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Documents authored by Hebrard, Emmanuel

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Software
DOI: 10.4230/artifacts.24099
Tempo

Authors: Emmanuel Hebrard

Abstract
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Emmanuel Hebrard. Tempo (Software, Source Code). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@misc{dagstuhl-artifact-24099,
   title = {{Tempo}}, 
   author = {Hebrard, Emmanuel},
   note = {Software, swhId: \href{https://archive.softwareheritage.org/swh:1:dir:bbab6415fb8f44fef04878de35e7b50e797596bd;origin=https://gitlab.laas.fr/roc/emmanuel-hebrard/tempo;visit=swh:1:snp:f3fa3dfd7696c9fdb4decc67161bf8e7892b9864;anchor=swh:1:rev:e8a41276e0563e397ffeed83c051390feec458d4}{\texttt{swh:1:dir:bbab6415fb8f44fef04878de35e7b50e797596bd}} (visited on 2025-08-08)},
   url = {https://gitlab.laas.fr/roc/emmanuel-hebrard/tempo},
   doi = {10.4230/artifacts.24099},
}
Document
DOI: 10.4230/LIPIcs.CP.2025.3
Solving the Agile Earth Observation Satellite Scheduling Problem with CP and Local Search

Authors: Valentin Antuori, Damien T. Wojtowicz, and Emmanuel Hebrard

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
The increasing hunger for remote sensing data fuels a boom in satellite imagery, leading to larger agile Earth observation satellite (AEOS) constellations. Therefore, instances of the AEOS scheduling problem (AEOSSP) has become harder to solve. As most existing approaches to solve AEOSSP are designed for a single spacecraft or smaller constellations in mind, they are not tailored to the need of our industrial partner that is about to launch a constellation of 20 AEOSs. Hence, we designed a local search solver able to schedule observations and downloads at such a scale. It relies on solving a series of sub-problems as travelling salesman problem with time windows (TSPTW), first greedily, then using a CP-SAT exact solver in order to find a solution when the greedy insertion fails. Lastly, it schedules downloads and enforces memory constraints with greedy algorithms. Experiments were carried out on instances from the literature as well as generated instances from a simulator we designed. Our experiments show that using CP to solve the sub-problem significantly improve the solutions, and overall our method is slightly better than state-of-the-art approaches.
Cite as

Valentin Antuori, Damien T. Wojtowicz, and Emmanuel Hebrard. Solving the Agile Earth Observation Satellite Scheduling Problem with CP and Local Search. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 3:1-3:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{antuori_et_al:LIPIcs.CP.2025.3,
  author =	{Antuori, Valentin and Wojtowicz, Damien T. and Hebrard, Emmanuel},
  title =	{{Solving the Agile Earth Observation Satellite Scheduling Problem with CP and Local Search}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{3:1--3:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.3},
  URN =		{urn:nbn:de:0030-drops-238647},
  doi =		{10.4230/LIPIcs.CP.2025.3},
  annote =	{Keywords: Local Search, Greedy Algorithms, Aerospace Applications}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.13
Disjunctive Scheduling in Tempo

Authors: Emmanuel Hebrard

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
In this paper we introduce a constraint programming lazy clause and literal generation solver embarking ideas from SAT Modulo theories. A key aspect of the solver are Boolean variables with an associated semantic in difference logic, i.e., systems of binary numeric difference constraints or edges, making it particularly adapted to scheduling and other temporal problems. We apply this solver to disjunctive scheduling problems, where edges are used as branching variables, can be inferred via the edge finding rule as well as by transitivity reasoning, and can in turn strengthen propagation via temporal graph reasoning. Our experiments on job-shop scheduling show that a deep integration of these techniques makes our solver competitive with state-of-the-art approaches on these problems.
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Emmanuel Hebrard. Disjunctive Scheduling in Tempo. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 13:1-13:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hebrard:LIPIcs.CP.2025.13,
  author =	{Hebrard, Emmanuel},
  title =	{{Disjunctive Scheduling in Tempo}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{13:1--13:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.13},
  URN =		{urn:nbn:de:0030-drops-238746},
  doi =		{10.4230/LIPIcs.CP.2025.13},
  annote =	{Keywords: Scheduling, Constraint solvers, Clause-learning}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.27
Understanding the Impact of Value Selection Heuristics in Scheduling Problems

Authors: Tim Luchterhand, Emmanuel Hebrard, and Sylvie Thiébaux

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract
It has been observed that value selection heuristics have less impact than other heuristic choices when solving hard combinatorial optimization (CO) problems. It is often thought that this is because more time is spent on unsatisfiable sub-problems where the value ordering is irrelevant. In this paper we investigate this belief in the scheduling domain and come up with a more detailed explanation. We find that, even though there are less relevant choices to be made on hard instances, each mistake tends to have a bigger impact, to a point where the potential gain from a value heuristic predominates. Moreover, we observe two interesting and relatively surprising phenomena when solving scheduling problems. First, the accuracy of a given value selection heuristic decreases with the optimality gap. Second, the computational penalty of a mistake increases with the accuracy of the heuristic. For the first observation, we argue that on hard problems, constraint propagation removes a large portion of choices that align with the intuition behind the heuristic. This means that the heuristic faces mostly difficult choices. For the second observation, we argue that simple heuristics tend to make more mistakes on intuitive choice points, and the computational cost for refuting these mistakes is smaller than for those made by a more accurate heuristic.
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Tim Luchterhand, Emmanuel Hebrard, and Sylvie Thiébaux. Understanding the Impact of Value Selection Heuristics in Scheduling Problems. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 27:1-27:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{luchterhand_et_al:LIPIcs.CP.2025.27,
  author =	{Luchterhand, Tim and Hebrard, Emmanuel and Thi\'{e}baux, Sylvie},
  title =	{{Understanding the Impact of Value Selection Heuristics in Scheduling Problems}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{27:1--27:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.27},
  URN =		{urn:nbn:de:0030-drops-238885},
  doi =		{10.4230/LIPIcs.CP.2025.27},
  annote =	{Keywords: Scheduling, Branching Heuristics, Constraint Programming}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.19
An Efficient Constraint Programming Approach to Preemptive Job Shop Scheduling

Authors: Carla Juvin, Emmanuel Hebrard, Laurent Houssin, and Pierre Lopez

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

Abstract
Constraint Programming has been widely, and very successfully, applied to scheduling problems. However, the focus has been on uninterruptible tasks, and preemptive scheduling problems are typically harder for existing constraint solvers. Indeed, one usually needs to represent all potential task interruptions thus introducing many variables and symmetrical or dominated choices. In this paper, building on mostly known results, we observe that a large class of preemptive disjunctive scheduling problems do not require an explicit model of task interruptions. We then introduce a new constraint programming approach for this class of problems that significantly outperforms state-of-the-art dedicated approaches in our experimental results.
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Carla Juvin, Emmanuel Hebrard, Laurent Houssin, and Pierre Lopez. An Efficient Constraint Programming Approach to Preemptive Job Shop Scheduling. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 19:1-19:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{juvin_et_al:LIPIcs.CP.2023.19,
  author =	{Juvin, Carla and Hebrard, Emmanuel and Houssin, Laurent and Lopez, Pierre},
  title =	{{An Efficient Constraint Programming Approach to Preemptive Job Shop Scheduling}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{19:1--19:16},
  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.2023.19},
  URN =		{urn:nbn:de:0030-drops-190568},
  doi =		{10.4230/LIPIcs.CP.2023.19},
  annote =	{Keywords: Constraint Programming, Scheduling, Preemptive Resources}
}
Document
DOI: 10.4230/LIPIcs.CP.2022.9
Complexity of Minimum-Size Arc-Inconsistency Explanations

Authors: Christian Bessiere, Clément Carbonnel, Martin C. Cooper, and Emmanuel Hebrard

Published in: LIPIcs, Volume 235, 28th International Conference on Principles and Practice of Constraint Programming (CP 2022)

Abstract
Explaining the outcome of programs has become one of the main concerns in AI research. In constraint programming, a user may want the system to explain why a given variable assignment is not feasible or how it came to the conclusion that the problem does not have any solution. One solution to the latter is to return to the user a sequence of simple reasoning steps that lead to inconsistency. Arc consistency is a well-known form of reasoning that can be understood by a human. We consider explanations as sequences of propagation steps of a constraint on a variable (i.e. the ubiquitous revise function in arc consistency algorithms) that lead to inconsistency. We characterize, on binary CSPs, cases for which providing a shortest such explanation is easy: when domains are Boolean or when variables have maximum degree two. However, these polynomial cases are tight. Providing a shortest explanation is NP-hard if the maximum degree is three, even if the number of variables is bounded, or if domain size is bounded by three. It remains NP-hard on trees, despite the fact that arc consistency is a decision procedure on trees. Finally, the problem is not FPT-approximable unless the Gap-ETH is false.
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Christian Bessiere, Clément Carbonnel, Martin C. Cooper, and Emmanuel Hebrard. Complexity of Minimum-Size Arc-Inconsistency Explanations. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 9:1-9:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{bessiere_et_al:LIPIcs.CP.2022.9,
  author =	{Bessiere, Christian and Carbonnel, Cl\'{e}ment and Cooper, Martin C. and Hebrard, Emmanuel},
  title =	{{Complexity of Minimum-Size Arc-Inconsistency Explanations}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{9:1--9:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-240-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{235},
  editor =	{Solnon, Christine},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.9},
  URN =		{urn:nbn:de:0030-drops-166380},
  doi =		{10.4230/LIPIcs.CP.2022.9},
  annote =	{Keywords: Constraint programming, constraint propagation, minimum explanations, complexity}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.13
On How Turing and Singleton Arc Consistency Broke the Enigma Code

Authors: Valentin Antuori, Tom Portoleau, Louis Rivière, and Emmanuel Hebrard

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

Abstract
In this paper, we highlight an intriguing connection between the cryptographic attacks on Enigma’s code and local consistency reasoning in constraint programming. The coding challenge proposed to the students during the 2020 ACP summer school, to be solved by constraint programming, was to decipher a message encoded using the well known Enigma machine, with as only clue a tiny portion of the original message. A number of students quickly crafted a model, thus nicely showcasing CP technology - as well as their own brightness. The detail that is slightly less favorable to CP technology is that solving this model on modern hardware is challenging, whereas the "Bombe", an antique computing device, could solve it eighty years ago. We argue that from a constraint programming point of vue, the key aspects of the techniques designed by Polish and British cryptanalysts can be seen as, respectively, path consistency and singleton arc consistency on some constraint satisfaction problems.
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Valentin Antuori, Tom Portoleau, Louis Rivière, and Emmanuel Hebrard. On How Turing and Singleton Arc Consistency Broke the Enigma Code. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 13:1-13:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{antuori_et_al:LIPIcs.CP.2021.13,
  author =	{Antuori, Valentin and Portoleau, Tom and Rivi\`{e}re, Louis and Hebrard, Emmanuel},
  title =	{{On How Turing and Singleton Arc Consistency Broke the Enigma Code}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{13:1--13:16},
  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.2021.13},
  URN =		{urn:nbn:de:0030-drops-153040},
  doi =		{10.4230/LIPIcs.CP.2021.13},
  annote =	{Keywords: Constraint Programming, Cryptography}
}
Document
DOI: 10.4230/LIPIcs.CP.2021.14
Combining Monte Carlo Tree Search and Depth First Search Methods for a Car Manufacturing Workshop Scheduling Problem

Authors: Valentin Antuori, Emmanuel Hebrard, Marie-José Huguet, Siham Essodaigui, and Alain Nguyen

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

Abstract
Many state-of-the-art methods for combinatorial games rely on Monte Carlo Tree Search (MCTS) method, coupled with machine learning techniques, and these techniques have also recently been applied to combinatorial optimization. In this paper, we propose an efficient approach to a Travelling Salesman Problem with time windows and capacity constraints from the automotive industry. This approach combines the principles of MCTS to balance exploration and exploitation of the search space and a backtracking method to explore promising branches, and to collect relevant information on visited subtrees. This is done simply by replacing the Monte-Carlo rollouts by budget-limited runs of a DFS method. Moreover, the evaluation of the promise of a node in the Monte-Carlo search tree is key, and is a major difference with the case of games. For that purpose, we propose to evaluate a node using the marginal increase of a lower bound of the objective function, weighted with an exponential decay on the depth, in previous simulations. Finally, since the number of Monte-Carlo rollouts and hence the confidence on the evaluation is higher towards the root of the search tree, we propose to adjust the balance exploration/exploitation to the length of the branch. Our experiments show that this method clearly outperforms the best known approaches for this problem.
Cite as

Valentin Antuori, Emmanuel Hebrard, Marie-José Huguet, Siham Essodaigui, and Alain Nguyen. Combining Monte Carlo Tree Search and Depth First Search Methods for a Car Manufacturing Workshop Scheduling Problem. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 14:1-14:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{antuori_et_al:LIPIcs.CP.2021.14,
  author =	{Antuori, Valentin and Hebrard, Emmanuel and Huguet, Marie-Jos\'{e} and Essodaigui, Siham and Nguyen, Alain},
  title =	{{Combining Monte Carlo Tree Search and Depth First Search Methods for a Car Manufacturing Workshop Scheduling Problem}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{14:1--14:16},
  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.2021.14},
  URN =		{urn:nbn:de:0030-drops-153052},
  doi =		{10.4230/LIPIcs.CP.2021.14},
  annote =	{Keywords: Monte-Carlo Tree Search, Travelling Salesman Problem, Scheduling}
}
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