Identifying challenges and prospects for advancing evolutionary approaches in Reliability Redundancy Allocation Problem

Abstract

In most of the industries, reliability has been an important aspect that determines system efficiency and performance. In this thesis, we seek to identify the prevalent challenges in one of the sub-areas of reliability optimization, i.e., Reliability Redundancy Allocation Problem (RRAP). It focuses on optimizing system reliability while balancing constraints like cost, weight, and volume and finding ways to resolve it using some tested methods such as evolutionary approaches or computational Intelligence methods, demonstrating the accuracy in solving complex engineering problems and thus, addressing the growing complexity of modern systems. Bibliometric analysis of the related publications was captured to get the intrinsic insights from the publications and the current trend of RRAP. In recent times, researchers are deeply involved in using the evolutionary algorithm in solving the RRAP due to its complexity and ability to adapt to changing conditions while exploring a wider solution space to reduce risk. It eventually contributes to the development of more cost-effective systems and dependability. Several works on the issues relating to reliability, redundancy, and the combination of both have been critically accessed and identified in the thesis. The thesis explicitly offers invaluable insights for researchers and engineers in replicating more advanced sustainable systems in driving immense innovation in the reliability engineering field and discuss the comprehensive understanding of the trend and evolution landscape of RRAP research and possible future innovations in the field.