Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm

Abstract

Modular redundancy plays a significant role for designing a reliable system. This study examines the optimization of system reliability in a modular redundancy allocation problem in crisp and intuitionistic fuzzy atmospheres with the goal of maximizing total system reliability while adhering to resource restrictions. On applying modular technique on a redundancy allocation problem, one can increase the fault tolerance to the optimum design of the system, making it very effective in terms of component redundancy. So, modular redundancy should be seen as a proper replacement for the old technique of component level redundancy for improving the reliability, efficiency, and maintainability of a working system. The multi-level redundancy allocation problem is being addressed and solved comprehensively in this study using an advanced genetic algorithm (GA) and a penalty function approach in both crisp and intuitionistic fuzzy settings Finally, numerical examples are solved and sensitivity studies are carried out visually to evaluate the consequences of changing key parameters involved in GA.