Invited Speakers

The rapid evolution of artificial intelligence (AI) has introduced powerful capabilities and unique challenges to traditional operational research methodologies. This keynote presentation explores the modern landscape of modeling complex stochastic service systems through three core lenses: Analytical Models (AM), Simulation Models (SM), and Learning-based Models (LM). Drawing on real-world applications in healthcare clinics and border-crossing operations, the session critically examines whether modern AI tools act as a disruptive force or a collaborative assistant across these distinct modeling paradigms. Rather than evaluating these approaches in isolation, this talk investigates the limitations of relying on a single method and proposes an integrated framework to address system complexities. The discussion provides key insights into where AI delivers the highest practical utility, where human mathematical innovation remains entirely irreplaceable, and how to effectively synthesize these tools for modern operational challenges.