Optimizing port system resilience through integrated preparedness and recovery strategies

Ports, recognized as intricate systems, are susceptible to a variety of human-induced incidents and natural phenomena that can result in disruptions. Strengthening the port’s ability to manage disruptions and bolstering the resilience of the port system play a crucial role in ensuring the smooth ope...

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Veröffentlicht in:Reliability engineering & system safety Jg. 266; S. 111770
Hauptverfasser: Xing, Zheng, Zhou, Chenhao, Shen, Yu, Chew, Ek Peng, Tan, Kok Choon
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.02.2026
Schlagworte:
Double-decomposed methodology
Port resilience
Preparedness and recovery actions
Stochastic mixed-integer linear programming
Double-decomposed methodology
Preparedness and recovery actions
Stochastic mixed-integer linear programming
Port resilience
ISSN:0951-8320
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Zusammenfassung:Ports, recognized as intricate systems, are susceptible to a variety of human-induced incidents and natural phenomena that can result in disruptions. Strengthening the port’s ability to manage disruptions and bolstering the resilience of the port system play a crucial role in ensuring the smooth operation of commercial trade. Nevertheless, assessing the port’s resilience and making decisions regarding pre- and post-disruption actions in uncertain circumstances present notable challenges. This research delves into the topic of network resilience within port logistics and operational infrastructure, introducing a novel indicator for evaluating port resilience. Moreover, the study frames this issue as a stochastic mixed-integer linear programming (SMILP), determining preparedness and recovery measures to enhance the resilience of the port system. Subsequently, a double-decomposed methodology is suggested for resolving the model, which incorporates Lagrangian Decomposition and a branch-and-price algorithm utilizing Dantzig–Wolfe Decomposition. Ultimately, the efficacy of the algorithm and the significance of the strategies in risk management are validated through a practical case study.
ISSN:0951-8320
DOI:10.1016/j.ress.2025.111770