Multi-objective resilience-oriented optimisation for the global container shipping network against cascading failures

• A new multi-objective stepwise optimisation framework for proactive resilience maintenance against cascading failures in the GCSN. • Solution to complex cargo redistribution in the GCSN concerning the economic efficiency, functionality, and structural completeness. • New load redistribution strategies for the GCSN to mitigate the cascading effects when being disrupted. • Extensive simulations and case studies for model validation and practical insights across 21 major global ports. Disruptive events at ports (e.g., epidemics, natural hazards and regional conflicts) continuously challenge the stability of cargo flows, leading to cascading failures that significantly undermine the resilience of global shipping networks. To address these challenges, this study proposes a new Multi-objective Stepwise Optimisation (MSO) framework that can aid decision-makers in maintaining resilience against cascading failures. Specifically, this study first formulates multiple objectives aimed at minimising adverse impacts on maritime stakeholders by reducing transit time, alleviating port overload, and preserving the network’s structural completeness. Then, to explore ideal load redistribution strategies mitigating the cascading effects, a Stepwise Cascading Mitigation (SCM) model is newly developed. In this model, all feasible target ports are identified, followed by an iterative algorithm applied to determine the equilibrium volumes of load redistributed to each target. An evolutionary procedure is then designed to ensure renewal of diverse solutions and reduce computational complexity. By simulating the entire cascading process, multi-dimensional reductions in shipping network resilience are eventually assessed. Taking the Global Container Shipping Network (GCSN) as a case study, comprehensive experiments, alongside targeted analyses of major international ports, are conducted to validate the effectiveness and superiority of the MSO framework over four benchmarking methods. Sensitivity analysis results further reveal that maintaining appropriate redundancy across the network, combined with the proposed optimal redistribution strategies, can effectively mitigate the adverse impacts of cascading failures on system resilience. Therefore, this study provides stakeholders with adaptable emergency response protocols to alleviate excessive congestion at critical ports, ensuring the timely and reliable movement of goods, thereby proactively protecting the overall robustness and resilience of global supply chains.

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Publication Details

Journal
Transportation Research Part A Policy and Practice
Published
2025-08-29
DOI
https://doi.org/10.1016/j.tra.2025.104659
Citations
5
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
11.13
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article

Multi-objective resilience-oriented optimisation for the global container shipping network against cascading failures

Jin Wang, Xinjian Wang, Zaili L. Yang, Yuhao Cao et al.
5 citations
Transportation Research Part A Policy and Practice
Supply Chain Resilience and Risk Management
11.13
article

Multi-objective resilience-oriented optimisation for the global container shipping network against cascading failures

Jin Wang, Xinjian Wang, Zaili L. Yang, Yuhao Cao, Xuri Xin
article en
5 citations

Abstract

• A new multi-objective stepwise optimisation framework for proactive resilience maintenance against cascading failures in the GCSN. • Solution to complex cargo redistribution in the GCSN concerning the economic efficiency, functionality, and structural completeness. • New load redistribution strategies for the GCSN to mitigate the cascading effects when being disrupted. • Extensive simulations and case studies for model validation and practical insights across 21 major global ports. Disruptive events at ports (e.g., epidemics, natural hazards and regional conflicts) continuously challenge the stability of cargo flows, leading to cascading failures that significantly undermine the resilience of global shipping networks. To address these challenges, this study proposes a new Multi-objective Stepwise Optimisation (MSO) framework that can aid decision-makers in maintaining resilience against cascading failures. Specifically, this study first formulates multiple objectives aimed at minimising adverse impacts on maritime stakeholders by reducing transit time, alleviating port overload, and preserving the network’s structural completeness. Then, to explore ideal load redistribution strategies mitigating the cascading effects, a Stepwise Cascading Mitigation (SCM) model is newly developed. In this model, all feasible target ports are identified, followed by an iterative algorithm applied to determine the equilibrium volumes of load redistributed to each target. An evolutionary procedure is then designed to ensure renewal of diverse solutions and reduce computational complexity. By simulating the entire cascading process, multi-dimensional reductions in shipping network resilience are eventually assessed. Taking the Global Container Shipping Network (GCSN) as a case study, comprehensive experiments, alongside targeted analyses of major international ports, are conducted to validate the effectiveness and superiority of the MSO framework over four benchmarking methods. Sensitivity analysis results further reveal that maintaining appropriate redundancy across the network, combined with the proposed optimal redistribution strategies, can effectively mitigate the adverse impacts of cascading failures on system resilience. Therefore, this study provides stakeholders with adaptable emergency response protocols to alleviate excessive congestion at critical ports, ensuring the timely and reliable movement of goods, thereby proactively protecting the overall robustness and resilience of global supply chains.

Transportation Research Part A Policy and PracticeVol. 200
Dalian Maritime University (CN), Liverpool John Moores University (GB)
Openalex Percentile: Top 2%
Supply Chain Resilience and Risk Management
11.13
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