A Particle Swarm Optimization (PSO) approach for non-periodic preventive maintenance scheduling programming

This work presents a Particle Swarm Optimization (PSO) approach for non-periodic preventive maintenance scheduling optimization. The probabilistic model, which is focused on reliability and cost evaluation, is developed in such a way that flexible intervals between maintenance interventions are allowed. Due to the fact that PSO is typically skilled for real-coded continuous spaces, with fixed dimension (number of search parameters), a non-straightforward codification for solution candidates has been developed in order to allow PSO to deal with variable number of maintenance interventions. To evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop Pressurized Water Reactor (PWR) has been considered. The optimization problem consists in maximizing the system’s average availability for a given period of time, considering realistic features such as: i) the probability of needing a repair (corrective maintenance), ii) the cost of such repair, iii) typical outage times, iv) preventive maintenance costs, v) the impact of the maintenance in the systems reliability as a whole, vi) probability of imperfect maintenance, etc. Obtained results demonstrated good capability of proposed PSO approach for automatic expert knowledge acquisition (without any a priori information), which allowed it to find optimal solutions.