License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.12.10.166
URN: urn:nbn:de:0030-drops-178257
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17825/
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Frejinger, Emma ; Lodi, Andrea ; Lombardi, Michele ; Yorke-Smith, Neil
Weitere Beteiligte (Hrsg. etc.): Emma Frejinger and Andrea Lodi and Michele Lombardi and Neil Yorke-Smith

Data-Driven Combinatorial Optimisation (Dagstuhl Seminar 22431)

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dagrep_v012_i010_p166_22431.pdf (3 MB)

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.

BibTeX - Entry

@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.dagstuhl.de/opus/volltexte/2023/17825},
  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}
}

Keywords: combinatorial optimisation, constraint programming, machine learning, Mixed integer programming, operations research, Reinforcement learning
Collection: DagRep, Volume 12, Issue 10
Issue Date: 2023
Date of publication: 03.05.2023

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