2 Search Results for "Arbelaez, Alejandro"

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.
Cite as

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.2022.33
Large Neighborhood Search for Robust Solutions for Constraint Satisfaction Problems with Ordered Domains

Authors: Jheisson López, Alejandro Arbelaez, and Laura Climent

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

Abstract
Often, real-world Constraint Satisfaction Problems (CSPs) are subject to uncertainty/dynamism not known in advance. Some techniques in the literature offer robust solutions for CSPs. Here, we analyze a previous exact/complete approach from the state-of-the-art that focuses on CSPs with ordered domains and dynamic bounds. However, this approach has low performance in large-scale CSPs. For this reason, in this paper, we present an inexact/incomplete approach that is faster at finding robust solutions for large-scale CSPs. It is useful when the computation time available for finding a solution is limited and/or in situations where a new one must be re-computed online because the dynamism invalidated the original one. Specifically, we present a Large Neighbourhood Search (LNS) algorithm combined with Constraint Programming (CP) and Branch-and-bound (B&B) that searches for robust solutions. We also present a robust-value selection heuristic that guides the search toward more promising branches. We evaluate our approach with large-scale CSPs instances, including the case study of scheduling problems. The evaluation shows a considerable improvement in the robustness of the solutions achieved by our algorithm for large-scale CSPs.
Cite as

Jheisson López, Alejandro Arbelaez, and Laura Climent. Large Neighborhood Search for Robust Solutions for Constraint Satisfaction Problems with Ordered Domains. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 33:1-33:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{lopez_et_al:LIPIcs.CP.2022.33,
  author =	{L\'{o}pez, Jheisson and Arbelaez, Alejandro and Climent, Laura},
  title =	{{Large Neighborhood Search for Robust Solutions for Constraint Satisfaction Problems with Ordered Domains}},
  booktitle =	{28th International Conference on Principles and Practice of Constraint Programming (CP 2022)},
  pages =	{33:1--33:16},
  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.2022.33},
  URN =		{urn:nbn:de:0030-drops-166625},
  doi =		{10.4230/LIPIcs.CP.2022.33},
  annote =	{Keywords: Constraint Programming, Large Neighbourhood Search, Robust Solutions}
}
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