This PhD course explores the integration of artificial intelligence, machine learning, and reinforcement learning into operational decision-making. With an emphasis on practical applications rather than exhaustive theoretical development, the course builds on recent advances in the operations management literature. Class sessions introduce core theoretical concepts from the classical literature to develop intuition, provide direction for deeper study, and frame discussion around contemporary research and applications.
Topics span agentic AI systems, generative AI and digital twins, modeling of customer choice, experimental design under interference, AI-driven experimentation, and optimization enhanced by AI tools. The course concludes with reinforcement learning and its growing role in operational decision-making. Sessions combine discussion of recent research, student and instructor presentations, and guest lectures from leading scholars in the field.
