计算机集成制造系统 ›› 2020, Vol. 26 ›› Issue (5期): 1355-1365.DOI: 10.13196/j.cims.2020.05.021

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基于欠载失效的供应链网络级联失效建模

王英聪1,肖人彬2+   

  1. 1.郑州轻工业大学电气信息工程学院
    2.华中科技大学人工智能与自动化学院
  • 出版日期:2020-05-31 发布日期:2020-05-31
  • 基金资助:
    国家自然科学基金资助项目(51875220,61702463);郑州轻工业学院博士科研基金资助项目(2017BSJJ004);河南省科技攻关资助项目(182102310968)。

Underload cascading failure model for supply chain networks

  • Online:2020-05-31 Published:2020-05-31
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.51875220,61702463),the Doctoral Scientific Research Foundation of Zhengzhou University of Light Industry,China(No.2017BSJJ004),and the Science and Technology Research Project of Henan Province,China(No.182102310968).

摘要: 鉴于直接采用基础设施网络中的过载级联失效模型研究供应链网络中的欠载级联失效现象存在不足,从企业欠载失效、企业负载容量具有上下限和负载按照企业间的业务关系强度重分配3方面出发,提出一个更加适合供应链网络的级联失效模型。利用Erdos-Renyi网络模型和Barabasi-Albert网络模型构建了供应链网络,在随机攻击和蓄意攻击模式下验证了模型的有效性,同时研究了模型中的各参数对级联失效的影响。结果表明,负载可调参数θ和关系强度参数τ存在一个最优取值区间,使得网络对级联失效表现出强鲁棒性;容量上限参数δ与级联失效规模负相关;容量下限参数σ与级联失效规模正相关。

关键词: 供应链网络, 欠载级联失效, 容量上限, 容量下限, 级联失效模型

Abstract: Owing to the deficiency of researching underload cascading failure by underload cascade failure model of supply chain network directly in infrastructure network,a new cascading failure model was proposed from three aspects: firm failure due to under-load,load capacity of firm with upper and lower limits,and loads redistribution between firms according to business relationship strength.Erdos-Renyi and Barabasi-Albert network model were used to generate supply chain networks,and the validity of the proposed cascading model was verified under random attack and target attack.The effects of model parameters on cascading failure spread were investigated.Results demonstrated that there was a best value range for load parameter  and relationship strength parameter,in which the networks achieved better robustness against cascading failure spread;there was a negative correlation between capacity upper limit parameter  and cascading failure size;there was a positive correlation between capacity lower limit parameter  and cascading failure size.

Key words: supply chain networks, underload cascading failure, capacity upper limit, capacity lower limit, cascading failure model

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