Large Neighborhood Search for Robust Solutions for Constraint Satisfaction Problems with Ordered Domains

Authors Jheisson López , Alejandro Arbelaez , Laura Climent



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Author Details

Jheisson López
  • University College Cork, School of Computer Science, Ireland
  • SFI Centre for Research Training in Artificial Intelligence, Cork, Ireland
Alejandro Arbelaez
  • Department of Computer Engineering, Autonomous University of Madrid, Spain
Laura Climent
  • Department of Computer Engineering, Autonomous University of Madrid, Spain

Acknowledgements

We thank Christophe Lecoutre for his assistance with the ACE solver.

Cite AsGet BibTex

Jheisson López, Alejandro Arbelaez, and Laura Climent. Large Neighborhood Search for Robust Solutions for Constraint Satisfaction Problems with Ordered Domains. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 33:1-33:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.CP.2022.33

Abstract

Often, real-world Constraint Satisfaction Problems (CSPs) are subject to uncertainty/dynamism not known in advance. Some techniques in the literature offer robust solutions for CSPs. Here, we analyze a previous exact/complete approach from the state-of-the-art that focuses on CSPs with ordered domains and dynamic bounds. However, this approach has low performance in large-scale CSPs. For this reason, in this paper, we present an inexact/incomplete approach that is faster at finding robust solutions for large-scale CSPs. It is useful when the computation time available for finding a solution is limited and/or in situations where a new one must be re-computed online because the dynamism invalidated the original one. Specifically, we present a Large Neighbourhood Search (LNS) algorithm combined with Constraint Programming (CP) and Branch-and-bound (B&B) that searches for robust solutions. We also present a robust-value selection heuristic that guides the search toward more promising branches. We evaluate our approach with large-scale CSPs instances, including the case study of scheduling problems. The evaluation shows a considerable improvement in the robustness of the solutions achieved by our algorithm for large-scale CSPs.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
Keywords
  • Constraint Programming
  • Large Neighbourhood Search
  • Robust Solutions

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References

  1. A. Charnes and W. W. Cooper. Chance-Constrained Programming. Management Science, 6(1):73-79, 1959. Google Scholar
  2. J. Christopher Beck, T. K. Feng, and Jean Paul Watson. Combining constraint programming and local search for job-shop scheduling. INFORMS Journal on Computing, 23(1):1-14, 2011. Google Scholar
  3. Aharon Ben-Tal, Laurent El Ghaoui, and Arkadi Nemirovski. Robust optimization. Princeton University Press, 2009. Google Scholar
  4. Frédéric Boussemart, Fred Hemery, Christophe Lecoutre, and Lakhdar Sais. Boosting systematic search by weighting constraints. Frontiers in Artificial Intelligence and Applications, 110:146-150, 2004. Google Scholar
  5. Tom Carchrae and J. Christopher Beck. Principles for the design of large neighborhood search. Journal of Mathematical Modelling and Algorithms, 8(3):245-270, 2009. Google Scholar
  6. Laura Climent, Richard J. Wallace, Barry O'Sullivan, and Eugene C. Freuder. Extrapolating from limited uncertain information in large-scale combinatorial optimization problems to obtain robust solutions. International Journal on Artificial Intelligence Tools, 25:1-21, 2016. In this paper a way to use extrapolation to enable Stochastic approach with limited knowledge is treated. Important: Ordered Domains. URL: https://doi.org/10.1142/S0218213016600058.
  7. Laura Climent, Richard J. Wallace, Miguel A. Salido, and Federico Barber. Robustness and stability in constraint programming under dynamism and uncertainty. Journal of Artificial Intelligence Research, 49:49-78, 2014. Google Scholar
  8. Thierry Denœux. Belief functions induced by random fuzzy sets: A general framework for representing uncertain and fuzzy evidence. Fuzzy Sets and Systems, 1:1-29, 2020. Google Scholar
  9. Ivan Dotu, Manuel Cebrián, Pascal Van Hentenryck, and Peter Clote. Protein Structure Prediction with Large Neighborhood Constraint Programming Search. In Peter J. Stuckey, editor, Principles and Practice of Constraint Programming, pages 82-96. Springer, 2008. Google Scholar
  10. Helene Fargier, Jerome Lang, and Thomas Schiex. Mixed constraint satisfaction: a framework for decision problems under incomplete knowledge. Proceedings of the National Conference on Artificial Intelligence, 1(January 1996):175-180, 1996. Google Scholar
  11. Markus P J Fromherz. Constraint-based scheduling. Proceedings of the American Control Conference, pages 3231-3244, 2001. Google Scholar
  12. Luca Di Gaspero, Andrea Rendl, and Tommaso Urli. Balancing bike sharing systems with constraint programming. Constraints, 21(2):318-348, 2016. Google Scholar
  13. Emmanuel Hebrard. Robust Solutions for Constraint Satisfaction and Optimisation under Uncertainty. PhD thesis, University of New South Wales, 2007. Google Scholar
  14. Michellgendreauuperiodcentered Jean-Yvesspotvin. Handbook of Metaheuristics. International Series in Operations Research & Management Science, third edit edition, 2010. Google Scholar
  15. Christophe Lecoutre and Sebastien Tabary. Abscon 112 : towards more robustness. In 3rd International Constraint Solver Competition, pages 41-48, 2013. Google Scholar
  16. Dario Pacino and Pascal Van Hentenryck. Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling. IJCAI International Joint Conference on Artificial Intelligence, pages 1997-2002, 2011. Google Scholar
  17. Laurent Perron, Paul Shaw, and Vincent Furnon. Propagation guided Large Neighbourhood Search. In Mark Wallace, editor, Principles and Practice of Constraint Programming, volume 3258, pages 469-481, Toronto, 2004. Springer. Google Scholar
  18. Paul Shaw. Using constraint programming and local search methods to solve vehicle routing problems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1520:417-431, 1998. Google Scholar
  19. Michele Surico, Uzay Kaymak, David Naso, and Rommert Dekker. Hybrid Meta-Heuristics for Robust Scheduling. ERIM Report Research in Management, 2006. Google Scholar
  20. Nihat Engin Toklu. Matheuristics for Robust Optimization - Application to Real-World Problems. PhD thesis, Università della Svizzera Italiana, 2014. Google Scholar
  21. Behnam Vahdani, Reza Tavakkoli-Moghaddam, Fariborz Jolai, and Arman Baboli. Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model. Engineering Optimization, 45(6):745-765, 2013. Google Scholar
  22. Gérard Verfaillie and Narendra Jussien. Constraint solving in uncertain and dynamic environments: A survey. Constraints, 10(3):253-281, 2005. Google Scholar
  23. Toby Walsh. Stochastic constraint programming. Proceedings of the 15th Eureopean Conference on Artificial Intelligence, pages 111-115, 2002. Google Scholar
  24. Huayue Wu. Randomization and Restart Strategies. PhD thesis, University of Waterloo, 2006. Google Scholar
  25. Weixiong Zhang. Branch-and-Bound Search Algorithms and Their Computational Complexity. Technical report, Information Sciences Institute, Marina del Rey, California, 1996. Google Scholar
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