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Data-Driven Combinatorial Optimisation (Dagstuhl Seminar 22431)

Authors: Emma Frejinger, Andrea Lodi, Michele Lombardi, and Neil Yorke-Smith

Published in: Dagstuhl Reports, Volume 12, Issue 10 (2023)


Abstract
Machine learning’s impressive achievements in the last decade have urged many scientific communities to ask if and how the techniques developed in that field to leverage data could be used to advance research in others. The combinatorial optimisation community is one of those, and the area of data-driven combinatorial optimisation has emerged. The motivation of the seminar and its design and development have followed the idea of making researchers both in academia and industry belonging to different communities - from operations research to constraint programming, from artificial intelligence to machine learning - communicate, establish a shared language, and ultimately (try to) set the roadmap for the development of the field.

Cite as

Emma Frejinger, Andrea Lodi, Michele Lombardi, and Neil Yorke-Smith. Data-Driven Combinatorial Optimisation (Dagstuhl Seminar 22431). In Dagstuhl Reports, Volume 12, Issue 10, pp. 166-174, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@Article{frejinger_et_al:DagRep.12.10.166,
  author =	{Frejinger, Emma and Lodi, Andrea and Lombardi, Michele and Yorke-Smith, Neil},
  title =	{{Data-Driven Combinatorial Optimisation (Dagstuhl Seminar 22431)}},
  pages =	{166--174},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{10},
  editor =	{Frejinger, Emma and Lodi, Andrea and Lombardi, Michele and Yorke-Smith, Neil},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.12.10.166},
  URN =		{urn:nbn:de:0030-drops-178257},
  doi =		{10.4230/DagRep.12.10.166},
  annote =	{Keywords: combinatorial optimisation, constraint programming, machine learning, Mixed integer programming, operations research, Reinforcement learning}
}
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