Stochastic Constraint Programming: A Scenario-Based Approach

Authors:
S. Armagan Tarim;Suresh Manandhar;Toby Walsh
Affiliations:
Cork Constraint Computation Centre, Department of Computer Science, University College Cork, Cork, Ireland;Artificial Intelligence Group, Department of Computer Science, University of York, York, UK;National ICT Australia and School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Venue:
Constraints
Year:
2006

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Abstract

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.