New approaches for optimizing standby redundant systems with fuzzy lifetimes

Standby redundancy is one of the most popular ways to optimize systems reliability with several applications in modern engineering. Under real-world conditions, the system lifetime can be modeled as a fuzzy interval due to parameter uncertainty and data inaccuracy. In this paper, several fuzzy standby redundancy optimization models are proposed utilizing three distinct types of decision criteria, namely as the expected value model, the chance-constrained and the dependent-chance programming. Thereafter, two solution processes are proposed. The first one utilizes a novel algorithm with lower computational complexity for a specific fuzzy objective which is embedded into intelligent optimization algorithms to derive a hybrid algorithm. While the other approach relies upon the transformation of the fuzzy model into its deterministic counterpart employing a family of fuzzy operational laws, then the solution derives in a straightforward manner using classical optimization algorithms or some well-developed software packages. Finally, several diverse numerical examples are considered for demonstrating the superiority of our treatment. It is shown that the herein developed solution approaches for the fuzzy standby redundancy optimization problems exhibit higher efficiency and satisfactory accuracy as compared with standard solution methods.