A Fuzzy Multi-Objective Optimization for Series-Parallel Redundancy Allocation Problem Considering Mixed Redundancy Strategy

This study explores the problem of allocating reliability redundancy with multiple objectives in a series-parallel system, emphasizing two primary objectives: enhancing the total reliability and minimizing the associated design costs. Precisely calculating component reliability is challenging due to the influence of various factors. To address this imprecision, interval-valued fuzzy numbers are used to model parameters, capturing the inherent uncertainty. A new fuzzy multi-objective optimization problem is designed to effectively address the inherent uncertainties associated with the issue at hand. Subsequently, a defuzzification technique is utilized, thereby facilitating a more straightforward analysis and solution process. Furthermore, to enhance system reliability, we employ a mixed redundancy strategy that incorporates active and standby redundancy strategies, enabling various levels of active and standby components to operate concurrently within each subsystem. An effective multi-objective evolutionary algorithm, NSGA-II, is applied to tackle this problem. To showcase the benefits of the proposed multi-objective model and algorithm, we address test problems that highlight their effectiveness. The developed method has the ability to identify a collection of optimal solutions known as the Pareto fronts. After finding the Pareto fronts, the [Formula: see text] norm technique has been applied to select the best solution. Finally, the effectiveness of the proposed approach is shown in a real case study.