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, therefore 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.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, therefore a markovian model is proposed for reliability assessment.