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Constraint Programming for Mixed-Model Assembly Line Scheduling with Complex Industrial Constraints

Authors Guillaume Povéda , Javier Buil Tejero, Tamara Borreguero Sanchidrian

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Planning and scheduling
  • Theory of computation → Constraint and logic programming
  • Applied computing → Industry and manufacturing
Keywords
  • modeling
  • scheduling
  • heuristics
  • multi-objective

Metrics

Abstract

The aeronautical industry transitioned in the 90s to takt-paced, product-specific assembly lines. The current trend of increased customization and demand variability is pushing for a transition to flexible mixed-model assembly lines. We address a mid-term planning problem for an airframe assembly plant, modeled as a Resource-Constrained Project Scheduling Problem (RCPSP) with complex industrial constraints. This formulation serves a dual purpose: facilitating high-level production planning and validating plant designs, particularly during ramp-up scenarios. We specifically tackle challenges involving calendar-based preemption, variable resource capacity, and resource blocking between task groups. We propose a Constraint Programming (CP) formulation that optimizes conflicting objectives, including Tardiness and Just-in-Time costs. To ensure scalability for large industrial instances, we introduce a sequential solving method based on topological decomposition. The proposed approach proves effective in handling complex scenarios, acting as a foundation for more realistic models.

Cite As Get BibTex

Guillaume Povéda, Javier Buil Tejero, and Tamara Borreguero Sanchidrian. Constraint Programming for Mixed-Model Assembly Line Scheduling with Complex Industrial Constraints. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 46:1-46:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.46

Author Details

Guillaume Povéda
  • Airbus SAS, Toulouse, France
Javier Buil Tejero
  • ALTEN GmbH, Manching, Germany
Tamara Borreguero Sanchidrian
  • Airbus DS, Getafe, Spain

Funding

Our work has benefitted from the AI Interdisciplinary Institute ANITI. ANITI is funded by the French "Investing for the Future – PIA3" program under the Grant agreement n°ANR-23-IACL-0002.

Supplementary Materials

References

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