@inbook{8e85aef1483340309c9f6ba900cd6096,

title = "Cost-Based Domain Filtering for Stochastic Constraint Programming",

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 [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",

author = "R. Rossi and S.A. Tarim and B. Hnich and S. Prestwich",

year = "2008",

doi = "10.1007/978-3-540-85958-1_16",

language = "English",

isbn = "9783540859574",

series = "Lecture Notes in Computer Science",

publisher = "Springer Berlin/Heidelberg",

number = "5202",

pages = "235--250",

editor = "P.J. Stuckey",

booktitle = "Principles and Practice of Constraint Programming",

}