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 [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 f...

• Type: Conference
• Year: 2008
• Where: CP
• Authors: Roberto Rossi, Armagan Tarim, Brahim Hnich, Steven David Prestwich
• Keywords: Artificial Intelligence, CP 2008, Stochastic, Stochastic Constraint, Stochastic Constraint Programming