Reliability redundancy allocation problem for butter oil processing machine performance analysis with hybrid grey wolf optimizer and cuckoo search

Abstract The reliability-redundancy allocation problem (RRAP) is playing an important role for complex system to improving reliability through component quality selection and component redundancy. In this article, a metaheuristic algorithm named hybrid grey wolf optimizer and cuckoo search (HGWOCS) is used for RRAP, which is a well-known nature-based technique for global optimization. The butter oil processing machine system (BOPM)is taken into consideration for RRAP with constraints on cost, weight, and volume. A non-linear mixed integer programming challenge is addressed to maximize reliability and availability of BOPM. Furthermore, applying the different five levels of redundancies results in the optimal redundancy allocation that maximizes BOPM reliability. Additionally, the statistical outcomes, Friedman ranking test, Wilcoxon test and convergence rate show that the proposed approaches provided an excellent performance. At last, to demonstrate the HGWOCS’s advantages, a comparative analysis is provided.