🔬 Prof. Amel Jaoua’s research focus primarily revolves around simulation modeling and optimization techniques to address complex logistical and industrial challenges. Her work spans various domains, including stochastic freight transportation scheduling, real-time fleet management, and intelligent control architectures for industrial systems. She explores innovative applications such as digital twins for smart manufacturing and reinforcement learning for container loading optimization. With a keen interest in interdisciplinary studies, she has contributed significantly to fields like call center management, supply chain optimization, and condition-based maintenance using machine learning. Prof. Jaoua’s diverse research portfolio demonstrates her commitment to advancing technology-driven solutions for real-world problems. 🌐