A novel component mixing and mixed redundancy strategy for reliability optimization

Maximizing overall system reliability by identifying optimal system configuration considering several design constraints is known as reliability redundancy allocation problem (RRAP). Since reliability is an important quality attribute in critical systems, RRAP has been intensively investigated in the literature. In this paper, a new model of RRAP for heterogeneous and homogeneous components is developed. Our proposed model handles component mixing in subsystems under both active and cold-standby redundancy strategies. The problem, therefore, is to decide the number of components in each subsystem (redundancy level), the failure rate of selected components, and the type of redundancy strategy for each of them under multiple design constraints including system weight, cost, and volume. Since RRAP falls into the NP-hard category of engineering optimization problems, a teaching learning-based optimization (TLBO) algorithm is implemented to solve it. Finally, the simulation results of the proposed RRAP model by TLBO on three well-known benchmark problems are provided, followed by the comparisons with recent existing related works. The comparative results suggested the effectiveness of the proposed approach in finding the optimal system configuration with higher system reliability in all cases.

Automated summary

This paper investigates the reliability redundancy allocation problem (RRAP) and proposes a new model to optimize system reliability by considering multiple design constraints. The study uses a teaching learning-based optimization algorithm to solve the NP-hard engineering problem and shows the effectiveness of the proposed approach in finding the optimal system configuration with higher system reliability in all cases.