Chen Toly, Wang Yu-Cheng, Chiu Min-Chi
Department of Industrial Engineering and Management, National Chiao Tung University, 1001, University Road, Hsinchu 30010, Taiwan.
Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan.
Healthcare (Basel). 2020 Nov 12;8(4):481. doi: 10.3390/healthcare8040481.
The COVID-19 pandemic has affected the operations of factories worldwide. However, the impact of the COVID-19 pandemic on different factories is not the same. In other words, the robustness of factories to the COVID-19 pandemic varies. To explore this topic, this study proposes a fuzzy collaborative intelligence approach to assess the robustness of a factory to the COVID-19 pandemic. In the proposed methodology, first, a number of experts apply a fuzzy collaborative intelligence approach to jointly evaluate the relative priorities of factors that affect the robustness of a factory to the COVID-19 pandemic. Subsequently, based on the evaluated relative priorities, a fuzzy weighted average method is applied to assess the robustness of a factory to the COVID-19 pandemic. The assessment result can be compared with that of another factory using a fuzzy technique for order preference by similarity to ideal solution. The proposed methodology has been applied to assess the robustness of a wafer fabrication factory in Taiwan to the COVID-19 pandemic.
新冠疫情影响了全球工厂的运营。然而,新冠疫情对不同工厂的影响不尽相同。换句话说,工厂对新冠疫情的抵御能力存在差异。为探究这一主题,本研究提出一种模糊协作智能方法,用于评估工厂对新冠疫情的抵御能力。在所提出的方法中,首先,多位专家运用模糊协作智能方法共同评估影响工厂对新冠疫情抵御能力的各因素的相对优先级。随后,基于评估出的相对优先级,应用模糊加权平均法来评估工厂对新冠疫情的抵御能力。可使用与理想解相似性的顺序偏好模糊技术,将评估结果与另一家工厂的结果进行比较。所提出的方法已应用于评估台湾一家晶圆制造厂对新冠疫情的抵御能力。