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Documents authored by Verhaeghe, Hélène

Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2026.63
The CP Shortcut: Solving the High-Power Pump Activation Problem Without the Overkill (Short Paper)

Authors: Mohamed-Anass Gallass, Philippe Greiner, Antoine Tuerlinckx, and Hélène Verhaeghe

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

Abstract
Constraint Programming (CP) has been around for decades, yet it remains largely unknown in industry. When faced with combinatorial optimization problems, industry practitioners not knowledgeable in CP techniques often resort to more creative but not necessarily adequate solutions. This paper is the result of an actual case study brought by Technord, an industry consultant. The problem at hand is the optimization of the activation schedule for high-power pumps in a water treatment facility under fluctuating energy costs. The schedule was previously generated using Discrete Particle Swarm Optimization (DPSO). This method struggled with the increasing complexity of volatile market signals and strict operational constraints. Our simpler model, developed in Python using the CPMpy library, formalizes the problem as a CP model. Our experiments demonstrate the benefits of our model’s simplicity compared to the DPSO solution. This use case also showcases the importance of the accessibility of constraint modelling solutions for less knowledgeable practitioners.
Cite as

Mohamed-Anass Gallass, Philippe Greiner, Antoine Tuerlinckx, and Hélène Verhaeghe. The CP Shortcut: Solving the High-Power Pump Activation Problem Without the Overkill (Short Paper). In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 63:1-63:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{gallass_et_al:LIPIcs.CP.2026.63,
  author =	{Gallass, Mohamed-Anass and Greiner, Philippe and Tuerlinckx, Antoine and Verhaeghe, H\'{e}l\`{e}ne},
  title =	{{The CP Shortcut: Solving the High-Power Pump Activation Problem Without the Overkill}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{63:1--63:13},
  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.2026.63},
  URN =		{urn:nbn:de:0030-drops-266965},
  doi =		{10.4230/LIPIcs.CP.2026.63},
  annote =	{Keywords: Application, Industrial Problem, Modelling}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.29
Mutational Fuzz Testing for Constraint Modeling Systems

Authors: Wout Vanroose, Ignace Bleukx, Jo Devriendt, Dimos Tsouros, Hélène Verhaeghe, and Tias Guns

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

Abstract
Constraint programming (CP) modeling languages, like MiniZinc, Essence and CPMpy, play a crucial role in making CP technology accessible to non-experts. Both solver-independent modeling frameworks and solvers themselves are complex pieces of software that can contain bugs, which undermines their usefulness. Mutational fuzz testing is a way to test complex systems by stochastically mutating input and verifying preserved properties of the mutated output. We investigate different mutations and verification methods that can be used on the constraint specifications directly. This includes methods proposed in the context of SMT problem specifications, as well as new methods related to global constraints, optimization, and solution counting/preservation. Our results show that such a fuzz testing approach improves the overall code coverage of a modeling system compared to only unit testing, and is able to find bugs in the whole toolchain, from the modeling language transformations themselves to the underlying solvers.
Cite as

Wout Vanroose, Ignace Bleukx, Jo Devriendt, Dimos Tsouros, Hélène Verhaeghe, and Tias Guns. Mutational Fuzz Testing for Constraint Modeling Systems. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 29:1-29:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{vanroose_et_al:LIPIcs.CP.2024.29,
  author =	{Vanroose, Wout and Bleukx, Ignace and Devriendt, Jo and Tsouros, Dimos and Verhaeghe, H\'{e}l\`{e}ne and Guns, Tias},
  title =	{{Mutational Fuzz Testing for Constraint Modeling Systems}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{29:1--29:25},
  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.2024.29},
  URN =		{urn:nbn:de:0030-drops-207149},
  doi =		{10.4230/LIPIcs.CP.2024.29},
  annote =	{Keywords: fuzz testing, Constraint modeling language, bugs, mutational testing, modeling, constraint reformulation}
}
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}
}
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