Cost-Based Domain Filtering for Stochastic Constraint Programming

  • Authors:

  • Roberto Rossi;S. Armagan Tarim;Brahim Hnich;Steven Prestwich

  • Affiliations:

  • Cork Constraint Computation Centre - CTVR, University College, Cork, Ireland;Department of Management, Hacettepe University, Ankara, Turkey;Faculty of Computer Science, Izmir University of Economics, Turkey;Cork Constraint Computation Centre - CTVR, University College, Cork, Ireland

  • Venue:
  • CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
  • Year:
  • 2008

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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.