Beyond Modeling: How ML Is Reshaping Operations Research for Dynamic Decision Making

Shadi Sharif Azadeh examines how artificial intelligence is turning operations research into a learning-enabled approach to decisions in dynamic, uncertain and data-rich systems. The talk develops two directions: integrating prediction, optimization and learning sequentially, and embedding AI within optimization algorithms. Applications address demand and supply in multimodal mobility with uncertain resources, train scheduling and modular autonomous vehicle operations. Rather than replacing optimization, AI can predict changing states, learn from operational feedback, improve computational efficiency and support adaptation where static models are inadequate.