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Revisiting Optional Variables in Lazy Clause Generation Solvers for Flexible Scheduling

Author Arthur Bit-Monnot

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LIPIcs.CP.2026.7.pdf
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Subject Classification

ACM Subject Classification
  • Computing methodologies → Discrete space search
Keywords
  • Constraint Programming
  • Scheduling
  • Lazy-Clause Generation
  • Optional Variables

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Abstract

Optional variables have for a long time been identified as a crucial component of state-of-the-art scheduling solvers. They not only offer a natural way to model many scheduling problems but also play an important role in the efficiency of such solvers by enabling a stronger inference in a constraint propagation engine. Despite this recognition, their adoption in modern solvers remains extremely scarce presumably because their native support requires pervasive changes to a solver and handling many subtleties that have not been extensively studied.
In this paper, we aim to provide a good foundation in this direction by providing a formal characterization of optional variables and of their interactions with key components of a constraint programming solver including propagators, reification and lazy clause generation. In addition, we present a new lazy clause generation solver with native support for optional variables and demonstrate its efficiency on variants of the flexible jobshop problem.

Cite As Get BibTex

Arthur Bit-Monnot. Revisiting Optional Variables in Lazy Clause Generation Solvers for Flexible Scheduling. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 7:1-7:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.7

Author Details

Arthur Bit-Monnot
  • LAAS-CNRS, Université de Toulouse, INSA, CNRS, France

Funding

This research was partly funded by the Agence Nationale de la Recherche (ANR) under project HumFleet (ANR-23-CE33-0003-01) and benefited from the AI Interdisciplinary Institute ANITI, funded by the France 2030 program under the Grant agreement n°ANR-23-IACL-0002.

Supplementary Materials

References

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