Heterogeneous Port Digital Twin Integrating Artificial Intelligence for Performance, Resilience, and Security Optimization: An Africa–Europe Comparative Perspective

Abstract

This paper proposes a heterogeneous Digital Twin framework enhanced with Artificial Intelligence to optimize performance, resilience, and security in port systems. The approach integrates multi-source data acquisition, numerical modeling, discrete-event simulation, and adaptive AI-based decision mechanisms. It supports both operational and strategic decision-making.

A modular prototype was developed and validated using realistic scenarios with heterogeneous quay capacities, stochastic vessel arrivals, and operational disruptions, resulting in a 12–18% reduction in vessel waiting times. Simulation results indicate significant improvements in vessel turnaround times, quay utilization, congestion mitigation, and risk management, under both normal and disrupted conditions.

A comparative analysis between African and European ports highlights infrastructure constraints, digital maturity gaps, and opportunities for context-specific digital transformation. This study provides quantitative evidence supporting the adoption of AI-driven heterogeneous digital twins as a decision-support tool for sustainable, secure, and competitive port management. The proposed framework also provides a scalable foundation for cybersecurity integration and governance in digitally evolving port ecosystems.