Artificial Intelligence and Discrete Optimization

February 27 - March 3, 2023


In recent years, the use of Machine Learning techniques to Operations Research (OR) problems, especially in the Discrete Optimization (DO) a.k.a. Combinatorial Optimization context, opens very interesting scenarios because DO is the “home” of an endless list of decision-making problems that are of fundamental importance in multitude applications.

The workshop will bring together experts in mathematics (optimization, graph theory, sparsity, combinatorics, statistics), operations research (assignment problems, routing, planning, Bayesian search, automation, scheduling), machine learning (deep learning, supervised, self-supervised and reinforcement learning) and artificial intelligence at large (including multi-agent systems, interpretability, fairness, etc.). In addition, the focus will be on

  • Algorithmic challenges and potential of the interaction between AI and OR;
  • Data requirements in which such an interaction can be profitable; and
  • Application areas that are likely to lead to game-changing results (e.g., transportation, supply chain, public policy, energy).

Program Flyer

Organizing Committee

Xavier Bresson (National University of Singapore)
Bistra Dilkina (University of Southern California (USC))
Andrea Lodi (Cornell University)
Pascal Van Hentenryck (Georgia Institute of Technology)