Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon

Understand

This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems.

• Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined.
• Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way.
• We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so.