Machine Learning and Simulation for Efficiency and Sustainability in Container Terminals

Abderaouf Benghalia (), Amani Ferdjallah, Mustapha Oudani and Jaouad Boukachour
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Abderaouf Benghalia: Department of Computer Science, Faculty of Sciences, University of Algiers, Algiers 16000, Algeria
Amani Ferdjallah: Department of Computer Science, Faculty of Sciences, University of Algiers, Algiers 16000, Algeria
Mustapha Oudani: TICLab, College of Engineering and Architecture, International University of Rabat, Rabat 11103, Morocco
Jaouad Boukachour: IUT, Le Havre Normandy University, 76610 Le Havre, France

Sustainability, 2025, vol. 17, issue 7, 1-22

Abstract: This paper examines the impact of reducing ship turnaround time on the performance of container terminals, with a focus on leveraging artificial intelligence (AI) to enhance operational efficiency. It presents a novel framework combining machine learning algorithms with discrete-event simulation to predict ship turnaround times using historical data. The proposed approach is empirically validated with data from the Algiers Port Container Terminal, achieving an exceptionally high predictive precision of 0.9991. Simulating terminal operations with both real and predicted data offers valuable insights into improving performance. The results demonstrate that minimizing empty trips and reducing the waiting times for handling equipment significantly enhance turnaround time. Additionally, optimizing terminal operations reduces carbon emissions, aligning with sustainable development objectives in port logistics. This study proposes a novel integration of machine learning and simulation, demonstrating its effectiveness in optimizing ship turnaround times and reducing carbon emissions. By integrating machine learning and discrete-event simulation, this research offers new perspectives on port logistics, contributing to the advancement of sustainable and efficient terminal operations.

Keywords: carbon emissions; sustainability; machine learning; container terminal; simulation; port logistics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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