Hybrid metaheuristics for stochastic constraint programming

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Prestwich S. D., Tarim Ş. A., Rossi R., Hnich B.

CONSTRAINTS, vol.20, no.1, pp.57-76, 2015 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 20 Issue: 1
  • Publication Date: 2015
  • Doi Number: 10.1007/s10601-014-9170-x
  • Journal Name: CONSTRAINTS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.57-76
  • Open Archive Collection: AVESIS Open Access Collection
  • Hacettepe University Affiliated: Yes

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.