Fuzzy multiobjective system reliability optimization by genetic algorithms and clustering analysis

System reliability optimization is a key element for a competitive and safe industrial plant. This paper addresses the multiobjective system reliability optimization in the presence of fuzzy data. A framework solution approach is proposed and based on four steps: defuzzify the data into crisp values by the ranking function procedure, the defuzzified problems are solved by the non-sorting genetic algorithms II and III (NSGA-II and NSGA-III), the Pareto fronts are compared by the spacing method for selecting the best one, and then the best Pareto front is reduced by the clustering analysis for helping the decision maker. A case study presented in the literature as a mono-objective redundancy allocation problem with fuzzy data is investigated in the present paper as multiobjective redundancy allocation and reliability-redundancy allocation problems show the applicability of the approach.