Fuzzy Reliability Redundancy Allocation Problem Using Multi-factorial Evolutionary Algorithm

The Reliability Redundancy Allocation Problem (RRAP) is generally classified as a NP-hard problem. The RRAP aims to achieve the system's optimal reliability considering the minimum number of redundant system components while keeping volume, weight, and cost in mind as the key constraints. However, the dynamic changes in the manufacturing process lead to uncertainty in these system parameters, which makes them the suitable fit for being formulated as a fuzzy quantity. There are previous studies that have modelled the parameters as fuzzy and solved various fuzzy RRAP as separate problems to cope with the uncertainty. However, owing to some similarities between the cases, such as fuzzy series and complex bridge systems, they can also be solved simultaneously. As a result, using the Multi-factorial Evolutionary Algorithm (MFEA) method, this paper proposes a technique for simultaneously solving two fuzzy RRAP cases: fuzzy complex (bridge) and fuzzy series system. The similar characteristics of these two systems facilitate the evolution process through implicit knowledge. The experiments' findings demonstrate that the developed methodology, as compared to other evolutionary approaches using a benchmark dataset, was able to solve these problems effectively.