Operational resilience modeling of cross-border freight railway systems: A study of strategies to improve proactive and reactive capabilities

Haiwen Li, Lin Qi, Mudan Wang and Junying Liu

Reliability Engineering and System Safety, 2025, vol. 257, issue PA

Abstract: Cross-border freight railway systems (CBFRSs) are increasingly vulnerable to disturbances created by volatile international situations and worsening climate conditions. This study aims to develop measures for enhancing the operational resilience of CBFRSs against risks and uncertainties. A novel four-stage framework for studying the operational resilience of CBFRSs is proposed. Major disruptions affecting CBFRSs are identified, and resilience-enhancing strategies are presented. A hierarchical structure is then designed to divide operational resilience into proactive and reactive capabilities, using different metrics to categorize strategies for assessing these two capabilities. An evidence fusion–fuzzy Bayesian network model is proposed, employing triangular fuzzy numbers and Dempster–Shafer evidence theory to derive prior probabilities for disruptions and strategies, and conditional probabilities for disruption categories and resilience metrics. The actual train capacity, lost train capacity, and restored train capacity are introduced to quantify the operational resilience of CBFRSs. Using bidirectional reasoning and sensitivity analysis, the developed model simulates the evolution of operational resilience under multiple disruptions and various strategies, identifying the key strategies and pathways for enhancing resilience. A case study shows that enhancing resilience requires a holistic consideration of capabilities, metrics, and strategies, offering actionable insights for decision-making regarding the allocation of resilience measures in CBFRSs.

Keywords: Cross-border freight railway systems (CBFRSs); Operational resilience; Disruptions; Resilience strategies; Evidence fusion–fuzzy Bayesian network (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:257:y:2025:i:pa:s0951832025000596

DOI: 10.1016/j.ress.2025.110856

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