A novel general model for RAP and RRAP optimization of k-out-of-n:G systems with mixed redundancy strategy. (January 2023) - Zhang, Jinchun; Lv, Hang; Hou, Jinxiu
Highlights: A novel general model of k- out-of- n :G systems with mixed redundancy strategies based on continuous-time markov chains are proposed. Redundancy allocation problem and reliability–redundancy allocation problem are formulated and solved using a pseudo-parallel genetic algorithm. The proposed markov chain model is efficient for evaluating and optimization system reliability. Abstract: A novel general reliability model optimization model based on mixed redundancy strategy is proposed for k-out-of-n:G subsystems. In this model, the minimum required component for subsystem operation k is involved as a variable to adapt to general situations, and the redundancy strategy is considered to be a decision variable that any one of strategies (active, cold-standby or mixed) can be selected for each subsystem. To improve modeling accuracy and reduce complexity, the system reliability function is established by using continuous-time Markov chain. A pseudo-parallel genetic algorithm (PPGA) is designed for redundancy allocation problem (RAP) and reliability–redundancy allocation problem (RRAP) optimization. To verify the proposed model and the designed algorithm, numerical analysis for four typical benchmark test problems were performed. The comparisons of the proposed new model with the models under the choice of redundancy strategies based on lower bound formulation (LBF), as well as based on Markov method, for each benchmark problem show that the proposed model can provide higher reliability than previous models. The results of the algorithm performance comparison show that the designed PPGA can not only search for the optimal reliability for RAP and RRAP, but also has the advantage of fast convergence.