Utilizing AI algorithms for optimizing and intelligently managing transportation in ports

Document Type : Original Article

Authors
Mohammad Amin Ebrahimzadeh 1
Arshia Attar 2
Saba Mayeli 2
Melika Zeyni Ahmadabad 2
1 Department of Civil Engineering, Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran.
2 Department of Industrial Engineering, Faculty of Engineering, Karaj Branch, Islamic Azad University, Iran.
10.22034/road.2025.514907.2383
Abstract
In this study, the application of artificial intelligence algorithms in optimizing and intelligently managing transportation in ports has been examined. Major challenges in port operations, including congestion, improper resource allocation, high operational costs, and delays in cargo processing, are issues that can be improved using smart technologies. Intelligent systems, through big data analysis, machine learning models, and real-time information processing, enhance port operational efficiency and improve productivity.The quantitative results of this study indicate that utilizing the Genetic Algorithm (GA) for optimizing container truck routes has led to a 33.3% reduction in transportation time and a 25% decrease in fuel consumption. Additionally, the LSTM-based traffic prediction model, with an accuracy of 88%, outperformed traditional methods such as ARIMA (with an accuracy of 82%). The Particle Swarm Optimization (PSO) algorithm also improved operational efficiency by 20% in resource allocation.Furthermore, smart systems can reduce unnecessary delays, manage operational costs, and minimize environmental impacts. The qualitative findings suggest that AI technologies can reduce reliance on traditional processes, minimize human errors, and enhance workforce efficiency. Despite their significant advantages, implementing these technologies faces challenges such as high initial costs, the need for robust digital infrastructure, and resistance to organizational changes. This study provides practical solutions to overcome these challenges and facilitate the development of smart ports.
Keywords
Artificial Intelligence
Smart Ports
Optimization
Transportation Management
Machine Learning
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