Risk-Based, genetic algorithm approach to optimize outage maintenance schedule

Abstract A huge number of components are typically scheduled for maintenance when a nuclear power plant is shut down for its planned outage. Among these components, a number of them are risk significant so that their operability as well as reliability is of prime concern. Lack of proper maintenance for such components during the outage would impose substantial risk on the nuclear power plant (NPP) operation. In this paper, a new approach based on genetic algorithm (GA) is presented for the optimization of the NPP maintenance schedule during plant outage/overhaul, and an optimizer is developed accordingly. The developed optimizer, coupled with the suggested risk-cost model, compromises the cost in favor of maintaining the risk imposed by each schedule below regulatory/industry set limits. The suggested cost model consists of two elements, one considering the cost incurred by maintenance activities and the other incorporating the loss of revenues if needed, but unscheduled component maintenance causes further plant shutdown. The optimizer is developed in such a way that any risk and/or cost models the user desires can be applied. The performance of the developed GA/optimizer is evaluated by comparing its predictions with Monte Carlo simulation results. It is shown that the GA/optimizer performs significantly better.