Cellular manufacturing systems are still quite popular among researchers, and they have proposed metaheuristics capable to solve such complex optimization models. In this study, machines are considered unreliable whose lifespan follows a Weibull distribution. The intra- and inter-cell movements of both parts and machines determined using batch sizes for transferring parts are related to the distances traveled through a rectilinear distance. The objectives of this study are to minimize the total cost of parts’ relocations and maximize the reliability of the processing routes due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent natureinspired algorithms, including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA), are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of the aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and, also, validated by the outputs of the exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the individual ones.
