOPSIS (technique for order preference by similarity to ideal solution)–a multiple attribute decision making, w: Multiple attribute decision making–methods and applications, a state-of-the-at survey. Berlin: Springer.
Yong, D. (2006). Plant location selection based on fuzzy TOPSIS. The International Journal of Advanced Manufacturing Technology, 28, 839–844.
Chen, T.-Y., & Tsao, C.-Y. (2008). The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets and Systems, 159, 1410–1428.
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, 2790–2798.
Celik, E., Bilisik, O. N., Erdogan, M., et al. (2013). An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul. Transportation Research Part E: Logistics and Transportation Review, 58, 28–51.
Cevik Onar, S., Oztaysi, B., & Kahraman, C. (2014). Strategic decision selection using hesitant fuzzy TOPSIS and interval type-2 fuzzy AHP: A case study. International Journal of Computational intelligence systems, 7, 1002–1021.
Boran, F., Boran, K., & Menlik, T. (2012). The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS. Energy Sources, Part B: Economics, Planning and Policy, 7, 81–90.
Ozdemir, Y., Gul, M., & Celik, E. (2017). Assessment of occupational hazards and associated risks in fuzzy environment: A case study of a university chemical laboratory. Human and Ecological Risk Assessment: An International Journal, 23, 895–924.
Ak, M. F., & Gul, M. (2018). AHP–TOPSIS integration extended with Pythagorean fuzzy sets for information security risk analysis. Complex & Intelligent Systems 1–14.
Gul, M., & Ak, M. F. (2018). A comparative outline for quantifying risk ratings in occupational health and safety risk assessment. Journal of Cleaner Production, 196, 653–664.
Oz, N. E., Mete, S., Serin, F., & Gul, M. (2018). 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 1–18.
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 1–16. https://doi.org/10.1080/10807039.2018.1546115.
Mete, S., Serin, F., Oz, N. E., & Gul, M. (2019). A decision-support system based on Pythagorean fuzzy VIKOR for occupational risk assessment of a natural gas pipeline construction. Journal of Natural Gas Science and Engineering, 71, 102979.
Gul, M., Ak, M. F., & Guneri, A. F. (2019). Pythagorean fuzzy VIKOR-based approach for safety risk assessment in mine industry. Journal of Safety Research, 69, 135–153.
Gul, M., Guneri, A. F., & Baskan, M. (2018). An occupational risk assessment approach for construction and operation period of wind turbines. Global Journal of Environmental Science and Management, 4(3), 281–298.
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Gul, M., Mete, S., Serin, F., Celik, E. (2021). Fine–Kinney-Based Occupational Risk Assessment Using Single-Valued Neutrosophic TOPSIS. 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_7
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DOI: https://doi.org/10.1007/978-3-030-52148-6_7
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