Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment: A fuzzy multi-criteria genetic algorithm approach

Reliability is central to productivity and effectiveness of real world industrial systems [22, 35]. To maximize productivity, the systems should always be available. However, it is difficult for an industrial system, comprising several complex components to survive over time since its reliability directly depends on the characteristics of its components. Failure is inevitable, such that system reliability optimization has become a key subject area in industry. Developing effective system reliability optimization is imperative. Two approaches for system reliability improvement are: (i) using redundant elements in subsystems, and (ii) increasing the reliability of system components. Reliability-redundancy allocation maximizes system reliability via redundancy and component reliability choices [23], with restrictions on cost, weight, and volume of the resources. The aim is to find a trade-off between reliability and other resource constraints [22]. Thus, for a highly reliability system, the main problem is to balance reliability enhancement and resource consumption. A number of approaches in the literature focus on the application of metaheuristic methods for solving system reliability optimization problems [9, 7, 27, 15, 33, 34, 10, 13]. However, real-life reliability optimization problems are complex:

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