An Optimal Constraint Programming Approach to the Open-Shop Problem

Authors

  • Arnaud Malapert University Nice Sophia Antipolis
  • Hadrien Cambazard Grenoble Institute of Technology
  • Christelle Guéret University of Angers
  • Narendra Jussien École des Mines de Nantes INRIA TASC UMR CNRS 6241
  • André Langevin École Polytechnique de Montréal
  • Louis-Martin Rousseau École Polyechnique de Montréal

DOI:

https://doi.org/10.1609/icaps.v23i1.13575

Keywords:

Production-Scheduling, Open shop, Artificial Intelligence, Constraint Programming, Randomization and Restart

Abstract

This is a summary of the journal article published by Journal on Computing entitled "An Optimal Constraint Programming Approach to the Open-Shop Problem." The article presents an optimal constraint programming approach for the Open-Shop scheduling problem, which integrates recent constraint propagation and branching techniques with new upper bound heuristics. Randomized restart policies combined with nogood recording allow to search diversification and learning from restarts. This approach is compared with the best-known metaheuristics and exact algorithms, and shows better results on a wide range of benchmark instances.

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Published

2013-06-02

How to Cite

Malapert, A., Cambazard, H., Guéret, C., Jussien, N., Langevin, A., & Rousseau, L.-M. (2013). An Optimal Constraint Programming Approach to the Open-Shop Problem. Proceedings of the International Conference on Automated Planning and Scheduling, 23(1), 478-479. https://doi.org/10.1609/icaps.v23i1.13575

Issue

Vol. 23 (2013): Twenty-Third International Conference on Automated Planning and Scheduling

Section

Journal Presentation Track