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Assessing the Robustness of a Factory Amid the COVID-19 Pandemic: A Fuzzy Collaborative Intelligence Approach.
Chen, Toly; Wang, Yu-Cheng; Chiu, Min-Chi.
  • Chen T; Department of Industrial Engineering and Management, National Chiao Tung University, 1001, University Road, Hsinchu 30010, Taiwan.
  • Wang YC; Department of Aeronautical Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan.
  • Chiu MC; Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan.
Healthcare (Basel) ; 8(4)2020 Nov 12.
Article in English | MEDLINE | ID: covidwho-918942
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Year: 2020 Document Type: Article Affiliation country: Healthcare8040481

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies Language: English Year: 2020 Document Type: Article Affiliation country: Healthcare8040481