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Documents authored by de Nijs, Frits

Document
DOI: 10.4230/LIPIcs.CP.2026.34
CrewAId: Interactive Optimisation for Human-In-The-Loop Crew Rostering and Rerostering

Authors: Matthias Klapperstueck, Frits de Nijs, Ilankaikone Senthooran, Matteo Miceli, and Michael Wybrow

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

Abstract
Constraint programming technology allows optimisation experts to solve a broad category of personnel rostering problems, such as nurse rostering, airline crew rostering or retail worker scheduling. However, for problem domain experts to use this technology, the optimisation system must bridge the gap for users to easily explore solutions and influence constraints. Working with our energy industry partner for several years, we identified rostering problems involving multi-skilled shift workers present on site for extended periods. Their existing workflow for handling rostering (crew allocation), and rerostering (dealing with inevitable employee absences) and for time-limited formation of dedicated maintenance crews is labour intensive and complex, requiring in-depth knowledge of personnel files and skill competencies. To address this, we propose an interactive decision support system for crew rostering and rerostering, currently being deployed by our industry partner, that provides interactive tools for domain experts to perform exploration, validation, and conflict recovery.
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Matthias Klapperstueck, Frits de Nijs, Ilankaikone Senthooran, Matteo Miceli, and Michael Wybrow. CrewAId: Interactive Optimisation for Human-In-The-Loop Crew Rostering and Rerostering. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 34:1-34:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{klapperstueck_et_al:LIPIcs.CP.2026.34,
  author =	{Klapperstueck, Matthias and de Nijs, Frits and Senthooran, Ilankaikone and Miceli, Matteo and Wybrow, Michael},
  title =	{{CrewAId: Interactive Optimisation for Human-In-The-Loop Crew Rostering and Rerostering}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{34:1--34:23},
  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.2026.34},
  URN =		{urn:nbn:de:0030-drops-266666},
  doi =		{10.4230/LIPIcs.CP.2026.34},
  annote =	{Keywords: Crew Rostering, Rerostering, Constraint Programming, Human-Centric Optimisation}
}
Document
DOI: 10.4230/LIPIcs.CP.2026.61
Constraint-Aware Self-Supervised Learning for Edge Selection

Authors: Xinda Zheng, Frits de Nijs, and Edward Lam

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

Abstract
Many edge-selection problems, such as the Traveling Salesman Problem and Orienteering Problem, are NP-hard, making them expensive to solve with exact methods and challenging to address with hand-crafted heuristics. Learning-based approaches provide an efficient alternative, while self-supervised methods avoid costly solution labels. However, existing approaches often still rely on heavy post-processing or narrow problem-specific designs. We propose a reusable self-supervised framework for edge-selection optimization that learns directly from unlabeled instances. The framework uses differentiable surrogate objectives and feasibility-driven penalties to encourage the model to learn feasibility-aware solution structure during training. To support efficient inference, we introduce a lightweight graph architecture centered on a cost-attention convolution, where edge costs and feasibility information directly shape message passing. Experiments on three problem families demonstrate strong solution quality and efficient inference across diverse edge-selection settings.
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Xinda Zheng, Frits de Nijs, and Edward Lam. Constraint-Aware Self-Supervised Learning for Edge Selection. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 61:1-61:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{zheng_et_al:LIPIcs.CP.2026.61,
  author =	{Zheng, Xinda and de Nijs, Frits and Lam, Edward},
  title =	{{Constraint-Aware Self-Supervised Learning for Edge Selection}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{61:1--61:18},
  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.2026.61},
  URN =		{urn:nbn:de:0030-drops-266949},
  doi =		{10.4230/LIPIcs.CP.2026.61},
  annote =	{Keywords: Combinatorial Optimization, Learning to optimize, Graph neural networks}
}
Document
DOI: 10.4230/LIPIcs.CP.2023.21
Exploring Hydrogen Supply/Demand Networks: Modeller and Domain Expert Views

Authors: Matthias Klapperstueck, Frits de Nijs, Ilankaikone Senthooran, Jack Lee-Kopij, Maria Garcia de la Banda, and Michael Wybrow

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

Abstract
Energy companies are considering producing renewable fuels such as hydrogen/ammonia. Setting up a production network means deciding where to build production plants, and how to operate them at minimum electricity and transport costs. These decisions are complicated by many factors including the difficulty in obtaining accurate current data (e.g., electricity price and transport costs) for potential supply locations, the accuracy of data predictions (e.g., for demand and costs), and the need for some decisions to be made due to external (not modelled) factors. Thus, decision-makers need access to a user-centric decision system that helps them visualise, explore, interact and compare the many possible solutions of many different scenarios. This paper describes the system we have built to support our energy partner in making such decisions, and shows the advantages of having a graphical user-focused interactive tool, and of using a high-level constraint modelling language (MiniZinc) to implement the underlying model.
Cite as

Matthias Klapperstueck, Frits de Nijs, Ilankaikone Senthooran, Jack Lee-Kopij, Maria Garcia de la Banda, and Michael Wybrow. Exploring Hydrogen Supply/Demand Networks: Modeller and Domain Expert Views. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 21:1-21:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{klapperstueck_et_al:LIPIcs.CP.2023.21,
  author =	{Klapperstueck, Matthias and de Nijs, Frits and Senthooran, Ilankaikone and Lee-Kopij, Jack and Garcia de la Banda, Maria and Wybrow, Michael},
  title =	{{Exploring Hydrogen Supply/Demand Networks: Modeller and Domain Expert Views}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{21:1--21:18},
  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.2023.21},
  URN =		{urn:nbn:de:0030-drops-190584},
  doi =		{10.4230/LIPIcs.CP.2023.21},
  annote =	{Keywords: Facility Location, Hydrogen Supply Chain, Human-Centric Optimisation}
}
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