Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 6, November-December 2024
Page(s) 4905 - 4925
DOI https://doi.org/10.1051/ro/2024127
Published online 21 November 2024
  • B. Adenso-Díaz, J. Mar-Ortiz and S. Lozano, Assessing supply chain robustness to links failure. Int. J. Prod. Res. 56 (2017) 5104–5117. [Google Scholar]
  • M.Z. Alvarenga, M.P.V.d. Oliveira and T.A.G.F.d. Oliveira, The impact of using digital technologies on supply chain resilience and robustness: the role of memory under the covid-19 outbreak. Supply Chain Manag. Int. J. 6 (2023) 1–32. [Google Scholar]
  • A. Azzolin, L. Dueñas-Osorio, F. Cadini and E. Zio, Electrical and topological drivers of the cascading failure dynamics in power transmission networks. Reliab. Eng. Syst. Saf. 175 (2018) 196–206. [CrossRef] [Google Scholar]
  • L. Bai, C. Song, X. Zhou, Y. Tian and L. Wei, Assessing project portfolio risk via an enhanced GA-BPNN combined with PCA. Eng. Appl. Artif. Intell. 126 (2023) 106779. [CrossRef] [Google Scholar]
  • L. Bai, Y. An and Y. Sun, Measurement of project portfolio benefits with a GA-BP neural network group. IEEE Trans. Eng. Manag. 71 (2024) 4737–4749. [Google Scholar]
  • L. Bai, J. Lin, Q. Xie, Y. Zhang and X. Guo, A hybrid simulation model for the allocation of shared resources in a project portfolio. IEEE Trans. Eng. Manag. 71 (2024) 5998–6014. [CrossRef] [Google Scholar]
  • Z. Bao, Z. Jiang and L. Wu, Evaluation of bi-directional cascading failure propagation in integrated electricity-natural gas system. Int. J. Electr. Power Energy Syst. 121 (2020) 1–10. [Google Scholar]
  • A. Beiranvand and P. Cuffe, Negative results on deploying distributed series reactance devices to improve power system robustness against cascading failures. IEEE Trans. Power Syst. 36 (2021) 5210–5221. [CrossRef] [Google Scholar]
  • K. Bilal, M. Manzano, A. Erbad, E. Calle and S.U. Khan, Robustness quantification of hierarchical complex networks under targeted failures. Comput. Electr. Eng. 72 (2018) 112–124. [CrossRef] [Google Scholar]
  • H. Chen, L. Zhang, Q. Liu, H. Wang and X. Dai, Simulation-based vulnerability assessment in transit systems with cascade failures. J. Clean. Prod. 295 (2021) 1–17. [Google Scholar]
  • D. Chen, D. Sun, Y. Yin, L. Dhamotharan, A. Kumar and Y. Guo, The resilience of logistics network against node failures. Int. J. Prod. Econ. 244 (2022) 1–15. [Google Scholar]
  • Y. Ding, M. Zhang, S. Chen and R. Nie, Assessing the resilience of China’s natural gas importation under network disruptions. Energy 211 (2020) 1–14. [Google Scholar]
  • W.-B. Du, X.-L. Zhou, O. Lordan, Z. Wang, C. Zhao and Y.B. Zhu, Analysis of the Chinese airline network as multi-layer networks. Transp. Res. E: Logist. Transp. Rev. 89 (2016) 108–116. [CrossRef] [Google Scholar]
  • C.F. Durach, P. Maria Jesus Saenz, D. Xenophon Koufteros, Antecedents and dimensions of supply chain robustness: a systematic literature review. Int. J. Phys. Distrib. Logist. Manag. 45 (2015) 118–137. [CrossRef] [Google Scholar]
  • J. El Baz and S. Ruel, Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. Int. J. Prod. Econ. 233 (2021) 107972. [CrossRef] [Google Scholar]
  • Y. Gao and X.-L. Tian, Prefabrication policies and the performance of construction industry in China. J. Clean. Prod. 253 (2020) 1–13. [Google Scholar]
  • C. Gao, Y. Fan, S. Jiang, Y. Deng, J. Liu and X. Li, Dynamic robustness analysis of a two-layer rail transit network model. IEEE Trans. Intell. Transp. Syst. 23 (2022) 6509–6524. [CrossRef] [Google Scholar]
  • L. Geng, R. Xiao and S. Xie, Research on self-organization in resilient recovery of cluster supply chains. Discrete Dyn. Nature Soc. 2013 (2013) 1–11. [CrossRef] [Google Scholar]
  • J. Gheidar-kheljani and K. Halat, Developing a resilient supply chain in complex product systems through investment in reliability and cooperative contracts. RAIRO:RO 58 (2024) 79–102. [CrossRef] [EDP Sciences] [Google Scholar]
  • N. Guo, P. Guo, H. Dong, J. Zhao and Q. Han, Modeling and analysis of cascading failures in projects: A complex network approach. Comput. Ind. Eng. 127 (2019) 1–7. [CrossRef] [Google Scholar]
  • X. Guo, Q. Du, Y. Li, Y. Zhou, Y. Wang, Y. Huang and B. Martinez-Pastor, Cascading failure and recovery of metro–bus double-layer network considering recovery propagation. Transp. Res. D: Transp. Environ. 122 (2023) 103861. [CrossRef] [Google Scholar]
  • J. Han and K. Shin, Evaluation mechanism for structural robustness of supply chain considering disruption propagation. Int. J. Prod. Res. 54 (2015) 135–151. [Google Scholar]
  • S.H. Hashemi, A. Karimi and M. Tavana, An integrated green supplier selection approach with analytic network process and improved Grey relational analysis. Int. J. Prod. Econ. 159 (2015) 178–191. [CrossRef] [Google Scholar]
  • E.J.S. Hearnshaw and M.M.J. Wilson, A complex network approach to supply chain network theory. Int. J. Oper. Prod. Manag. 33 (2013) 442–469. [CrossRef] [Google Scholar]
  • W. Huang, B. Zhou, Y. Yu, H. Sun and P. Xu, Using the disaster spreading theory to analyze the cascading failure of urban rail transit network. Reliab. Eng. Syst. Saf. 215 (2021) 1–10. [Google Scholar]
  • Y. Li and C.W. Zobel, Exploring supply chain network resilience in the presence of the ripple effect. Int. J. Prod. Econ. 228 (2020) 1–40. [Google Scholar]
  • J. Li, X. Zeng, C. Liu and X. Zhou, A parallel Lagrange algorithm for order acceptance and scheduling in cluster supply chains. Knowl.-Based Syst. 143 (2018) 271–283. [Google Scholar]
  • C.Z. Li, Z. Chen, F. Xue, X.T. Kong, B. Xiao, X. Lai and Y. Zhao, A blockchain- and IoT-based smart product-service system for the sustainability of prefabricated housing construction. J. Clean. Prod. 286 (2021) 1–17. [Google Scholar]
  • X. Li, C. Wang, M.A. Kassem, H.H. Alhajlah and S. Bimenyimana, Evaluation method for quality risks of safety in prefabricated building construction using SEM-SDM ppproach. Int. J. Environ. Res. Public Health 19 (2022) 1–17. [Google Scholar]
  • H. Liu, X. Chen, L. Huo, Y. Zhang and C. Niu, Impact of inter-network assortativity on robustness against cascading failures in cyber–physical power systems. Reliab. Eng. Syst. Saf. 217 (2022) 1–13. [Google Scholar]
  • H. Liu, Y. Han and A. Zhu, Modeling supply chain viability and adaptation against underload cascading failure during the COVID-19 pandemic. Nonlinear Dyn. 110 (2022) 2931–2947. [CrossRef] [PubMed] [Google Scholar]
  • O. Lordan, J.M. Sallan, N. Escorihuela and D. Gonzalez-Prieto, Robustness of airline route networks. Phys. A: Stat. Mech. Appl. 445 (2016) 18–26. [CrossRef] [Google Scholar]
  • Y. Lou, R. Wu, J. Li, L. Wang and G. Chen, A convolutional neural network approach to predicting network connectedness robustness. IEEE Trans. Netw. Sci. Eng. 8 (2021) 3209–3219. [CrossRef] [Google Scholar]
  • P.J. Macdonald, E. Almaas and A.-L. Barabasi, Minimum spanning trees of weighted scale-free networks. Europhys. Lett. 72 (2005) 308–314. [CrossRef] [Google Scholar]
  • J. Monostori, Mitigation of the ripple effect in supply chains: Balancing the aspects of robustness, complexity and efficiency. CIRP J. Manuf. Sci. Technol. 32 (2021) 370–381. [CrossRef] [Google Scholar]
  • S. Navaratnam, A. Satheeskumar, G. Zhang, K. Nguyen, S. Venkatesan and K. Poologanathan, The challenges confronting the growth of sustainable prefabricated building construction in Australia: Construction industry views. J. Build. Eng. 48 (2022) 1–15. [Google Scholar]
  • M. Piraveenan, H. Jing, P. Matous and Y. Todo, Topology of international supply chain networks: A case study using factset revere datasets. IEEE Access 8 (2020) 154540–154559. [CrossRef] [Google Scholar]
  • X. Shi, D. Deng, W. Long, Y. Li and X. Yu, Research on the robustness of interdependent supply networks with tunable parameters. Comput. Ind. Eng. 158 (2021) 1–19. [Google Scholar]
  • J. Sun, J. Tang, W. Fu, Z. Chen and Y. Niu, Construction of a multi-echelon supply chain complex network evolution model and robustness analysis of cascading failure. Comput. Ind. Eng. 144 (2020) 1–16. [Google Scholar]
  • G. Sun, C.-C. Chen and S. Bin, Study of cascading failure in multisubnet composite complex networks. Symmetry 13 (2021) 1–14. [Google Scholar]
  • L. Tang, K. Jing, J. He and H.E. Stanley, Complex interdependent supply chain networks: Cascading failure and robustness. Phys. A: Stat. Mech. Appl. 443 (2016) 58–69. [CrossRef] [Google Scholar]
  • L. Tang, K. Jing, J. He and H.E. Stanley, Robustness of assembly supply chain networks by considering risk propagation and cascading failure. Phys. A: Stat. Mech. Appl. 459 (2016) 129–139. [CrossRef] [Google Scholar]
  • S. Wandelt, X. Sun, M. Zanin and S. Havlin, QRE: Quick robustness estimation for large complex networks. Future Gener. Comput. Syst. 83 (2018) 413–424. [CrossRef] [Google Scholar]
  • Y. Wang and R. Xiao, An ant colony based resilience approach to cascading failures in cluster supply network. Phys. A: Stat. Mech. Appl. 462 (2016) 150–166. [CrossRef] [Google Scholar]
  • Y. Wang and F. Zhang, Modeling and analysis of under-load-based cascading failures in supply chain networks. Nonlinear Dyn. 92 (2018) 1403–1417. [CrossRef] [Google Scholar]
  • R. Wiedmer and S.E. Griffis, Structural characteristics of complex supply chain networks. J. Bus. Logist. 42 (2021) 264–290. [CrossRef] [Google Scholar]
  • Y. Wu, Z. Chen, X. Zhao, Y. Liu, P. Zhang and Y. Liu, Robust analysis of cascading failures in complex networks. Phys. A: Stat. Mech. Appl. 583 (2021) 1–10. [Google Scholar]
  • X. Xue, X. Li, Q. Shen and Y. Wang, An agent-based framework for supply chain coordination in construction. Autom. Constr. 14 (2005) 413–430. [CrossRef] [Google Scholar]
  • X. Yan and H. Zhang, Computer vision–based disruption management for prefabricated building construction schedule. J. Comput. Civ. Eng. 35 (2021) 1–19. [Google Scholar]
  • Q. Yang, C.M. Scoglio and D.M. Gruenbacher, Robustness of supply chain networks against underload cascading failures. Phys. A: Stat. Mech. Appl. 563 (2021). [Google Scholar]
  • Q. Yang, C.M. Scoglio and D.M. Gruenbacher, Robustness of supply chain networks against underload cascading failures. Phys. A: Stat. Mech. Appl. 563 (2021) 1–16. [Google Scholar]
  • Y. Zeng and R. Xiao, Modelling of cluster supply network with cascading failure spread and its vulnerability analysis. Int. J. Prod. Res. 52 (2014) 6938–6953. [CrossRef] [Google Scholar]
  • G. Zhang, J. Shi, S. Huang, J. Wang and H. Jiang, A cascading failure model considering operation characteristics of the communication layer. IEEE Access 9 (2021) 9493–9504. [CrossRef] [Google Scholar]
  • K. Zhao, K. Scheibe, J. Blackhurst and A. Kumar, Supply chain network robustness against disruptions: Topological analysis, measurement, and optimization. IEEE Trans. Eng. Manag. 66 (2019) 127–139. [Google Scholar]
  • K. Zhao, Z. Zuo and J.V. Blackhurst, Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach. J. Oper. Manag. 65 (2019) 190–212. [CrossRef] [Google Scholar]
  • P. Zhao, Z. Li, X. Han and X. Duan, Supply chain network resilience by considering disruption propagation: Topological and operational perspectives. IEEE Syst. J. (2022) 1–20. [Google Scholar]
  • Y. Zhou and J. Wang, Efficiency of complex networks under failures and attacks: A percolation approach. Phys. A: Stat. Mech. Appl. 512 (2018) 658–664. [CrossRef] [Google Scholar]
  • H. Zhou, X. Zhang and Y. Hu, Robustness of open source product innovation community’s knowledge collaboration network under the dynamic environment. Phys. A: Stat. Mech. Appl. 540 (2020) 1–34. [Google Scholar]
  • J. Zhou, D.W. Coit, F.A. Felder and D. Wang, Resiliency-based restoration optimization for dependent network systems against cascading failures. Reliab. Eng. Syst. Saf. 207 (2021) 1–18. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.