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dc.contributor.authorGül, Muhammet
dc.contributor.authorMete, Süleyman
dc.contributor.authorSerin, Faruk
dc.contributor.authorÇelik, Erkan
dc.date.accessioned2022-07-04T14:06:39Z
dc.date.available2022-07-04T14:06:39Z
dc.identifier.citationGül M. (Editör), Mete S. (Editör), Serin F. (Editör), Çelik E. (Editör), Fine–Kinney-Based Fuzzy Multi-criteria Occupational Risk Assessment: Approaches, Case Studies and Python Applications, Springer, London/Berlin , Zug, 2021
dc.identifier.otherav_685659cb-562d-453c-bd8a-785df5fe32ab
dc.identifier.othervv_1032021
dc.identifier.urihttp://hdl.handle.net/20.500.12627/183121
dc.identifier.urihttps://link.springer.com/book/10.1007/978-3-030-52148-6#toc
dc.description.abstractThis book presents a number of approaches to Fine–Kinney–based multi-criteria occupational risk-assessment. For each proposed approach, it provides case studies demonstrating their applicability, as well as Python coding, which will enable readers to implement them into their own risk assessment process.The book begins by giving a review of Fine–Kinney occupational risk-assessment methods and their extension by fuzzy sets. It then progresses in a logical fashion, dedicating a chapter to each approach, including the fuzzy best and worst method, interval-valued Pythagorean fuzzy VIKOR and interval type-2 fuzzy QUALIFLEX.This book will be of interest to professionals and researchers working in the field of occupational risk management, as well as postgraduate and undergraduate students studying applications of fuzzy systems.
dc.language.isoeng
dc.publisherSpringer, London/Berlin 
dc.subjectMühendislik ve Teknoloji
dc.subjectMÜHENDİSLİK, ENDÜSTRİYEL
dc.subjectEndüstri Mühendisliği
dc.subjectMühendislik
dc.subjectMühendislik, Bilişim ve Teknoloji (ENG)
dc.titleFine–Kinney-Based Fuzzy Multi-criteria Occupational Risk Assessment: Approaches, Case Studies and Python Applications
dc.typeKitap
dc.contributor.departmentGaziantep Üniversitesi , Mühendislik Fakültesi , Endüstri Mühendisliği Bölümü
dc.contributor.firstauthorID3407680


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