Artificial Intelligence in Port Logistics: A Bibliometric Analysis of Technological Integration and Research Dynamics

Résumé

The paper explores the transformation of port logistics operations with artificial intelligence (AI) during the port transformation into a smart port. The research integrates capabilities-based resource analysis and dynamic capabilities with sociotechnical implementations of technologies and resilience approaches of complex systems under disruptions. The system applies robust data infrastructures (AIS/IoT streams and data quality and interoperability) to propel analytical and AI modules (forecasting and optimization and anomaly detection) that become effective once integrated with sufficient governance systems and trained personnel and operational processes to transform planning and safety and sustainability operations.

It applies Scopus bibliometric research to analyze 123 articles using a systematic approach with both a search protocol and a document screening and duplication verification. It incorporates annual behavior and distribution of author and country performance analysis with science mapping techniques that explore keyword relation and co-citation and bibliographic coupling and conceptual structuring tools that construct thematic maps and multiple correspondence analysis with community detection while applying explicit thresholding and robust tests.

The research connects AI applications to smart port domains through specific data-to-impact pathways while providing a method for bibliometric analysis that enables future updates. The research presents a step-by-step approach for data readiness followed by predictive and optimization implementation and organizational integration. The paper supports public policy through recommendations for data sharing standards and complete environmental benefit assessments. The research proposes a future study plan which combines field-based testing with multiple port assessments to enhance both cause-effect understanding and research applicability.

Classification JEL: O33

Paper type: Theoretical Research

Erratum Notice:

In the article entitled “Artificial Intelligence in Port Logistics: A Bibliometric Analysis of Technological Integration and Research Dynamics”, authored by [Author’s Name] et al., an error was identified in the reference section.

Two sources were incorrectly cited in the original version of the paper. Following a formal notice from Professor Harilaos N. Psaraftis, the author has reviewed and corrected these inaccuracies. The correct reference is as follows:

Latinopoulos, C., Zavvos, E., Kaklis, D., Leemen, V., & Halatsis, A. (2025). Marine Voyage Optimization and Weather Routing with Deep Reinforcement Learning. Journal of Marine Science and Engineering, 13(5), 902. https://doi.org/10.3390/jmse13050902

All other parts of the manuscript remain unchanged.

The author sincerely apologizes to Professor Harilaos N. Psaraftis and to the readers for the oversight and expresses appreciation for the valuable feedback and understanding.