Optimizing redundancy allocation problem with repairable components based on the Monte Carlo simulation

The optimization of reliability is crucial across various engineering domains. The redundancy allocation problem (RAP) is among the key challenges within reliability. This study introduces an RAP incorporating repairable components and a k-out-of-n sub-systems structure. The objective function aims to maximize system reliability while adhering to cost and weight constraints. The goal is to determine the optimal number of components for each subsystem, including the appropriate allocation of repairmen to each subsystem. Given that this model is classified as an Np-Hard problem, we employed a genetic algorithm (GA) to solve the proposed model. Additionally, response surface methodology (RSM) was utilized to fine-tune the algorithm parameters. To calculate the reliability of each subsystem, as well as the overall system reliability, a Monte Carlo simulation was employed. Lastly, a numerical example was solved to assess the algorithm's performance.

Keywords:

Reliability , Redundancy allocation problem , k-out-of-n sub-systems , Common Cause Failures , Genetic algorithm