Abstract. Cost-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 [19] . 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.
Another related work is REF , which provides cost-based domain filtering for stochastic knapsack constraints, indicating some relationship between chance-constraints and cost/soft-constraints.
Cost-based domain filtering for stochastic constraint programming
In Proceedings of the 14th International Conference on the Principles and Practice of Constraint Programming
Computer Science Mathematics