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AI-driven Just-In-Time coordination between port and ships: Advancing maritime decarbonization

Author

Listed:
  • Nguyen, Son
  • Fu, Xiuju
  • Zhao, Liangbin
  • Xu, Haiyan
  • Zhang, Xiaocai
  • Li, Ning
  • Zhang, Wei
  • Yin, Xiao Feng
  • Daichi, Ogawa
  • Koh, Jimmy
  • Zheng, Qin

Abstract

The hurry-up-and-wait behaviors and the bunching of arriving ships at ports are global maritime logistics challenges, causing backlogs and delays, high traffic density, and unnecessary fuel consumption and emissions. Just-in-time arrival (JITA) of vessels utilizes potential waiting time for slow-steaming and alleviates pressure on port resources. However, practical frameworks and solutions supporting JITA are still scarce due to the involvement of different stakeholders and the complexity of operational orchestration. This study proposes a comprehensive JITA framework to enable intelligent port-ship coordination that leverages maritime big data and AI-powered analytics to address dynamic operating conditions (e.g., ship speed optimization, metocean conditions, and port resource availability). Integrating Collaborative Decision-Making and System of Systems based on Supply Chain Collaboration, the combinative framework efficiently enables JITA for early speed optimization, reliability of effectiveness, quantifiable benefits, and management and minimalism of data requirements. Utilizing the framework, AI for Just-In-Time (AI4JIT) was developed as a JITA decision support tool to provide descriptive, predictive, and prescriptive analytics for port-ship coordination. Adapting to one of the biggest ports in Asia, the integrated system achieved 6.45 %–7.39 % fuel and emission savings in 2.94 % port calls based on the test using historical data. Apart from presenting a well-structured framework to JITA system design and implementation, this study demonstrates the capability of AI-augmented collaborative optimization in maritime logistics and offers quantifiable benefits for decision-making. This research also paves the way for broader adoption of JITA and AI in the industry, with identified knowledge gaps in data, risk, and quality governance.

Suggested Citation

  • Nguyen, Son & Fu, Xiuju & Zhao, Liangbin & Xu, Haiyan & Zhang, Xiaocai & Li, Ning & Zhang, Wei & Yin, Xiao Feng & Daichi, Ogawa & Koh, Jimmy & Zheng, Qin, 2026. "AI-driven Just-In-Time coordination between port and ships: Advancing maritime decarbonization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:transe:v:207:y:2026:i:c:s1366554525005976
    DOI: 10.1016/j.tre.2025.104569
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    References listed on IDEAS

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