Availability prediction of the repairable equipment using artificial neural network, EWMA, AR, MA and ARMA models

Availability is considered one of the most important criteria in public services quality. In this study, this criterion is evaluated using artificial neural network (ANN). In addition, availability values for future periods are predicted using exponential weighted moving average (EWMA) scheme and several time series models (TSM) including autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA). Results obtained through the comparison of four methods based on ANN, considering several conditions for the effective parameters in ANN, show that the generalized regression method is the best method for predicting availability compared to other existing methods. Furthermore, results obtained from EWMA method and the three aforementioned TSMs demonstrate that MA model outperforms other models in predicting the availability values in future periods.