Reliability optimization in generalized stochastic-flow networks

A model and an algorithm for reliability optimization in generalized stochastic-flow networks are given. This algorithm uses the relationship of the k-weak-link sets and sets of failure events to the parameters of the generalized stochastic flow networks. This facilitates a fast location of the optimal capacity expansion for a system from the information obtained by the latest iteration and alleviates the dimension calamity on computation. Consequently, this reliability optimization method is powerful for large systems. As an example, the IEEE Reliability Test System with 24 nodes and 70 components was tested, revealing the components and their capacity values which should be enhanced. The computation results show that the algorithm can be applied to practical problems