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ML-KULeuven/HMLV

Authors: Marco Foschini, Emilio Gamba, Lucas Kletzander, and Tias Guns

Abstract

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Marco Foschini, Emilio Gamba, Lucas Kletzander, Tias Guns. ML-KULeuven/HMLV (Software, Source Code & Data). Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@misc{dagstuhl-artifact-26899,
   title = {{ML-KULeuven/HMLV}}, 
   author = {Foschini, Marco and Gamba, Emilio and Kletzander, Lucas and Guns, Tias},
   note = {Software (visited on 2026-07-13)},
   url = {https://github.com/ML-KULeuven/HMLV},
   doi = {10.4230/artifacts.26899},
}

Document

DOI: 10.4230/LIPIcs.CP.2026.25

From CP Modeling to Preference Elicitation in HMLV Assembly Problems

Authors: Marco Foschini, Emilio Gamba, Lucas Kletzander, and Tias Guns

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

Abstract

High Mix Low Volume (HMLV) assembly problems involve producing a variety of items in small quantities, each of which requires scheduling a sequence of actions performed by machines or human operators. For the production process, companies are increasingly adopting reconfigurable manufacturing systems (RMS) where they choose which machines to deploy. Importantly, the selection of machines can substantially influence overall production time. For this reason, we present a CP model for solving HMLV for RMS. However, solely minimizing makespan does not necessarily yield the most desirable solution from a managerial perspective. For example, it may heavily rely on human operators. Since determining preferred solutions is challenging, incorporating Decision Maker (DM) feedback becomes essential. Therefore, to support DMs in selecting solutions that better reflect their preferences, we adapt pairwise preference elicitation methods for this industrial multi-objective combinatorial problem, while also comparing with trade-off-based methods.

Cite as

Marco Foschini, Emilio Gamba, Lucas Kletzander, and Tias Guns. From CP Modeling to Preference Elicitation in HMLV Assembly Problems. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 25:1-25:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{foschini_et_al:LIPIcs.CP.2026.25,
  author =	{Foschini, Marco and Gamba, Emilio and Kletzander, Lucas and Guns, Tias},
  title =	{{From CP Modeling to Preference Elicitation in HMLV Assembly Problems}},
  booktitle =	{32nd International Conference on Principles and Practice of Constraint Programming (CP 2026)},
  pages =	{25:1--25:20},
  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.2026.25},
  URN =		{urn:nbn:de:0030-drops-266579},
  doi =		{10.4230/LIPIcs.CP.2026.25},
  annote =	{Keywords: Job Shop Scheduling Problem, Multi-objective, Preference Elicitation}
}

Document

DOI: 10.4230/LIPIcs.CP.2023.11

Simplifying Step-Wise Explanation Sequences

Authors: Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, and Tias Guns

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

Abstract

Explaining constraint programs is useful for debugging an unsatisfiable program, to understand why a given solution is optimal, or to understand how to find a unique solution. A recently proposed framework for explaining constraint programs works well to explain the unique solution to a problem step by step. It can also be used to step-wise explain why a model is unsatisfiable, but this may create redundant steps and introduce superfluous information into the explanation sequence. This paper proposes methods to simplify a (step-wise) explanation sequence, to generate simple steps that together form a short, interpretable sequence. We propose an algorithm to greedily construct an initial sequence and two filtering algorithms that eliminate redundant steps and unnecessarily complex parts of explanation sequences. Experiments on diverse benchmark instances show that our techniques can significantly simplify step-wise explanation sequences.

Cite as

Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, and Tias Guns. Simplifying Step-Wise Explanation Sequences. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{bleukx_et_al:LIPIcs.CP.2023.11,
  author =	{Bleukx, Ignace and Devriendt, Jo and Gamba, Emilio and Bogaerts, Bart and Guns, Tias},
  title =	{{Simplifying Step-Wise Explanation Sequences}},
  booktitle =	{29th International Conference on Principles and Practice of Constraint Programming (CP 2023)},
  pages =	{11:1--11:20},
  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.2023.11},
  URN =		{urn:nbn:de:0030-drops-190489},
  doi =		{10.4230/LIPIcs.CP.2023.11},
  annote =	{Keywords: explanation, deduction, constraint programming, propagation}
}

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Documents authored by Gamba, Emilio

ML-KULeuven/HMLV

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From CP Modeling to Preference Elicitation in HMLV Assembly Problems

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Simplifying Step-Wise Explanation Sequences

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