Abstract
Liner shipping networks play a vital role in global and regional trade. However, they are susceptible to damage from unexpected interruptions, which can trigger dynamic cascading failures and undermine the system’s resilience. To address this challenge, we propose a novel cascading failure model for liner shipping networks that considers the characteristics of the network structure and port functions. First, we design two load redistribution methods that rely on network topology and employ a cooperative mechanism for coordination. This cooperative mechanism aims to balance the benefits for carriers and shippers by effectively controlling losses. Subsequently, we develop three metrics—network congestion rate, failure rate, and shipper loss—to assess the resilience of the network during cascading failures. To verify the impact of the cooperative mechanism, we apply the proposed methods to the China-Europe liner shipping network. Through simulations involving various port failures and resistance levels, we analyze the effectiveness of the cooperative mechanism. The results demonstrate that redistributing the load to downstream ports within the network effectively mitigates deep cascading failures. Additionally, the implementation of a port cooperative mechanism enhances resilience in the face of uncertainties by safeguarding crucial ports within the network and significantly reducing shipper losses. When port resistance is low, the cooperative mechanism reduces shipper losses by nearly half and lowers the average congestion rate. Although port reserve capacity can resist cascading failures, it falls short in the face of severe disruptions. In such cases, the cooperative mechanism compensates for capacity shortages, enhancing port resilience at a low cost. This study contributes to combating and minimizing cascading congestion in liner shipping networks, offering valuable insights for risk prevention and management strategies for ports and shipping companies. It also has implications for yield management and policy decisions from a network perspective.
Published Version
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