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

•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 dec...

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Veröffentlicht in:European journal of operational research Jg. 290; H. 2; S. 405 - 421
Hauptverfasser: Bengio, Yoshua, Lodi, Andrea, Prouvost, Antoine
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 16.04.2021
Schlagworte:
Branch and bound
Combinatorial optimization
Machine learning
Mixed-integer programming solvers
Mixed-integer programming solvers
Branch and bound
Combinatorial optimization
Machine learning
ISSN:0377-2217, 1872-6860
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Zusammenfassung:•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 which 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. 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.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2020.07.063