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Documents authored by Bouvry, Pascal

Artifact
Software
DOI: 10.4230/artifacts.26934
Multi-objective algorithms implemented in Choco solver.

Authors: Manuel Combarro Simón, Pierre Talbot, and Pascal Bouvry

Abstract
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Manuel Combarro Simón, Pierre Talbot, Pascal Bouvry. Multi-objective algorithms implemented in Choco solver. (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@misc{dagstuhl-artifact-26934,
   title = {{Multi-objective algorithms implemented in Choco solver.}}, 
   author = {Combarro Sim\'{o}n, Manuel and Talbot, Pierre and Bouvry, Pascal},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:rel:5f25b7af157a3f8a5c71eb72ba4de0f07fc7c56e;origin=https://github.com/mancs20/choco-solver;visit=swh:1:snp:a17099d68a2d0c48d35cfb0bcc976a11db76ecc5}{\texttt{swh:1:rel:5f25b7af157a3f8a5c71eb72ba4de0f07fc7c56e}} (visited on 2026-07-13)},
   url = {https://github.com/mancs20/choco-solver/tree/CP-2026},
   doi = {10.4230/artifacts.26934},
}
Document
DOI: 10.4230/LIPIcs.CP.2026.14
Combining an ε-Constraint Method with the Pareto Global Constraint

Authors: Manuel Combarro Simón, Pierre Talbot, and Pascal Bouvry

Published in: LIPIcs, Volume 379, 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)

Abstract
Many real-life problems involve multiple conflicting objectives; hence, the decision maker is provided with a set of trade-off solutions, the Pareto front. While many methods to compute Pareto fronts have been proposed in the mathematical programming literature, comparatively few approaches are available for constraint programming (CP). One of the main state-of-the-art algorithms in CP is a branch-and-bound method that uses a Pareto global constraint, denoted here as MOBAB-CP. In this work, we adapt the SAUGMECON algorithm, a well-known and efficient ε-constraint method, in a CP solver. We also propose a new algorithm that combines SAUGMECON with the Pareto global constraint. Experimental results show that the proposed algorithm consistently achieves better results than our CP implementation of SAUGMECON and is competitive with MOBAB-CP, outperforming it on several of the studied problems.
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Manuel Combarro Simón, Pierre Talbot, and Pascal Bouvry. Combining an ε-Constraint Method with the Pareto Global Constraint. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 14:1-14:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{combarrosimon_et_al:LIPIcs.CP.2026.14,
  author =	{Combarro Sim\'{o}n, Manuel and Talbot, Pierre and Bouvry, Pascal},
  title =	{{Combining an \epsilon-Constraint Method with the Pareto Global Constraint}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{14:1--14:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-432-1},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{379},
  editor =	{Beldiceanu, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2026.14},
  URN =		{urn:nbn:de:0030-drops-266474},
  doi =		{10.4230/LIPIcs.CP.2026.14},
  annote =	{Keywords: Multi-objective combinatorial optimization, constraint programming, Pareto global constraint, \epsilon-constraint method, SAUGMECON}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2023.44
Constraint Model for the Satellite Image Mosaic Selection Problem (Short Paper)

Authors: Manuel Combarro Simón, Pierre Talbot, Grégoire Danoy, Jedrzej Musial, Mohammed Alswaitti, and Pascal Bouvry

Published in: LIPIcs, Volume 280, 29th International Conference on Principles and Practice of Constraint Programming (CP 2023)

Abstract
Satellite imagery solutions are widely used to study and monitor different regions of the Earth. However, a single satellite image can cover only a limited area. In cases where a larger area of interest is studied, several images must be stitched together to create a single larger image, called a mosaic, that can cover the area. Today, with the increasing number of satellite images available for commercial use, selecting the images to build the mosaic is challenging, especially when the user wants to optimize one or more parameters, such as the total cost and the cloud coverage percentage in the mosaic. More precisely, for this problem the input is an area of interest, several satellite images intersecting the area, a list of requirements relative to the image and the mosaic, such as cloud coverage percentage, image resolution, and a list of objectives to optimize. We contribute to the constraint and mixed integer lineal programming formulation of this new problem, which we call the satellite image mosaic selection problem, which is a multi-objective extension of the polygon cover problem. We propose a dataset of realistic and challenging instances, where the images were captured by the satellite constellations SPOT, Pléiades and Pléiades Neo. We evaluate and compare the two proposed models and show their efficiency for large instances, up to 200 images.
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Manuel Combarro Simón, Pierre Talbot, Grégoire Danoy, Jedrzej Musial, Mohammed Alswaitti, and Pascal Bouvry. Constraint Model for the Satellite Image Mosaic Selection Problem (Short Paper). In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 44:1-44:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{combarrosimon_et_al:LIPIcs.CP.2023.44,
  author =	{Combarro Sim\'{o}n, Manuel and Talbot, Pierre and Danoy, Gr\'{e}goire and Musial, Jedrzej and Alswaitti, Mohammed and Bouvry, Pascal},
  title =	{{Constraint Model for the Satellite Image Mosaic Selection Problem}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{44:1--44:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-300-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{280},
  editor =	{Yap, Roland H. C.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2023.44},
  URN =		{urn:nbn:de:0030-drops-190815},
  doi =		{10.4230/LIPIcs.CP.2023.44},
  annote =	{Keywords: constraint modeling, satellite imaging, set covering, polygon covering}
}
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