Machine learning for combinatorial optimization: A methodological tour d’horizon

Yoshua Bengio, Andrea Lodi and Antoine Prouvost

European Journal of Operational Research, 2021, vol. 290, issue 2, 405-421

Abstract: 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. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.

Keywords: Combinatorial optimization; Machine learning; Branch and bound; Mixed-integer programming solvers (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (43)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:290:y:2021:i:2:p:405-421

DOI: 10.1016/j.ejor.2020.07.063

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