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Fine–kinney-based occupational risk assessment using interval type-2 fuzzy topsisGul, MuhammetMete, SuleymanSerin, FarukCelik, ErkanThis chapter proposed an improved Fine–Kinney occupational risk assessment approach using a well-known MCDM method “TOPSIS” under interval type-2 fuzzy set concept. It is defined as a technique for order preference by similarity to ideal solution by Hwang and Yoon [1]. It is based on separation from ideal and anti-ideal solution concept. Since the initial crisp data-based version is insufficient by time in reflecting the uncertainty in decision-maker’s opinions, fuzzy sets are integrated to the TOPSIS algorithm to provide a solid and comprehensive method. Interval type-2 fuzzy set is an improved version of the type-1 fuzzy set. It is also a special version of a general type-2 fuzzy set. Since general type-2 fuzzy systems contain complex computational operations, they cannot be easily applied to real-world problems such as occupational risk assessment. Interval type-2 fuzzy sets are the most frequently used type-2 fuzzy sets due to their ability in handling more uncertainty and producing more accurate and solid results. The Fine–Kinney concept is merged with the interval type-2 fuzzy set concept and TOPSIS for the first time through the literature. To demonstrate the applicability of the proposed approach, a case study is carried out in a chrome plating unit of a gun factory. Some beneficial validation and sensitivity analysis are also performed. Finally, as a creative contribution of our book, the implementation of the proposed approach in Python is performed.Springer Science and Business Media Deutschland GmbH2021Book Chapterhttps://rdlopen.rawdatalibrary.net/r/munzur/2380https://doi.org/10.1007/978-3-030-52148-6_31434-9922enStudies in Fuzziness and Soft Computinginfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/bookParthttps://munzur-rdlopen.rawdatalibrary.net/tr/items/munzur/2380