Abstract
In this article, we develop a computation algorithm to generate the optimal block replacement schedule which minimizes the expected cost per unit time in the steady-state. Since the renewal function of the inter-failure time distribution is involved in the expected cost representation, one has to evaluate the renewal function numerically and calculate the optimal preventive replacement schedule. Then, the radial basis function neural network (RBFNN) is applied to calculate the renewal function effectively. Finally, in numerical examples, we show that the RBFNN approach can generate the optimal block replacement schedule with higher precision.
