Abstract
This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted.
•Integrated Markovian and back propagation neural network approach to compute reliability.•Markovian based reliability assessment method.•Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks.