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Simulation of dynamic complex systems-specifically, those comprised of large numbers of components with stochastic behaviors-for the purpose of probabilistic risk assessment faces challenges in every aspect of the problem. Scenario generation confronts many impediments, one being the problem of handling the large number of scenarios without compromising completeness. Probability estimation and consequence determination processes must also be performed under real world constraints on time and resources. In the approach outlined in this paper, hierarchical planning is utilized to generate a relatively small but complete group of risk scenarios to represent the unsafe behaviors of the system. Multi-level scheduling makes the probability estimation and consequence determination processes more efficient and affordable. The scenario generation and scheduling processes both benefit from an updating process that takes place after a number of simulation runs by fine-tuning the scheduler's level adjustment parameters and refining the planner's high level system model.