Open Access
Issue
RAIRO-Oper. Res.
Volume 59, Number 4, July-August 2025
Page(s) 1841 - 1864
DOI https://doi.org/10.1051/ro/2025077
Published online 23 July 2025
  • A. Ghadge, S. Dani and R. Kalawsky, Supply chain risk management: present and future scope. Int. J. Logistics Manage. 23 (2012) 313–339. [Google Scholar]
  • K.-M. Lee and K.-I. Goh, Strength of weak layers in cascading failures on multiplex networks: case of the international trade network. Sci. Rep. 6 (2016) 26346. [Google Scholar]
  • A. Świerczek, The impact of supply chain integration on the “snowball effect” in the transmission of disruptions: an empirical evaluation of the model. Int. J. Prod. Econ. 157 (2014) 89–104. [Google Scholar]
  • H. Aslam, T.A. Syed, C. Blome, A. Ramish and K. Ayaz, The multifaceted role of social capital for achieving organizational ambidexterity and supply chain resilience. IEEE Trans. Eng. Manage. 71 (2024) 10571–10584. [Google Scholar]
  • S. Fan, Z. Yang, J. Wang and J. Marsland, Shipping accident analysis in restricted waters: lesson from the Suez Canal blockage in 2021. Ocean Eng. 266 (2022) 113119. [Google Scholar]
  • V.F. Yu, A. Bera, S.K. Das, S. Manna, P.K. Jhulki, B. Dey and S.K.A. Ali, Optimizing green solid transportation with carbon cap and trade: a multi-objective two-stage approach in a type-2 pythagorean fuzzy context. Soft Comput. 28 (2024) 11015–11039. [Google Scholar]
  • S. K. Das and S.K. Roy, Effect of variable carbon emission in a multi-objective transportation-p-facility location problem under neutrosophic environment. Comput. Ind. Eng. 132 (2019) 311–324. [CrossRef] [Google Scholar]
  • S.K. Das and S.K. Roy, An approximation approach for fixed-charge transportation-p-facility location problem, in Logistics and Supply Chain Management, edited by Z. Molamohamadi, E. Babaee Tirkolaee, A. Mirzazadeh and G.-W. Weber. Springer International Publishing (2020) 219–237. [Google Scholar]
  • S.K. Das, S.K. Roy and G.-W. Weber, The Impact of Carbon Tax Policy in a Multi-Objective Green Solid Logistics Modelling Under Sustainable Development. Springer Nature Singapore, Singapore (2022) 49–66. [Google Scholar]
  • S.K. Das, V.F. Yu, S.K. Roy and G.W. Weber, Location–allocation problem for green efficient two-stage vehicle-based logistics system: a type-2 neutrosophic multi-objective modeling approach. Expert Syst. App. 238 (2024) 122174. [Google Scholar]
  • S. Bhunia, S.K. Das, J. Jablonsky and S.K. Roy, Evaluating carbon cap and trade policy effects on a multi-period bi-objective closed-loop supply chain in retail management under mixed uncertainty: towards greener horizons. Expert Syst. App. 250 (2024) 123889. [CrossRef] [Google Scholar]
  • W. Zou, C.K. Ahn and Z. Xiang, Fuzzy-approximation-based distributed fault-tolerant consensus for heterogeneous switched nonlinear multiagent systems. IEEE Trans. Fuzzy Syst. 29 (2021) 2916–2925. [Google Scholar]
  • A.E. Motter and Y.-C. Lai, Cascade-based attacks on complex networks. Phys. Rev. E 66 (2002) 065102. [Google Scholar]
  • K. Bimpikis, O. Candogan and S. Ehsani, Supply disruptions and optimal network structures. Manage. Sci. 65 (2019) 5504–5517. [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) 108373. [CrossRef] [Google Scholar]
  • W. Klibi and A. Martel, Modeling approaches for the design of resilient supply networks under disruptions. Int. J. Prod. Econ. 135 (2011) 882–898. [Google Scholar]
  • M. Lu, L. Ran and Z.-J.M. Shen, Reliable facility location design under uncertain correlated disruptions. Manuf. Serv. Oper. Manage. 17 (2015) 445–455. [CrossRef] [MathSciNet] [Google Scholar]
  • D.J. Watts and S.H. Strogatz, Collective dynamics of “small-world” networks. Nature 393 (1998) 440–442. [CrossRef] [Google Scholar]
  • A.L. Barabasi and R. Albert, Emergence of scaling in random networks. Science 286 (1999) 509–512. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • A. Bombelli, B.F. Santos and L. Tavasszy, Analysis of the air cargo transport network using a complex network theory perspective. Transp. Res. Part E-Logistics Transp. Rev. 138 (2020) 101959. [Google Scholar]
  • R. Wiedmer and S.E. Griffis, Structural characteristics of complex supply chain networks. J. Bus. Logistics 42 (2021) 264–290. [Google Scholar]
  • R. Albert, H. Jeong and A.L. Barabasi, Error and attack tolerance of complex networks. Nature 406 (2000) 378–382. [NASA ADS] [CrossRef] [Google Scholar]
  • L. Zhao, K. Park and Y.-C. Lai, Attack vulnerability of scale-free networks due to cascading breakdown. Phys. Rev. E 70 (2004) 035101. [Google Scholar]
  • N. Goldbeck, P. Angeloudis and W. Ochieng, Optimal supply chain resilience with consideration of failure propagation and repair logistics. Transp. Res. Part E-Logistics Transp. Rev. 133 (2020) 101830. [Google Scholar]
  • W. Liao, S. Salinas, M. Li, P. Li and K.A. Loparo, Cascading failure attacks in the power system: a stochastic game perspective. IEEE Int. Things J. 4 (2017) 2247–2259. [Google Scholar]
  • W. Ren, J. Wu, X. Zhang, R. Lai and L. Chen, A stochastic model of cascading failure dynamics in communication networks. IEEE Trans. Circuits Syst. II: Express Briefs 65 (2018) 632–636. [Google Scholar]
  • J. Wang, E. Sun, B. Xu, P. Li and C. Ni, Abnormal cascading failure spreading on complex networks. Chaos Solitons Fractals 91 (2016) 695–701. [Google Scholar]
  • X. Yin and J. Wu, Simulation study on topology characteristics and cascading failure of Hefei subway network. Sustainability 15 (2022) 422. [Google Scholar]
  • L. Zhang, J. Lu, B.-B. Fu and S.-B. Li, A cascading failures model of weighted bus transit route network under route failure perspective considering link prediction effect. Phys. A: Stat. Mech. App. 523 (2019) 1315–1330. [Google Scholar]
  • A.A. Ganin, M. Kitsak, D. Marchese, J.M. Keisler, T. Seager and I. Linkov, Resilience and efficiency in transportation networks. Sci. Adv. 3 (2017) e1701079. [Google Scholar]
  • A. Huang, H.M. Zhang, W. Guan, Y. Yang and G. Zong, Cascading failures in weighted complex networks of transit systems based on coupled map lattices. Math. Prob. Eng. 2015 (2015) 1–16. [Google Scholar]
  • L. Sun, Y. Huang, Y. Chen and L. Yao, Vulnerability assessment of urban rail transit based on multi-static weighted method in Beijing, China. Transp. Rese. Part A-Policy Pract. 108 (2018) 12–24. [Google Scholar]
  • M. Castells, Grassrooting the space of flows. Urban Geogr. 20 (1999) 294–302. [Google Scholar]
  • J. Li, J. Qian, Y. Liu and W. Kainz, A novel analysis method of geographical centrality based on space of flows. ISPRS Int. J. Geo-Inf. 6 (2017) 153. [Google Scholar]
  • P.J. Taylor, M. Hoyler and R. Verbruggen, External urban relational process: introducing central flow theory to complement central place theory. Urban Stud. 47 (2010) 2803–2818. [Google Scholar]
  • N. Green, Functional polycentricity: a formal definition in terms of social network analysis. Urban Stud. 44 (2007) 2077–2103. [Google Scholar]
  • P.J. Taylor, World City Network: A Global Urban Analysis. Psychology Press (2004). [Google Scholar]
  • P.G. Hall and K. Pain, The Polycentric Metropolis: Learning from Mega-City Regions in Europe. Routledge (2006). [Google Scholar]
  • D. Perrotti and O. Iuorio, Green infrastructure in the space of flows: an urban metabolism approach to bridge environmental performance and user’s wellbeing, in Planning Cities with Nature: Theories, Strategies and Methods. Springer (2019) 265–277. [Google Scholar]
  • K. Pain and P. Hall, Informational quantity versus informational quality: the perils of navigating the space of flows. Regional Stud. 42 (2008) 1065–1077. [Google Scholar]
  • Y. Zhang, H. Long, L. Ma, S. Tu, Y. Li and D. Ge, Analysis of rural economic restructuring driven by e-commerce based on the space of flows: the case of Xiaying village in Central China. J. Rural Stud. 93 (2022) 196–209. [CrossRef] [Google Scholar]
  • D. Xu, J.-H. Zhang, Z. Huang, Y. Zhou and Q. Fan, Tourism community detection: a space of flows perspective. Tourism Manage. 93 (2022) 104577. [Google Scholar]
  • L. Inostroza and H. Zepp, The metabolic urban network: urbanisation as hierarchically ordered space of flows. Cities 109 (2020) 103029. [Google Scholar]
  • J. Reades and D.A. Smith, Mapping the “space of flows”: the geography of global business telecommunications and employment specialization in the London mega-city-region. Regional Stud. 48 (2014) 105–126. [Google Scholar]
  • G.R. Aung, The frontier in heterogeneous time: finance, temporality, and an economic zone on hold. J. Cultural Econ. 16 (2023) 377–391. [Google Scholar]
  • M. Castells, Materials for an exploratory theory of the network society. Br. J. Soc. 51 (2000) 5–24. [Google Scholar]
  • M. Akhavan, H. Ghiara, I. Mariotti and C. Sillig, Logistics global network connectivity and its determinants. A European city network analysis. J. Transp. Geogr. 82 (2020) 102624. [Google Scholar]
  • A. Deshmukh and D.-W. Song, Probing into hinterland connectivity with a web of transport modes and logistics nodes: a case of Indian container ports. Transp. Res. Part A: Policy Pract. 189 (2024) 104200. [Google Scholar]
  • R. Lock, Y. Benavente, G. Gatica, P. Olivares, J. Ramirez and A. Gonzalez-Holgado, Modeling hospital logistics capacity through system dynamics during the COVID-19 pandemic: case of Pasco Healthcare Network in Peru. Proc. Comput. Sci. 238 (2024) 1042–1047. [Google Scholar]
  • R. Matsuyama, Y. Sugimura, R. Shibasaki and T.T.T. Tran, Scenario analysis on CO2 emission reductions in hinterland transport of Japan through intermodal logistics network simulation. J. Clean. Prod. 458 (2024) 142503. [Google Scholar]
  • Y. Lu, Q. Wang, S. Huang, W. Yu and S. Yao, Resilience quantification and recovery strategy simulation for urban underground logistics systems under node and link attacks: a case study of Nanjing City. Int. J. Critical Infrastruct. Prot. 47 (2024) 100704. [Google Scholar]
  • Y. Gu, X. Fu, Z. Liu, X. Xu and A. Chen, Performance of transportation network under perturbations: reliability, vulnerability, and resilience. Transp. Res. Part E 133 (2019) 101809. [Google Scholar]
  • R. Sreedevi and H. Saranga, Uncertainty and supply chain risk: the moderating role of supply chain flexibility in risk mitigation. Int. J. Prod. Econ. 193 (2017) 332–342. [Google Scholar]
  • Y. Yang, T. Nishikawa and A.E. Motter, Small vulnerable sets determine large network cascades in power grids. Science 358 (2017) eaan3184. [Google Scholar]
  • Y. Li and M. Zhang, Cascading failure analysis of interdependent water-power networks based on functional coupling. Reliab. Eng. Syst. Saf. 259 (2025) 110950. [Google Scholar]
  • J. Du, J. Cui, G. Ren, R.G. Thompson and L. Zhang, Cascading failures and resilience evolution in urban road traffic networks with bounded rational route choice. Phys. A: Stat. Mech. App. 664 (2025) 130456. [Google Scholar]
  • B. Li, C. Liu, Y. Yin, Q. Jiang, Y. Zhang and T. Liu, Study on power system resilience assessment considering cascading failures during wildfire disasters. Energy Rep. 13 (2025) 1819–1833. [Google Scholar]
  • J. Li, Q.-C. Lu, P.-C. Xu, L. Liu and S. Wang, Critical station identification for cascading failure mitigation considering the Lyapunov-stability of metro stations. Reliab. Eng. Syst. Saf. 256 (2025) 110772. [Google Scholar]
  • H.M.I. Kays, A.M. Sadri, K.K. Muralee” Muraleetharan, P.S. Harvey and G.A. Miller, Modeling flood propagation and cascading failures in interdependent transportation and stormwater networks. Int. J. Crit. Infrastruct. Prot. 48 (2025) 100741. [Google Scholar]
  • S. Soleimani, A. Afshar and H. Atrianfar, Critical component analysis of cyber-physical power systems in cascading failures using graph convolutional networks: an energy-based approach. Sustainable Energy Grids Netw. 42 (2025) 101653. [Google Scholar]
  • H. Liu, Y. Cheng, Y. Zhang, L. Zhou and F. Tao, Tolerance strategies for cascading failures in platform-aggregated manufacturing service collaboration. Adv. Eng. Inf. 64 (2025) 103000. [Google Scholar]
  • M. Zhang, X. Liao, Y. Fu, X. Gong and Y. Xu, Research on cascading failure based on high-order neighbors and residual capacities load redistribution process. Chaos Solitons Fractals 193 (2025) 116059. [Google Scholar]
  • X. Guo, Q. Du, Y. Li, X. Zong and L. Bai, Cascading failure and recovery propagation of metro-bus double-layer network under time-varying passengers. Transp. Res. Part D: Transp. Environ. 139 (2025) 104571. [Google Scholar]
  • H. Dui, J. Zhai and X. Fu, Attack strategies and reliability analysis of wireless mesh networks considering cascading failures. Reliab. Eng. Syst. Saf. 257 (2025) 110832. [Google Scholar]
  • Y. Cao, X. Xin, P. Jarumaneeroj, H. Li, Y. Feng, J. Wang, X. Wang, R. Pyne and Z. Yang, Data-driven resilience analysis of the global container shipping network against two cascading failures. Transp. Res. Part E: Logistics Transp. Rev. 193 (2025) 103857. [Google Scholar]
  • C.W. Craighead, J. Blackhurst, M.J. Rungtusanatham and R. Handfield, The severity of supply chain disruptions: design characteristics and mitigation capabilities. Decis. Sci. 38 (2007) 131–156. [CrossRef] [Google Scholar]
  • S. Wen, J. Jiang, B. Liu, Y. Xiang and W. Zhou, Using epidemic betweenness to measure the influence of users in complex networks. J. Netw. Comput. App. 78 (2017) 288–299. [Google Scholar]
  • R. Ghanbari, M. Jalili and X. Yu, Correlation of cascade failures and centrality measures in complex networks. Future Gener. Comput. Syst. 83 (2018) 390–400. [Google Scholar]
  • A. Falegnami, F. Costantino, G.D. Gravio and R. Patriarca, Unveil key functions in socio-technical systems: mapping fram into a multilayer network. Cognition Technol. Work 22 (2019) 1–23. [Google Scholar]
  • C. Fu, Y. Gao, J. Zhong, Y. Sun, P. Zhang and T. Wu, Attack-defense game for critical infrastructure considering the cascade effect. Reliab. Eng. Syst. Saf. 216 (2021) 107958. [Google Scholar]
  • X.-Y. Zhou, G. Lu, Z. Xu, X. Yan, S.-T. Khu, J. Yang and J. Zhao, Influence of Russia–Ukraine war on the global energy and food security. Res. Conserv. Recycling 188 (2023) 106657. [Google Scholar]
  • V. Latora and M. Marchiori, Efficient behavior of small-world networks. Phys. Rev. Lett. 87 (2001) 198701. [Google Scholar]
  • M. Li, Y. Fan, J. Chen, L. Gao, Z. Di and J. Wu, Weighted networks of scientific communication: the measurement and topological role of weight. Phys. A: Stat. Mech. App. 350 (2005) 643–656. [Google Scholar]
  • X. Wang, H. Li, H. Yao, D. Zhu and N. Liu, Simulation analysis of the spread of a supply crisis based on the global natural graphite trade network. Res. Policy 59 (2018) 200–209. [Google Scholar]

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