A Proposed Methodology for Risk Classification Using Fuzzy Group Decision Making and Fuzzy C-Means
Künye
Palop, J. J., Mucke, L., & Roberson, E. D. (2010). Quantifying biomarkers of cognitive dysfunction and neuronal network hyperexcitability in mouse models of Alzheimer’s disease: depletion of calcium-dependent proteins and inhibitory hippocampal remodeling. In Alzheimer's Disease and Frontotemporal Dementia (pp. 245-262). Humana Press, Totowa, NJ.Özet
Clean production and resource efficiency are two major concerns of contemporary manufacturing processes. The main reason is that the resources and environment are significant concerns for the future. The study proposes the assessment of risks in a butchery unit in a major retail company. The regular assessment using impact and probability requires concrete input from the relevant expert. The possible impact and probability are challenging to measure because of their vagueness in nature. The proposed study uses the aggregation of fuzzy opinions under group decisions to assess the impact and probability of each risk in the butchery unit for the first phase. The outputs of the first phase are the impact and probability values for each risk based on group decisions under fuzzy logic. The second phase involves converting global risk values to classified risk groups. In our study, Fuzzy-C-Means will be used to classify risks based on their importance to 3 groups. By applying classification, the responsible for the relevant actions can take preventive actions for the risks that deserve the most attention. The proposed methodology is applied to a real data set of risk analysis. Results of the research demonstrate that the use of fuzzy logic in the assessment of risk analysis shows a promising approach and is accepted as an improvement over the existing practice to define the risk and classify it. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
