A multi-objective decision making on reliability growth planning for in-service systems

New designs prioritized for the fast time-to-market usually can not carry out sufficient in-house reliability growth testing due to the stringent delivery deadline. Reliability improvement for those systems can be achieved by implementing corrective actions (CAs) on in-service systems. In this paper, three types of effectiveness functions are proposed to measure the reduction rate of failure modes given different CA resources. Integrated with the effectiveness function, a new failure intensity model is proposed for predicting the mean-time-between-failures (MTBF) of field systems. Finally, a multi-objective optimization model is formulated to maximize the system reliability and to minimize the reliability uncertainty with the constraint of the CA resources. Genetic algorithms combined with greedy heuristic are applied to search the optimal CA decisions that lead to the maximum reliability growth while minimizing the reliability uncertainty. Results show that the proposed reliability growth program can effectively guide decision-makers to find the most effective corrective actions for achieving the reliability goal for a large fleet of in-service systems. Throughout paper, systems and products will be used interchangeably.