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All issues Volume 680 (2025) E3S Web Conf., 680 (2025) 00053 References
Open Access
Issue
E3S Web Conf.
Volume 680, 2025
The 4th International Conference on Energy and Green Computing (ICEGC’2025)
Article Number 00053
Number of page(s) 12
DOI https://doi.org/10.1051/e3sconf/202568000053
Published online 19 December 2025
  1. Q. Du, K. Kishi, N. Aiura, and T. Nakatsuji, “Transportation network vulnerability,” Transp Res Rec, vol. 2410, pp. 96–104, Dec. 2014, doi: 10.3141/2410-11. [Google Scholar]
  2. C. Y. Lam, M. Fuse, and T. Shimizu, “Assessment of risk factors and effects in hydrogen logistics incidents from a network modeling perspective,” Int J Hydrogen Energy, vol. 44, no. 36, pp. 20572–20586, Jul. 2019, doi: 10.1016/j.ijhydene.2019.05.187. [Google Scholar]
  3. I. Abdulrashid, R. Zanjirani Farahani, S. Mammadov, M. Khalafalla, and W. C. Chiang, “Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents,” Transp Res E Logist Transp Rev, vol. 186, Jun. 2024, doi: 10.1016/j.tre.2024.103563. [Google Scholar]
  4. S. Ahmed and K. Dey, “Resilience modeling concepts in transportation systems: a comprehensive review based on mode, and modeling techniques,” Dec. 01, 2020, Springer Nature. doi: 10.1186/s43065-020-00008-9. [Google Scholar]
  5. N. Bešinović, “Resilience in railway transport systems: a literature review and research agenda,” Transp Rev, vol. 40, no. 4, pp. 457–478, Jul. 2020, doi: 10.1080/01441647.2020.1728419. [Google Scholar]
  6. Mattsson, L. G., & Jenelius, E. (2015). Vulnerability and resilience of transport systems–A discussion of recent research. Transportation research part A: policy and practice, 81, 16-34. [CrossRef] [Google Scholar]
  7. D. V. Achillopoulou, S. A. Mitoulis, S. A. Argyroudis, and Y. Wang, “Monitoring of transport infrastructure exposed to multiple hazards: a roadmap for building resilience,” Dec. 01, 2020, Elsevier B.V. doi: 10.1016/j.scitotenv.2020.141001. [Google Scholar]
  8. M. Pregnolato and D. A. Dawson, “Adaptation investments for transport resilience: Trends and recommendations,” International Journal of Safety and Security Engineering, vol. 8, no. 4, pp. 515–527, 2018, doi: 10.2495/SAFE-V8-N4-515-527. [Google Scholar]
  9. S. K. Sharma and S. A. George, “Modelling resilience of truckload transportation industry,” Benchmarking, vol. 25, no. 7, pp. 2531–2545, Oct. 2018, doi: 10.1108/BIJ-07-2017-0188. [Google Scholar]
  10. W. Bi, J. Schooling, and K. MacAskill, “Assessing flood resilience of urban rail transit systems: Complex network modelling and stress testing in a case study of London,” Transp Res D Transp Environ, vol. 134, Sep. 2024, doi: 10.1016/j.trd.2024.104263. [Google Scholar]
  11. I. Rahimi, A. H. Gandomi, P. G. Asteris, and F. Chen, “Analysis and prediction of covid-19 using sir, seiqr and machine learning models: Australia, italy and uk cases,” Information (Switzerland), vol. 12, no. 3, pp. 1–25, Mar. 2021, doi: 10.3390/info12030109. [Google Scholar]
  12. A. Géhin, S. Goorah, K. Moheeput, and S. Ramchurn, “COVID-19 outbreak in Mauritius: Logistic growth and SEIR modelling with quarantine and an effective reproduction number,” Sep. 22, 2020. doi: 10.1101/2020.09.22.20199364. [Google Scholar]
  13. C. C. Chen, C. M. Feng, Y. H. Tsai, and P. J. Wu, “Modeling resilience enhancement strategies for international express logistics,” Transp Res Rec, no. 2378, pp. 92–98, Dec. 2013, doi: 10.3141/2378-10. [Google Scholar]
  14. J. Cao et al., “Optimization of three-echelon logistics supply chain considering emergency scenarios under resilience strategy: A case study in power metering industry,” Comput Ind Eng, vol. 203, May 2025, doi: 10.1016/j.cie.2025.110985. [Google Scholar]
  15. K. X. Li, H. Wang, Y. Yang, and M. Li, “Resilience in maritime logistics: Theoretical framework, research methodology, and indicator system,” Ocean Coast Manag, vol. 259, Dec. 2024, doi: 10.1016/j.ocecoaman.2024.107465. [Google Scholar]
  16. A. König and S. Spinler, “The effect of logistics outsourcing on the supply chain vulnerability of shippers: Development of a conceptual risk management framework,” International Journal of Logistics Management, vol. 27, no. 1, pp. 122–141, May 2016, doi: 10.1108/IJLM-03-2014-0043. [Google Scholar]
  17. B. Xu, W. Liu, J. Li, Y. Yang, F. Wen, and H. Song, “Resilience measurement and dynamic optimization of container logistics supply chain under adverse events,” Comput Ind Eng, vol. 180, Jun. 2023, doi: 10.1016/j.cie.2023.109202. [Google Scholar]
  18. Sanchez‐Rodrigues, V., Potter, A., & Naim, M. M. (2010). The impact of logistics uncertainty on sustainable transport operations. International Journal of Physical Distribution & Logistics Management, 40(1/2), 61-83. [Google Scholar]
  19. M. Wang, “Impacts of supply chain uncertainty and risk on the logistics performance,” Asia Pacific Journal of Marketing and Logistics, vol. 30, no. 3, pp. 689–704, 2018, doi: 10.1108/APJML-04-2017-0065. [Google Scholar]
  20. A. Lorenzo-Espejo, J. Muñuzuri, A. Pegado-Bardayo, and J. Guadix, “A framework for analyzing service disruptions in last-mile and first-mile reverse logistics,” Research in Transportation Economics, vol. 108, Dec. 2024, doi: 10.1016/j.retrec.2024.101485. [Google Scholar]
  21. S. Adumene et al., “Dynamic logistics disruption risk model for offshore supply vessel operations in Arctic waters,” Maritime Transport Research, vol. 2, Jan. 2021, doi: 10.1016/j.martra.2021.100039. [Google Scholar]
  22. Robust Maiyar, L. M., & Thakkar, J. J. (2020). Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions. International Journal of Production Research, 58(18), 5651-5675. [Google Scholar]
  23. M. Liu, J. Cao, J. Liang, and M. J. Chen, Epidemic-logistics modeling: A new perspective on operations research. Springer Singapore, 2019. doi: 10.1007/978-981-13-9353-2. [Google Scholar]
  24. C. Hou and H. Jiang, “Methodology of emergency medical logistics for multiple epidemic areas in public health emergency,” PLoS One, vol. 16, no. 7 July, Jul. 2021, doi: 10.1371/journal.pone.0253978. [Google Scholar]
  25. Z. Yaagoub, A. El Bhih, and K. Allali, “Global analysis of a fractional-order infection model for the propagation of computer viruses,” Model Earth Syst Environ, vol. 11, no. 1, Feb. 2025, doi: 10.1007/s40808-024-02270-2. [Google Scholar]
  26. M. Liu and D. Zhang, “A dynamic logistics model for medical resources allocation in an epidemic control with demand forecast updating,” Journal of the Operational Research Society, vol. 67, no. 6, pp. 841–852, Jun. 2016, doi: 10.1057/jors.2015.105. [Google Scholar]

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