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Stochastic constraint programming: A scenario based approach

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JournalJournal of Constraints
DatePublished - Jan 2006
Issue number1
Volume11
Number of pages27
Pages (from-to)53-80
Original languageEnglish

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.

    Research areas

  • constraint programming, constraint satisfaction, reasoning under uncertainty, PORTFOLIO OPTIMIZATION

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