Conférences - Recherche opérationnelle - États-Unis

Relationships between the areas of control, learning, and optimization have always been strong, but have recently been expanding and deepening in surprising ways. Optimization formulations and algorithms have historically been vital to solving problems in control and learning, while conversely, control and learning have provided interesting perspectives on optimization methods. Intersections that have been explored recently include relationships between reinforcement learning and model predictive control, and the use of control techniques to analyze the convergence of optimization algorithms. This workshop will bring together researchers who work in Control, Learning, and Optimization to discuss current areas of interaction and explore possibilities for future areas of collaboration.

This workshop aims to bring together the essential communities from mathematics and engineering to address forthcoming issues central to our future transportation systems. It will bring together the operations research experts developing the next generation of fleet dispatching algorithms vital to mobility on demand services, the transportation engineers responsible for planning the future of our public and private transportation systems, and the modelers responsible for predicting new congestion dynamics at local and city scales. The workshop will also explore how these systems will operate in city, suburb, and rural contexts to support mobility needs for all.

Part of the Long Program Mathematical Challenges and Opportunities for Autonomous Vehicles

Électrotechnique,    Robotique, automation & mécatronique

Institute for Operations Research and the Management Sciences (informs)