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Constraints

, Volume 20, Issue 1, pp 57–76 | Cite as

Hybrid metaheuristics for stochastic constraint programming

  • S. D. PrestwichEmail author
  • S. A. Tarim
  • R. Rossi
  • B. Hnich
Article

Abstract

Stochastic Constraint Programming (SCP) is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. This paper proposes a metaheuristic approach to SCP that can scale up to large problems better than state-of-the-art complete methods, and exploits standard filtering algorithms to handle hard constraints more efficiently. For problems with many scenarios it can be combined with scenario reduction and sampling methods.

Keywords

Stochastic constraint programming Metaheuristics Filtering 

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • S. D. Prestwich
    • 1
    Email author
  • S. A. Tarim
    • 2
  • R. Rossi
    • 3
  • B. Hnich
    • 4
  1. 1.Insight Centre for Data AnalyticsUniversity College CorkCorkIreland
  2. 2.Institute of Population StudiesHacettepe UniversityAnkaraTurkey
  3. 3.University of Edinburgh Business SchoolEdinburghUK
  4. 4.Department of Computer EngineeringIzmir University of EconomicsIzmirTurkey

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