4 Search Results for "Belov, Gleb"

Document
DOI: 10.4230/LIPIcs.CP.2025.33
Constraint-Based In-Station Train Dispatching

Authors: Andreas Schutt, Matteo Cardellini, Jip J. Dekker, Daniel Harabor, Marco Maratea, and Mauro Vallati

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

Abstract
In-station dispatching is the problem of planning the movements of scheduled trains inside a railway station. Effective solutions for in-station dispatching are important for maximising the utilisation of railway infrastructure and for mitigating the impact of incidents and delays in the broader network. In this paper, we explore a constraint-based approach to perform in-station train dispatching. Our extensive empirical analysis of multiple modelling, search strategy, and solver choices, performed over synthetically generated, yet realistic, data, shows that our method outperforms the existing planning-based state-of-the-art approach. In addition, we present different optimisation criteria, which can be effortless defined thanks to the constraint-based approach.
Cite as

Andreas Schutt, Matteo Cardellini, Jip J. Dekker, Daniel Harabor, Marco Maratea, and Mauro Vallati. Constraint-Based In-Station Train Dispatching. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 33:1-33:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{schutt_et_al:LIPIcs.CP.2025.33,
  author =	{Schutt, Andreas and Cardellini, Matteo and Dekker, Jip J. and Harabor, Daniel and Maratea, Marco and Vallati, Mauro},
  title =	{{Constraint-Based In-Station Train Dispatching}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{33:1--33:24},
  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.2025.33},
  URN =		{urn:nbn:de:0030-drops-238941},
  doi =		{10.4230/LIPIcs.CP.2025.33},
  annote =	{Keywords: in-station train dispatching, train scheduling, railway scheduling, constraint programming, mixed-integer programming}
}
Document
DOI: 10.4230/LIPIcs.CP.2025.4
Conflict Analysis Based on Cutting-Planes for Constraint Programming

Authors: Robbin Baauw, Maarten Flippo, and Emir Demirović

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

Abstract
This paper introduces a novel constraint learning mechanism for Constraint Programming (CP) solvers that integrates cutting planes reasoning into the conflict analysis procedure. Drawing inspiration from Lazy Clause Generation (LCG), our approach, named Lazy Linear Generation (LLG), can generate linear integer inequalities to prune the search space, rather than propositional clauses as in LCG. This combines the strengths of constraint programming (strong propagation through global constraints) with cutting-planes reasoning. We present linear constraint explanations for various arithmetic constraints and the element constraint. An experimental evaluation shows that the improved generality of linear constraints has a practical impact on a CP solver by reducing the number of encountered conflicts in 45% of our benchmark instances. Our analysis and prototype implementation show promising results and are an important step towards a new paradigm to make constraint programming solvers more effective.
Cite as

Robbin Baauw, Maarten Flippo, and Emir Demirović. Conflict Analysis Based on Cutting-Planes for Constraint Programming. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 4:1-4:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{baauw_et_al:LIPIcs.CP.2025.4,
  author =	{Baauw, Robbin and Flippo, Maarten and Demirovi\'{c}, Emir},
  title =	{{Conflict Analysis Based on Cutting-Planes for Constraint Programming}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{4:1--4:19},
  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.2025.4},
  URN =		{urn:nbn:de:0030-drops-238655},
  doi =		{10.4230/LIPIcs.CP.2025.4},
  annote =	{Keywords: constraint programming, learning, conflict analysis}
}
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.2021.49
Human-Centred Feasibility Restoration

Authors: Ilankaikone Senthooran, Matthias Klapperstueck, Gleb Belov, Tobias Czauderna, Kevin Leo, Mark Wallace, Michael Wybrow, and Maria Garcia de la Banda

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

Abstract
Decision systems for solving real-world combinatorial problems must be able to report infeasibility in such a way that users can understand the reasons behind it, and understand how to modify the problem to restore feasibility. Current methods mainly focus on reporting one or more subsets of the problem constraints that cause infeasibility. Methods that also show users how to restore feasibility tend to be less flexible and/or problem-dependent. We describe a problem-independent approach to feasibility restoration that combines existing techniques from the literature in novel ways to yield meaningful, useful, practical and flexible user support. We evaluate the resulting framework on two real-world applications.
Cite as

Ilankaikone Senthooran, Matthias Klapperstueck, Gleb Belov, Tobias Czauderna, Kevin Leo, Mark Wallace, Michael Wybrow, and Maria Garcia de la Banda. Human-Centred Feasibility Restoration. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 210, pp. 49:1-49:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

Copy BibTex To Clipboard

@InProceedings{senthooran_et_al:LIPIcs.CP.2021.49,
  author =	{Senthooran, Ilankaikone and Klapperstueck, Matthias and Belov, Gleb and Czauderna, Tobias and Leo, Kevin and Wallace, Mark and Wybrow, Michael and de la Banda, Maria Garcia},
  title =	{{Human-Centred Feasibility Restoration}},
  booktitle =	{27th International Conference on Principles and Practice of Constraint Programming (CP 2021)},
  pages =	{49:1--49:18},
  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.2021.49},
  URN =		{urn:nbn:de:0030-drops-153408},
  doi =		{10.4230/LIPIcs.CP.2021.49},
  annote =	{Keywords: Combinatorial optimisation, modelling, human-centred, conflict resolution, feasibility restoration, explainable AI, soft constraints}
}
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