Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System

We compare two approaches for a markovian model in flexible manufacturing systems (fmss) using monte carlo simulation. the model, which is a development of fazlollahtabar and saidi-mehrabad (2013), considers two features of automated flexible manufacturing systems equipped with automated guided vehicle (agv), namely, the reliability of machines and the reliability of agvs in a multiple agv jobshop manufacturing system. the current methods for modeling reliability of a system involve determination of system state probabilities and transition states. since the failure of the machines and agvs could be considered in different states, a markovian model is proposed for reliability assessment. the traditional markovian computation is compared with a neural network methodology. monte carlo simulation has verified the neural network method having better performance for markovian computations.