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Documents authored by Povéda, Guillaume

Artifact
Software
DOI: 10.4230/artifacts.26935
airbus/discrete-optimization

Authors: Guillaume Poveda

Abstract
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Guillaume Poveda. airbus/discrete-optimization (Software, Library). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@misc{dagstuhl-artifact-26935,
   title = {{airbus/discrete-optimization}}, 
   author = {Poveda, Guillaume},
   note = {Software, Our work has benefitted from the AI Interdisciplinary Institute ANITI. ANITI is funded by the French "Investing for the Future – PIA3" program under the Grant agreement n°ANR-23-IACL-0002. (visited on 2026-07-13)},
   url = {https://github.com/airbus/discrete-optimization/},
   doi = {10.4230/artifacts.26935},
}
Document
DOI: 10.4230/LIPIcs.CP.2026.20
Scaling Industrial Logistics: Tackling Multi-Batching Problems via Sequential Solving

Authors: Emmanuelle Dietz, Guillaume Povéda, Karl Henning, and Clara Buire

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

Abstract
Logistic optimization frequently involves complex routing decisions bound by tight numerical constraints such as vehicle capacities. This paper addresses a real-world industrial multi-batching problem where products must be routed between distributed sites. The objective is to determine optimal routes, travel frequencies, and packing configurations at minimum cost. The problem corresponds to a minimum cost flow problem coupled a bin packing problem. We investigate direct formalizations, decompositions, and scalable sequential approaches across three base technologies: Mixed-Integer Linear Programming, Constraint Programming, and Constraint Answer Set Programming. Our contributions are threefold: we propose a direct formalization of the problem, additional distinct approaches that scale for an industrial use case, and finally an empirical evaluation. By comparing these approaches we highlight the most effective configurations. Results suggests that a three-step approach provides the best results: combining MILP for flow routing, a greedy bin packing and CP for refinement.
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Emmanuelle Dietz, Guillaume Povéda, Karl Henning, and Clara Buire. Scaling Industrial Logistics: Tackling Multi-Batching Problems via Sequential Solving. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 20:1-20:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{dietz_et_al:LIPIcs.CP.2026.20,
  author =	{Dietz, Emmanuelle and Pov\'{e}da, Guillaume and Henning, Karl and Buire, Clara},
  title =	{{Scaling Industrial Logistics: Tackling Multi-Batching Problems via Sequential Solving}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{20:1--20:16},
  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.2026.20},
  URN =		{urn:nbn:de:0030-drops-266534},
  doi =		{10.4230/LIPIcs.CP.2026.20},
  annote =	{Keywords: Mixed Integer Linear Programming, Constraint Programming, Discrete Optimization, Declarative Problem Solving, Industrial Logistics, Scalability, Constraint Answer Set Programming}
}
Document
DOI: 10.4230/LIPIcs.CP.2026.46
Constraint Programming for Mixed-Model Assembly Line Scheduling with Complex Industrial Constraints

Authors: Guillaume Povéda, Javier Buil Tejero, and Tamara Borreguero Sanchidrian

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

Abstract
The aeronautical industry transitioned in the 90s to takt-paced, product-specific assembly lines. The current trend of increased customization and demand variability is pushing for a transition to flexible mixed-model assembly lines. We address a mid-term planning problem for an airframe assembly plant, modeled as a Resource-Constrained Project Scheduling Problem (RCPSP) with complex industrial constraints. This formulation serves a dual purpose: facilitating high-level production planning and validating plant designs, particularly during ramp-up scenarios. We specifically tackle challenges involving calendar-based preemption, variable resource capacity, and resource blocking between task groups. We propose a Constraint Programming (CP) formulation that optimizes conflicting objectives, including Tardiness and Just-in-Time costs. To ensure scalability for large industrial instances, we introduce a sequential solving method based on topological decomposition. The proposed approach proves effective in handling complex scenarios, acting as a foundation for more realistic models.
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Guillaume Povéda, Javier Buil Tejero, and Tamara Borreguero Sanchidrian. Constraint Programming for Mixed-Model Assembly Line Scheduling with Complex Industrial Constraints. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 46:1-46:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{poveda_et_al:LIPIcs.CP.2026.46,
  author =	{Pov\'{e}da, Guillaume and Tejero, Javier Buil and Sanchidrian, Tamara Borreguero},
  title =	{{Constraint Programming for Mixed-Model Assembly Line Scheduling with Complex Industrial Constraints}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{46:1--46:21},
  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.2026.46},
  URN =		{urn:nbn:de:0030-drops-266793},
  doi =		{10.4230/LIPIcs.CP.2026.46},
  annote =	{Keywords: modeling, scheduling, heuristics, multi-objective}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.6
Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem

Authors: Ignace Bleukx, Ryma Boumazouza, Tias Guns, Nadine Laage, and Guillaume Poveda

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

Abstract
We present an industrial case on workforce allocation and scheduling in the aircraft manufacturing industry, where available teams need to be assigned to logistical operations. This application presents several challenges such as the scale of the problem, the need for fair workload distribution, and the need for methods for mitigating unforeseen disruptions due to technical malfunctions or incompatible weather conditions. We compare different Constraint Programming (CP) models for the allocation and scheduling problems, with extra focus on modeling the workload balancing component. Additionally, we investigate different techniques for explaining infeasibility of a disrupted schedule, such as conflict computation using Minimal Unsatisfiable Subsets (MUSes) and feasibility restoration using Minimal Correction Subsets (MCSes) or constraint relaxations. Our experimental results show that by using appropriate modeling techniques, the problem can be solved in reasonable time, thereby producing fair schedules. Additionally, we show how invalidated schedules can be explained and restored efficiently to help human operators in solving disruptions to the schedule.
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Ignace Bleukx, Ryma Boumazouza, Tias Guns, Nadine Laage, and Guillaume Poveda. Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 6:1-6:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{bleukx_et_al:LIPIcs.CP.2025.6,
  author =	{Bleukx, Ignace and Boumazouza, Ryma and Guns, Tias and Laage, Nadine and Poveda, Guillaume},
  title =	{{Modeling and Explaining an Industrial Workforce Allocation and Scheduling Problem}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{6:1--6:24},
  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.2025.6},
  URN =		{urn:nbn:de:0030-drops-238670},
  doi =		{10.4230/LIPIcs.CP.2025.6},
  annote =	{Keywords: modeling, scheduling, fairness, explanations, feasibility restoration}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.31
Partially Preemptive Multi Skill/Mode Resource-Constrained Project Scheduling with Generalized Precedence Relations and Calendars

Authors: Guillaume Povéda, Nahum Alvarez, and Christian Artigues

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

Abstract
Multi skill resource-constrained project scheduling Problems (MS-RCPSP) have been object of studies from many years. Also, preemption is an important feature of real-life scheduling models. However, very little research has been investigated concerning MS-RCPSPs including preemption, and even less research moving out from academic benchmarks to real problem solving. In this paper we present a solution to those problems based on a hybrid method derived from large neighborhood search incorporating constraint programming components tailored to deal with complex scheduling constraints. We also present a constraint programming model adapted to preemption. The methods are implemented in a new open source python library allowing to easily reuse existing modeling languages and solvers. We evaluate the methods on an industrial case study from aircraft manufacturing including additional complicating constraints such as generalized precedence relations, resource calendars and partial preemption on which the standard CP Optimizer solver, even with the preemption-specific model, is unable to provide solutions in reasonable times. The large neighborhood search method is also able to find new best solutions on standard multi-skill project scheduling instances, performing better than a reference method from the literature.
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Guillaume Povéda, Nahum Alvarez, and Christian Artigues. Partially Preemptive Multi Skill/Mode Resource-Constrained Project Scheduling with Generalized Precedence Relations and Calendars. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 31:1-31:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{poveda_et_al:LIPIcs.CP.2023.31,
  author =	{Pov\'{e}da, Guillaume and Alvarez, Nahum and Artigues, Christian},
  title =	{{Partially Preemptive Multi Skill/Mode Resource-Constrained Project Scheduling with Generalized Precedence Relations and Calendars}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{31:1--31:21},
  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.2023.31},
  URN =		{urn:nbn:de:0030-drops-190689},
  doi =		{10.4230/LIPIcs.CP.2023.31},
  annote =	{Keywords: Large-scale scheduling problem, partial preemption, multi-skill, multi-mode, resource calendars, constraint programming, large neighborhood search}
}
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