Cost-Based Domain Filtering for Stochastic Constraint Programming

  • Roberto Rossi
  • S. Armagan Tarim
  • Brahim Hnich
  • Steven Prestwich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5202)


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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Roberto Rossi
    • 1
  • S. Armagan Tarim
    • 2
  • Brahim Hnich
    • 3
  • Steven Prestwich
    • 1
  1. 1.Cork Constraint Computation Centre - CTVRUniversity CollegeCorkIreland
  2. 2.Department of ManagementHacettepe UniversityAnkaraTurkey
  3. 3.Faculty of Computer ScienceIzmir University of EconomicsTurkey

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