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    Analysis of Occupational Health and Safety Risks in Beekeeping with FMEA Method
    (2023) Özdemir, Mustafa; Kökhan, Serhan
    Contrary to popular belief, beekeeping, which dates back to prehistoric times and is one of the most important plant and animal production branches today, is not an innocent profession in terms of occupational health and safety. In this study, in order to determine the occupational health and safety risk factors in the beekeeping profession, Interviews with beekeepers were conducted in 10 apiaries operating in Bayburt, where especially wandering beekeeping is practiced. In light of the data obtained from the danger hunt applied by the occupational health and safety specialist, ergonomic, physical, biological, and chemical risks were revealed using the FMEA risk analysis method. The effect, probability, and detection values were found for each failure mode, and then Risk Priority Number values were calculated. As a result of the study, for the five basic stages of beekeeping, 15 processes, 39 failure modes, 72 potential effects, and 39 failure causes were determined. Failure modes with a Risk Priority Number value of 100 and above were evaluated as “situations where urgent action and axiom should be taken,” and preventive axioms were proposed for each relevant failure mode. The number of studies on the risk factors in the beekeeping profession is very limited in the literature. For this reason, it is predicted that this study will fill an important gap in the related field and make significant contributions to the literature.
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    Parallel Assembly Lines with Heterogeneous Workforce: A Cost-Driven Mathematical Model and Simulated Annealing Approach
    (Springer Science and Business Media Deutschland GmbH, 2021) Kökhan, Serhan; Baykoç, Ömer Faruk
    In recent years it has become possible for people, machines and robots to work collaboratively in most production environments. Collaborative working models, which are diversified with the concept of Industry 4.0, have become an important issue in Assembly Line Problems (ALP). In this study, a cost-driven mathematical model which combines Parallel Assembly Line Problems and human and robot collaborative workforce which is an important parameter of Industry 4.0 philosophy is proposed. The mathematical model was tested for new data sets derived from existing data sets in the parallel assembly lines literature and the results were analyzed. Because of the NP-hardness of ALP, a heuristic approach based Simulated Annealing (SA) algorithm was proposed to solve for larger sized problems. For this purpose, the same data sets were solved with SA and the results were compared and analyzed. The results show that the mathematical model gives better results up to Tonge data set (140 task), but feasible solutions can only be obtained by SA algorithm for the rest. Since there are very few studies with heterogeneous workforce in Parallel Assembly Line Balancing Problems (PALBP), this study is believed to provide a significant contribution to related literature. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.