Fazlollahtabar, H., Saidi-Mehrabad, M. (2016). Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System. Journal of Optimization in Industrial Engineering, 9(19), 75-86. doi: 10.22094/joie.2016.190
Hamed Fazlollahtabar; Mohamma Saidi-Mehrabad. "Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System". Journal of Optimization in Industrial Engineering, 9, 19, 2016, 75-86. doi: 10.22094/joie.2016.190
Fazlollahtabar, H., Saidi-Mehrabad, M. (2016). 'Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System', Journal of Optimization in Industrial Engineering, 9(19), pp. 75-86. doi: 10.22094/joie.2016.190
Fazlollahtabar, H., Saidi-Mehrabad, M. Monte Carlo Simulation to Compare Markovian and Neural Network Models for Reliability Assessment in Multiple AGV Manufacturing System. Journal of Optimization in Industrial Engineering, 2016; 9(19): 75-86. doi: 10.22094/joie.2016.190
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.
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