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

Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohamma

doi:10.22094/joie.2016.190

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

Document Type : Original Manuscript

Authors

Iran University of Science and Technology

10.22094/joie.2016.190

Abstract

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.

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