Prestwich, S. D.Tarim, S. A.Rossi, R.Hnich, B.2023-06-162023-06-1620151383-71331572-9354https://doi.org/10.1007/s10601-014-9170-xhttps://hdl.handle.net/20.500.14365/910Stochastic 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.eninfo:eu-repo/semantics/openAccessStochastic constraint programmingMetaheuristicsFilteringFiltering AlgorithmsLocal SearchOptimizationArticle10.1007/s10601-014-9170-x2-s2.0-85027950224