Optimizing Port Logistics Through Generative AI: Revolutionizing Efficiency and Resilience in the Maritime Industry
DOI:
https://doi.org/10.56177/12icmie2025.156Keywords:
artificial intelligence, generative AI, port logistics, maritime industry, digital twins, IoT, machine learning, supply chain resilience, predictive analytics.Abstract
The maritime industry faces growing challenges in optimizing port logistics due to increasing trade volumes, environmental regulations, and supply chain disruptions. This comprehensive literature review examines the transformative role of artificial intelligence (AI), with particular focus on generative AI, in enhancing efficiency and resilience in port operations. Through systematic analysis of 23 peer-reviewed studies published between 2021-2025, this review synthesizes advancements in real-time data integration, machine learning, digital twins, IoT, and autonomous systems that collectively improve operational decision-making, risk management, and environmental sustainability. Key findings reveal that machine learning applications achieve 90% effectiveness ratings in operational optimization, while predictive analytics demonstrates 89% effectiveness in risk management. The study identifies significant benefits including reduced vessel turnaround times (up to 45.8% reduction in carbon emissions), improved resource allocation through digital twins, and enhanced safety compliance (from 60% to 85%). However, implementation faces substantial challenges, with technical barriers representing 25% of obstacles, followed by cost considerations (22%) and organizational resistance (20%). The review outlines promising future research directions, emphasizing unexplored applications of generative AI in predictive maintenance and integration with emerging technologies like blockchain and quantum computing. This analysis provides valuable insights for researchers, industry stakeholders, and policymakers aiming to leverage AI for a more efficient, resilient, and sustainable maritime logistics ecosystem.
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Copyright (c) 2025 Minodora BADEA, Olga BUCOVETCHI, Adrian V. GHEORGHE, Gabriel RAICU
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