Complex System Reliability Analysis and Its Optimization

Abstract

This global era has made the applicability of the term reliability in almost every field as well as greatly impacted to all fields of social utility in terms of system designing and optimum output with available resources. This has paved the way for a great concern of improving the reliability of a system. With increasing demands of the customers due to the ongoing advancements in every field of society, there has arisen a need of not only longer life of the systems but optimum quality while in use. This very fact has demanded the attention of the researchers to look upon the different aspects of a complex system and its components and with varied process improvements concerning weight, cost, and number of required components in the successful running of a system for quality and long-lasting benefits. newlineReliability measures have been a central point for the decision makers to decide the quality and efficiency of complex systems. Reliability measures have been estimated by different methods. The first contribution of this work, is analysis of a stochastic model of a complex system under consideration of human failure along with component failures and rework policy. While the second contribution, discusses a k-out-of-n: F systems with the attention of human failure and rework policy. The mathematical models are formulated using Markov process and supplementary variable technique, and solved by Laplace transform. Reliability measures and their sensitivity have been examined for both the models and present the comparative study of models with and without human error. Various nature-inspired optimization algorithms have been very effective in giving good results. The third contribution, is about a hybrid of particle swarm optimization and grey wolf optimization (HPSOGWO) algorithm has been employed to optimize the reliability of complex bridge system (CBS) and life support system (LSS) in a Space Capsule and found that HPSOGWO gives better results as compare to PSO and GWO.