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Documents authored by Lecoutre, Christophe

Document
DOI: 10.4230/LIPIcs.CP.2025.25
Aircraft Resource-Constrained Assembly Line Balancing with Learning Effect: A Constraint Programming Approach

Authors: Duc Anh Le, Stéphanie Roussel, and Christophe Lecoutre

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

Abstract
Balancing aeronautical assembly lines is a major challenge in modern aerospace manufacturing. Aircraft manufacturing plants typically have a predetermined production rate, but the production system requires a period of adaptation at start-up. This phenomenon, known as the learning effect, refers to the gradual improvement in efficiency through task repetition, thereby reducing task duration. However, the stability of an assembly line is also a critical factor, as any change in the production process incurs costs. In this study, Constraint Programming (CP) is used to optimise assembly line balancing, taking into account the learning effect to address the trade-off between achieving target production rates and minimising adjustments to the line.
Cite as

Duc Anh Le, Stéphanie Roussel, and Christophe Lecoutre. Aircraft Resource-Constrained Assembly Line Balancing with Learning Effect: A Constraint Programming Approach. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 25:1-25:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{le_et_al:LIPIcs.CP.2025.25,
  author =	{Le, Duc Anh and Roussel, St\'{e}phanie and Lecoutre, Christophe},
  title =	{{Aircraft Resource-Constrained Assembly Line Balancing with Learning Effect: A Constraint Programming Approach}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{25:1--25:24},
  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.2025.25},
  URN =		{urn:nbn:de:0030-drops-238863},
  doi =		{10.4230/LIPIcs.CP.2025.25},
  annote =	{Keywords: Assembly Line Balancing, Resource-Constrained, Learning Effect, Ramp-Up, Aeronautic, Constraint Programming, Dominance-Breaking}
}
Document
DOI: 10.4230/LIPIcs.CP.2024.18
Learning Effect and Compound Activities in High Multiplicity RCPSP: Application to Satellite Production

Authors: Duc Anh Le, Stéphanie Roussel, Christophe Lecoutre, and Anouck Chan

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

Abstract
This paper addresses the High Multiplicity Resource-Constrained Project Scheduling Problem (HM-RCPSP), in which multiple projects are performed iteratively while sharing limited resources. We extend this problem by integrating the learning effect, which makes the duration of some activities decrease when they are repeated. Learning effect can be represented by any decreasing function, allowing us to get flexibility in modeling various scenarios. Additionally, we take composition of activities into consideration for reasoning about precedence and resources in a more abstract way. A Constraint Programming model is proposed for this richer problem, including a symmetry-breaking technique applied to some activities. We also present a heuristic-based search strategy. The effectiveness of these solving approaches is evaluated through an experimentation conducted on data concerning real-world satellite assembly lines, as well as on some adapted literature benchmarks. Obtained results demonstrate that our methods serve as robust baselines for addressing this novel problem (denoted by HM-RCPSP/L-C).
Cite as

Duc Anh Le, Stéphanie Roussel, Christophe Lecoutre, and Anouck Chan. Learning Effect and Compound Activities in High Multiplicity RCPSP: Application to Satellite Production. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 18:1-18:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{le_et_al:LIPIcs.CP.2024.18,
  author =	{Le, Duc Anh and Roussel, St\'{e}phanie and Lecoutre, Christophe and Chan, Anouck},
  title =	{{Learning Effect and Compound Activities in High Multiplicity RCPSP: Application to Satellite Production}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{18:1--18:25},
  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.2024.18},
  URN =		{urn:nbn:de:0030-drops-207037},
  doi =		{10.4230/LIPIcs.CP.2024.18},
  annote =	{Keywords: High-multiplicity Project Scheduling, Learning Effect, Compound Activities, Satellite Assembly Line, Constraint Programming, Symmetry Breaking}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.9
Guiding Backtrack Search by Tracking Variables During Constraint Propagation

Authors: Gilles Audemard, Christophe Lecoutre, and Charles Prud'homme

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

Abstract
It is well-known that variable ordering heuristics play a central role in solving efficiently Constraint Satisfaction Problem (CSP) instances. From the early 80’s, and during more than two decades, the dynamic variable ordering heuristic selecting the variable with the smallest domain was clearly prevailing. Then, from the mid 2000’s, some adaptive heuristics have been introduced: their principle is to collect some useful information during the search process in order to take better informed decisions. Among those adaptive heuristics, wdeg/dom (and its variants) remains particularly robust. In this paper, we introduce an original heuristic based on the midway processing of failing executions of constraint propagation: this heuristic called pick/dom tracks the variables that are directly involved in the process of constraint propagation, when ending with a conflict. The robustness of this new heuristic is demonstrated from a large experimentation conducted with the constraint solver ACE. Interestingly enough, one can observe some complementary between the early, midway and late forms of processing of conflicts.
Cite as

Gilles Audemard, Christophe Lecoutre, and Charles Prud'homme. Guiding Backtrack Search by Tracking Variables During Constraint Propagation. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 9:1-9:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{audemard_et_al:LIPIcs.CP.2023.9,
  author =	{Audemard, Gilles and Lecoutre, Christophe and Prud'homme, Charles},
  title =	{{Guiding Backtrack Search by Tracking Variables During Constraint Propagation}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{9:1--9:17},
  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.2023.9},
  URN =		{urn:nbn:de:0030-drops-190461},
  doi =		{10.4230/LIPIcs.CP.2023.9},
  annote =	{Keywords: Variable Ordering Heuristics, Variable Weighting}
}
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