Scenario-based Stochastic Constraint Programming

To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in iWalsh, 2002] along a number of important dimensions (e. g. to multiple chance constraints and to a range of new objectives). We also provide a new (but equivalent) semantics based on scenarios. Using this semantics, we can compile stochastic constraint programs down into conventional (nonstochastic) 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 [Hentenryck et a/. , 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as finance, agriculture and production.

Authors

• Suresh Manandhar
• Armagan Tarim
• Toby Walsh UNSW Sydney

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