Modeling and analysis of under-load-based cascading failures in supply chain networks

被引:65
作者
Wang, Yingcong [1 ]
Zhang, Fengpeng [2 ]
机构
[1] Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Henan, Peoples R China
[2] Otis Elect Elevator Co Ltd, Hangzhou 310019, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Cascading failure; Supply chain network; Under-load; Load capacity; SCALE-FREE NETWORKS; COMPLEX NETWORKS; RESILIENCE; ROBUSTNESS; VULNERABILITY; RISK;
D O I
10.1007/s11071-018-4135-z
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The phenomena of cascading failures often happen in complex networks. In most infrastructure networks, the subsequent failures of nodes are caused by overload and many overload cascading failure models are developed. Recently, some of these models are adopted to investigate the cascading failure phenomenon in supply chain networks, which cannot capture the real case very well. The subsequent failures of upriver/downriver firms in supply chain networks are triggered by the decreased product demand/material supply, i.e., under-load cascading failures take place. Based on the under-load failures, this paper proposed a more realistic cascading failure model for supply chain networks. In this model, the node firms are characterized by capacities with upper bound parameter and lower bound parameter . Results showed that has a negative relationship with cascading size, while has a positive relationship with cascading size. In addition, cascading size is mainly determined by , and helps mitigate the cascading propagation. In reality, is correlated with the spare production capacity of firms, the holding cost of which is high under stable operation of the market. is related to the core competence of firms, which is hard to improve in the short term. Our work may be helpful for developing the cascade control and defense strategies in supply chain networks.
引用
收藏
页码:1403 / 1417
页数:15
相关论文
共 54 条
  • [1] Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm
    Abu Arqub, Omar
    Abo-Hammour, Zaer
    [J]. INFORMATION SCIENCES, 2014, 279 : 396 - 415
  • [2] [Anonymous], 2003, SUPPLY CHAIN MANAGEM
  • [3] Optimizing complex networks for resilience against cascading failure
    Ash, J.
    Newth, D.
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2007, 380 : 673 - 683
  • [4] Emergence of scaling in random networks
    Barabási, AL
    Albert, R
    [J]. SCIENCE, 1999, 286 (5439) : 509 - 512
  • [5] The architecture of complex weighted networks
    Barrat, A
    Barthélemy, M
    Pastor-Satorras, R
    Vespignani, A
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2004, 101 (11) : 3747 - 3752
  • [6] Network analysis of supply chain systems: A systematic review and future research
    Bellamy, Marcus A.
    Basole, Rahul C.
    [J]. SYSTEMS ENGINEERING, 2013, 16 (02) : 235 - 249
  • [7] A network-based decision tool to model uncertainty in supply chain operations
    Blackhurst, J.
    Wu, T.
    O'Grady, P.
    [J]. PRODUCTION PLANNING & CONTROL, 2007, 18 (06) : 526 - 535
  • [8] Complex networks: Structure and dynamics
    Boccaletti, S.
    Latora, V.
    Moreno, Y.
    Chavez, M.
    Hwang, D. -U.
    [J]. PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 2006, 424 (4-5): : 175 - 308
  • [9] Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty
    Cardoso, Sonia R.
    Barbosa-Povoa, Ana Paula
    Relvas, Susana
    Novais, Augusto Q.
    [J]. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2015, 56 : 53 - 73
  • [10] Unveiling the structure of supply networks: case studies in Honda, Acura, and DaimlerChrysler
    Choi, TY
    Hong, Y
    [J]. JOURNAL OF OPERATIONS MANAGEMENT, 2002, 20 (05) : 469 - 493