Many industrial sectors are concerned on developing optimal maintenance planning because of the importance of maintenance on the economy and safety. Traditionally, the maintenance planning is formulated in terms of a multi-objective optimization problem where reliability, availability, maintainability and cost act as decision criteria and surveillance tests and maintenance strategies act as decision variables. However, the appropriate development of each maintenance strategy depends not only on the maintenance intervals but also on the resources available to implement such strategies. To solve the multi-objective optimization problem Particle Swarm Optimization (PSO) can be used. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking or fish schooling. In this paper the multi-objective optimization of the maintenance of a nuclear power plant safety equipment using PSO is presented.
['Sofía Carlos', 'Ana Sánchez', 'Sebastián Martorell']
Model to study the effect of workforce on a safety equipment and its optimization