A Systematic Review of Artificial Intelligence Applications in Port Logistics Optimization

Context: In the context of port logistics, efficient management of goods flow and operations is essential to ensure the smoothness of international trade exchanges. However, the logistical challenges faced by ports are becoming increasingly complex, primarily due to the rising freight volume, limited availability of handling resources, container storage spaces, and berths on the docks. The integration of artificial intelligence (AI) opens unprecedented opportunities to address these challenges. By using metaheuristic algorithms, AI can analyze vast datasets in real-time and propose optimization strategies aimed at improving the operational efficiency of ports. This approach aims to reduce waiting times, optimize resource utilization, minimize congestion and delays, and enhance the competitiveness of ports globally. Thus, the integration of AI into port logistics represents a burgeoning field of research and innovation, with significant implications for the efficiency, sustainability, and profitability of port operations worldwide. Objective: The main objective of this systematic review would be to contribute to a better understanding of the integration of artificial intelligence in port logistics optimization, including a thorough analysis of previous research on this topic, with the aim of identifying promising research directions for the future. Method: The methodology adopted for this study involved a comprehensive search of the Scopus database to identify relevant literature on the topic. A systematic approach was applied to numerous studies retrieved from the database, with a focus on those pertaining to the integration of artificial intelligence in port logistics optimization. Following a thorough screening process, a total of 119 studies were selected for inclusion in our review. These studies were analyzed to extract key insights and trends regarding the application of AI techniques in optimizing port logistics operations. Results: Examining existing studies on the integration of artificial intelligence and heuristics in port logistics, we found that this approach has proven effective in solving specific optimization problems encountered in port operations, particularly in terms of planning, coordination, and resource optimization. We also observed that most of the research in this SLR has been conducted in recent years, indicating a significant increase in interest in this field. This suggests a growing recognition of the importance of artificial intelligence and its potential to enhance the efficiency and competitiveness of ports. This concentration of studies in recent years may also be due to emerging logistic challenges, which likely prompted researchers and practitioners to explore new solutions to improve port operations management. Conclusion: Artificial intelligence in port logistics reflects both the increasing relevance of this field and the ongoing need for innovation and development to address current and future logistic challenges; there is still much to be done to fully harness the potential of artificial intelligence in port logistics. The gaps and opportunities identified in recent research underscore the need for ongoing research to address unresolved issues and explore new optimization pathways.