Enhancement Strategy for Port Resilience: Shipping Route Optimization Methods Based on Network Characteristics of Ports

被引:0
|
作者
Yuan, Xiang [1 ]
He, Xinhao [1 ]
机构
[1] Shanghai Maritime Univ, Sch Econ & Management, Shanghai 201306, Peoples R China
关键词
shipping route network; port resilience; link prediction; optimization; shipping network; network characteristics; LINK PREDICTION; VULNERABILITY;
D O I
10.3390/jmse13020325
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Ports and their affiliated shipping routes are fundamental to the maritime logistics system, crucial for global trade. However, they face risks from natural disasters and human-induced crises. Enhancing port resilience, the ability to quickly recover and maintain operations during disruptions is vital for a robust maritime network. This study focuses on enhancing port resilience by improving the shipping route network, using an innovative link-prediction-based approach. Initially, a multi-dimensional resilience analysis is conducted to identify key low-resilience and bottleneck ports, guiding targeted network optimizations. Then, a novel link prediction algorithm is applied to find potential new shipping connections, significantly enhancing network efficiency, robustness, and port resilience. The optimized network effectively improves the connectivity of critical low-resilience ports with central hub ports and bottleneck ports with surrounding ones. Route diversification mitigates risks and strengthens overall resilience. Key low-resilience ports and bottleneck ports are reduced by an average of 20% and 25%. Finally, practical strategies are proposed. Low-resilience ports should establish direct connections with major hubs, and regional sub-networks can offer support. For bottleneck ports, additional secondary and short distance links should be added to transform them into more integrated hubs, enhancing the network's robustness. These strategies improve the network's operational capacity during crises, ensuring efficient cargo flow.
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页数:34
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