Stochastic programming via scenario planning for system reliability optimization

In many engineering systems, it is not possible to determine the reliability of components exactly. Two following sources causing the reliability of components are deterministic and inaccurate: I Uncertainty in the quality of the components (confidence in parameter estimation, low accuracy of expert opinions and related standard) n. Variations in working conditions (uncertainty in the time of operation, working conditions, environmental stress, temperature and workload). This paper presents an efficient methodology which is developed for modeling redundancy allocation problem considering two types of variation in reliability data by stochastic programming and risk model. In this model, scenario planning is used for modeling variations in working conditions and risk model is presented for considering uncertainty of components reliability. The model is a Multi-objective reliability problems in series-parallel systems with the choice of redundancy which is solved by genetic algorithm.