供应链
托普西斯
层次分析法
计算机科学
运筹学
背景(考古学)
排名(信息检索)
多准则决策分析
风险分析(工程)
订单(交换)
风险管理
选择(遗传算法)
供应链管理
模糊逻辑
业务
工程类
人工智能
财务
营销
古生物学
生物
作者
Y. B. Zhang,Zhanwen Niu,Yaqing Zuo,Chaochao Liu
出处
期刊:Infor
[Informa]
日期:2023-09-08
卷期号:61 (4): 530-558
标识
DOI:10.1080/03155986.2023.2241324
摘要
AbstractIn the context of collaborative manufacturing, cyber risk caused by cyber attacks may lead to severe supply chain disruption. Currently, supplier selection and order allocation is regarded as effective means to mitigate the risks that might cause disruption. Thus, we propose a two-stage hybrid model for supplier selection and order allocation under cyber risk. The hybrid model consists of fuzzy analytical hierarchy process (Fuzzy AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and two-stage mixed integer linear programming (MIP). Based on the extracted cyber risk indicators, a Fuzzy AHP is used to calculate the level of cyber risk of suppliers. TOPSIS is utilized to quantitatively evaluate the cyber risk of suppliers and determine the ranking of suppliers. Then, a two-stage MIP model is developed to support decision-making on order allocation. The first-stage decisions are determined without emergencies, and the second-stage decisions are determined under emergencies. The results reveal that application of the proposed two-stage hybrid model could mitigate the negative impacts of cyber risks. By providing a theoretical basis and quantitative method for cyber risk evaluation, this research is of theoretical and practical significance to the field of supply chain management.Keywords: Collaborative manufacturingsupplier selectionorder allocationcyber risk Disclosure statementThe authors declare that there are no relevant financial or non-financial competing interests to report in this paper.Data availability statementThe data that support the findings of this study are available from the corresponding author, Chaochao Liu, upon reasonable request.Additional informationFundingThis work was supported by the National Key Research and Development Program of China under Grant NO. 2020YFB1712001.
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