Two metaheuristics for solving the reliability redundancy allocation problem to maximize mean time to failure of a series–parallel system

The redundancy allocation problem is one of the main branches of reliability optimization problems. Traditionally, the redundancy allocation model has focused mainly on maximizing system reliability at a predetermined time. Hence, in this study, we develop a more realistic model, such that the mean time to failure of a system is maximized. To overcome the structural complexity of the model, the Monte Carlo simulation method is applied. Two metaheuristics, Simulated Annealing (SA) and Genetic Algorithm (GA), are proposed to solve the problem. In addition, the design of experiments and response surface methodology are employed for tuning the GA and SA parameters. The metaheuristics are compared, based on their computation time and accuracy, in 30 test problems. Finally, the results are analyzed and discussed, and some conclusions are drawn.