Scenario-based Stochastic Constraint Programming

TL;DR

This paper extends stochastic constraint programming with new dimensions and semantics, enabling the compilation of stochastic models into conventional constraint programs for improved decision-making under uncertainty.

Contribution

It introduces an extended framework with multiple chance constraints, new objectives, and a scenario-based semantics, implemented in stochastic OPL for diverse real-world applications.

Findings

Framework supports complex stochastic decision problems

Models successfully applied in finance, agriculture, and production

Enables use of existing constraint solvers for stochastic problems

Abstract

To model combinatorial decision problems involving uncertainty and probability, we extend the stochastic constraint programming framework proposed in [Walsh, 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 al., 1999]. To illustrate the potential of this framework, we model a wide range of problems in areas as diverse as finance, agriculture and production.