Adapted Markovian Model to Control Reliability Assessment in Multiple AGV

This paper presents a Markovian model for flexible manufacturing systems (FMSs). The model 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. Performance measure is a critical factor used to judge the effectiveness of a manufacturing system. The studies in the literature did notcompare Markovian and neural networks especially in the reliability modeling of an advanced manufacturing system considering AGVs. 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. Also, a neural network model is developed to point out the difference in the accuracy of the Markovian model in comparison with the neural network. The optimization objectives in the proposed model are maximizing the total reliability of machines in shops in the whole jobshop system and maximizing the total reliability of the AGVs. The multi-objective mathematical model is optimized using an analytic hierarchy process.