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An Efficient Constraint Programming Approach to Preemptive Job Shop Scheduling

Authors Carla Juvin, Emmanuel Hebrard , Laurent Houssin , Pierre Lopez

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Author Details

Carla Juvin
  • LAAS-CNRS, Université de Toulouse, France
Emmanuel Hebrard
  • LAAS-CNRS, Université de Toulouse, France
Laurent Houssin
  • ISAE-SUPAERO, Université de Toulouse, France
Pierre Lopez
  • LAAS-CNRS, Université de Toulouse, France

Funding

  • Hebrard, Emmanuel: ANR Project AD-Lib (ANR-22-CE23-0014).

Acknowledgements

We would like to thank Claude-Guy Quimper for the advice and discussions while writing this paper.

Cite AsGet BibTex

Carla Juvin, Emmanuel Hebrard, Laurent Houssin, and Pierre Lopez. An Efficient Constraint Programming Approach to Preemptive Job Shop Scheduling. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 19:1-19:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CP.2023.19

Abstract

Constraint Programming has been widely, and very successfully, applied to scheduling problems. However, the focus has been on uninterruptible tasks, and preemptive scheduling problems are typically harder for existing constraint solvers. Indeed, one usually needs to represent all potential task interruptions thus introducing many variables and symmetrical or dominated choices. In this paper, building on mostly known results, we observe that a large class of preemptive disjunctive scheduling problems do not require an explicit model of task interruptions. We then introduce a new constraint programming approach for this class of problems that significantly outperforms state-of-the-art dedicated approaches in our experimental results.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Planning and scheduling
Keywords
  • Constraint Programming
  • Scheduling
  • Preemptive Resources

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