Mahmoud Golabi

My research focuses on the design and development of advanced optimization methodologies at the intersection of Operations Research and Artificial Intelligence. Particular emphasis is placed on hybrid approaches combining exact optimization methods, metaheuristics, evolutionary computation, machine learning, and data-driven decision-making techniques. Research interests include AI-assisted optimization, learning-enhanced metaheuristics, surrogate-assisted optimization, reinforcement learning for optimization, graph-based learning for combinatorial problems, and interactive multi-objective optimization. These methods are applied to address complex real-world challenges in logistics, transportation, energy systems, healthcare, scheduling, and industrial decision support.