, Volume 11, Issue 1, pp 53–80 | Cite as

Stochastic Constraint Programming: A Scenario-Based Approach

  • S. Armagan Tarim
  • Suresh Manandhar
  • Toby Walsh


To model combinatorial decision problems involving uncertainty and probability, we introduce scenario based stochastic constraint programming. Stochastic constraint programs contain both decision variables, which we can set, and stochastic variables, which follow a discrete probability distribution. We provide a semantics for stochastic constraint programs based on scenario trees. Using this semantics, we can compile stochastic constraint programs down into conventional (non-stochastic) constraint programs. This allows us to exploit the full power of existing constraint solvers. We have implemented this framework for decision making under uncertainty in stochastic OPL, a language which is based on the OPL constraint modelling language [Van Hentenryck et al., 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as portfolio diversification, agricultural planning and production/inventory management.


Constraint programming Constraint satisfaction Reasoning under uncertainty 


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Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  • S. Armagan Tarim
    • 1
  • Suresh Manandhar
    • 2
  • Toby Walsh
    • 3
  1. 1.Cork Constraint Computation Centre, Department of Computer ScienceUniversity College CorkCorkIreland
  2. 2.Artificial Intelligence Group, Department of Computer ScienceUniversity of YorkYorkUK
  3. 3.National ICT Australia and School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia

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