Rossi R.Tarim S.A.Hnich B.Prestwich S.2023-06-162023-06-162008354085957897835408595740302-9743https://doi.org/10.1007/978-3-540-85958-1_16https://hdl.handle.net/20.500.14365/3395Association of Constraint Programming;Cork Constraint Computation Centre;ILOG;National ICT Australia;University of New South Wales14th International Conference on Principles and Practice of Constraint Programming, CP 2008 -- 14 September 2008 through 18 September 2008 -- Sydney, NSW -- 74255Cost-based filtering is a novel approach that combines techniques from Operations Research and Constraint Programming to filter from decision variable domains values that do not lead to better solutions [7]. Stochastic Constraint Programming is a framework for modeling combinatorial optimization problems that involve uncertainty [9]. In this work, we show how to perform cost-based filtering for certain classes of stochastic constraint programs. Our approach is based on a set of known inequalities borrowed from Stochastic Programming - a branch of OR concerned with modeling and solving problems involving uncertainty. We discuss bound generation and cost-based domain filtering procedures for a well-known problem in the Stochastic Programming literature, the static stochastic knapsack problem. We also apply our technique to a stochastic sequencing problem. Our results clearly show the value of the proposed approach over a pure scenario-based Stochastic Constraint Programming formulation both in terms of explored nodes and run times. © 2008 Springer-Verlag Berlin Heidelberg.eninfo:eu-repo/semantics/openAccessCombinatorial mathematicsCombinatorial optimizationComputer programmingConstrained optimizationConstraint theoryCostsInteger programmingSignal interferenceStochastic programmingCombinatorial optimization problemsConstraint programmingsDecision variablesDomain filteringRun timesSequencing problemsStochastic constraintsStochastic knapsack problemsProblem solvingConference Object10.1007/978-3-540-85958-1_162-s2.0-56449097573