Optimizing minimize periodic preventive maintenance model for series-parallel systems based on particle swarm optimization

This study minimizes the periodic preventive maintenance cost for a series-parallel system using the particle swam optimization (IPSO). The optimal maintenance periods for all components in the system are determined efficiently. The proposed IPSO considers maintainable properties of a seriesparallel system. The importance measure of components is utilized to evaluate the effects of components on system reliability when maintaining a component. Furthermore, an adjustment mechanism is developed to deal with the problem in which particles move into an infeasible area. A replacement mechanism is implemented that replaces the first n particles ranked in descending order of total maintenance cost with randomly generated particles in the feasible area. The purpose of doing so is to overcome the weakness in that a typical PSO is easily trapped in local solutions when optimizing a complex problem. Additionally, this study employs response surface methodology (RSM) via systematic parameters experiments to determine the optimal settings of IPSO parameters. Finally, a case demonstrates the effectiveness of the proposed IPSO.