2 Search Results for "Vavrille, Mathieu"

Document
DOI: 10.4230/LIPIcs.CP.2024.30
Learning Precedences for Scheduling Problems with Graph Neural Networks

Authors: Hélène Verhaeghe, Quentin Cappart, Gilles Pesant, and Claude-Guy Quimper

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
The resource constrained project scheduling problem (RCPSP) consists of scheduling a finite set of resource-consuming tasks within a temporal horizon subject to resource capacities and precedence relations between pairs of tasks. It is NP-hard and many techniques have been introduced to improve the efficiency of CP solvers to solve it. The problem is naturally represented as a directed graph, commonly referred to as the precedence graph, by linking pairs of tasks subject to a precedence. In this paper, we propose to leverage the ability of graph neural networks to extract knowledge from precedence graphs. This is carried out by learning new precedences that can be used either to add new constraints or to design a dedicated variable-selection heuristic. Experiments carried out on RCPSP instances from PSPLIB show the potential of learning to predict precedences and how they can help speed up the search for solutions by a CP solver.
Cite as

Hélène Verhaeghe, Quentin Cappart, Gilles Pesant, and Claude-Guy Quimper. Learning Precedences for Scheduling Problems with Graph Neural Networks. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 30:1-30:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{verhaeghe_et_al:LIPIcs.CP.2024.30,
  author =	{Verhaeghe, H\'{e}l\`{e}ne and Cappart, Quentin and Pesant, Gilles and Quimper, Claude-Guy},
  title =	{{Learning Precedences for Scheduling Problems with Graph Neural Networks}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{30:1--30:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.30},
  URN =		{urn:nbn:de:0030-drops-207150},
  doi =		{10.4230/LIPIcs.CP.2024.30},
  annote =	{Keywords: Scheduling, Precedence graph, Graph neural network}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.56
Solution Sampling with Random Table Constraints

Authors: Mathieu Vavrille, Charlotte Truchet, and Charles Prud'homme

Published in: LIPIcs, Volume 210, 27th International Conference on Principles and Practice of Constraint Programming (CP 2021)

Abstract
Constraint programming provides generic techniques to efficiently solve combinatorial problems. In this paper, we tackle the natural question of using constraint solvers to sample combinatorial problems in a generic way. We propose an algorithm, inspired from Meel’s ApproxMC algorithm on SAT, to add hashing constraints to a CP model in order to split the search space into small cells of solutions. By sampling the solutions in the restricted search space, we can randomly generate solutions without revamping the model of the problem. We ensure the randomness by introducing a new family of hashing constraints: randomly generated tables. We implemented this solving method using the constraint solver Choco-solver. The quality of the randomness and the running time of our approach are experimentally compared to a random branching strategy. We show that our approach improves the randomness while being in the same order of magnitude in terms of running time.
Cite as

Mathieu Vavrille, Charlotte Truchet, and Charles Prud'homme. Solution Sampling with Random Table Constraints. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 56:1-56:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

Copy BibTex To Clipboard

@InProceedings{vavrille_et_al:LIPIcs.CP.2021.56,
  author =	{Vavrille, Mathieu and Truchet, Charlotte and Prud'homme, Charles},
  title =	{{Solution Sampling with Random Table Constraints}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{56:1--56:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-211-2},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{210},
  editor =	{Michel, Laurent D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2021.56},
  URN =		{urn:nbn:de:0030-drops-153477},
  doi =		{10.4230/LIPIcs.CP.2021.56},
  annote =	{Keywords: solutions, sampling, table constraint}
}
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