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From CP Modeling to Preference Elicitation in HMLV Assembly Problems

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

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LIPIcs.CP.2026.25.pdf
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Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Active learning settings
Keywords
  • Job Shop Scheduling Problem
  • Multi-objective
  • Preference Elicitation

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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 Get BibTex

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) https://doi.org/10.4230/LIPIcs.CP.2026.25

Author Details

Marco Foschini
  • DTAI, KU Leuven, Belgium
Emilio Gamba
  • Productions, Flanders Make VZW, Lommel, Belgium
Lucas Kletzander
  • DBAI, TU Wien, Austria
Tias Guns
  • DTAI, KU Leuven, Belgium

Funding

This research received funding from Flemish Goverment under "Onderzoeksprogramma Artificiële Intellgentie (AI) Vlaanderen" (https://www.flandersairesearch.be/en), Flanders Make (https://www.flandersmake.be/en) and partly funded by the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (Grant No 101002802, "CHAT-Opt")

Supplementary Materials

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