@inbook{a0a156e3d7d5455380f735e54aaa90e3,

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. Prestwich and S.A. Tarim and B. Hnich",

year = "2008",

month = sep,

day = "22",

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

language = "English",

isbn = "978-3-540-85957-4",

volume = "5202 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag GmbH",

pages = "235--250",

editor = "Stuckey, {Peter J. }",

booktitle = "Principles and Practice of Constraint Programming",

}