基于场景的随机约束规划
Scenario-based Stochastic Constraint Programming
摘要：
为了对涉及不确定性和概率的组合决策问题进行建模，我们沿着一些重要的维度（例如，多个机会约束和一系列新目标）扩展了[Walsh，2002]中提出的随机约束规划框架。我们还提供了一个新的（但等效的）语义场景的基础上。使用这种语义，我们可以编译随机约束程序到传统的（非随机）约束程序。这使我们能够充分利用现有的约束求解器。我们已经在随机OPL中实现了这个框架，用于在不确定性下进行决策，OPL是一种基于OPL约束建模语言的语言[Hentenryck等人，1999年]。为了说明这一框架的潜力，我们模拟了金融、农业和生产等领域的各种问题。
英文摘要：
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.
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