Hybrid metaheuristics for stochastic constraint programming


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

CONSTRAINTS, vol.20, no.1, pp.57-76, 2015 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 1
  • Publication Date: 2015
  • Doi Number: 10.1007/s10601-014-9170-x
  • Title of Journal : CONSTRAINTS
  • Page Numbers: pp.57-76

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.