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Stochastic Constraint Programming (SCP) is an extension of Constraint Programming for modelling and solving combinatorial problems involving uncertainty. This paper makes three contributions to the field. Firstly we propose a metaheuristic approach to SCP that can scale up to large problems better than state-of-the-art complete methods. Secondly we show how to use standard filtering algorithms to handle hard constraints more efficiently during search. Thirdly we extend our approach to problemsdoi:10.1007/s10601-014-9170-x fatcat:cqv5c44wojgq5nwkzrt2g7ebu4