A Multi-agent Digital Twin Framework for Predictive Maintenance Using Machine Learning and Genetic Algorithms: A Case Study Morocco, Tangier Med Port

Hamza Garmouch () and Otman Abdoun ()
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Hamza Garmouch: Abdelmalek Essaadi University, ISISA Team, Faculty of Science
Otman Abdoun: Abdelmalek Essaadi University, ISISA Team, Faculty of Science

SN Operations Research Forum, 2025, vol. 6, issue 4, 1-32

Abstract: Abstract This study proposes a digital twin–based predictive maintenance framework for crane systems in container terminals. The system integrates real-time sensor simulation, machine learning–based failure detection, and genetic algorithm–based maintenance optimization. Eight digital twin models of cranes were simulated on the Azure Digital Twins platform and monitored through a custom-designed dashboard. Using synthetic yet dynamic data, the framework forecasts failure risk and remaining useful life with a random forest model, while maintenance schedules are optimized to minimize cost and downtime. The results demonstrate the system’s novelty, scalability, and flexibility, as it unifies data-driven forecasting and evolutionary optimization within a real-time digital twin environment, establishing a foundation for next-generation predictive maintenance systems in ports.

Keywords: Predictive maintenance; Digital twin; Container terminals; Crane operations; Real-time simulation; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00579-x

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