Adapted Markovian model to control reliability assessment in multiple AGV manufacturing system

This paper presents a markovian model for flexible manufacturing systems(fmss). the model considers two features of automated exible manufacturing systemsequipped with the automated guided vehicle (agv), namely the reliability of machinesand the reliability of agvs in a multiple agv jobshop manufacturing system. performancemeasure is a critical factor used to judge the eectiveness of a manufacturing system. thestudies in the literature did not compare markovian and neural networks, especially inthe reliability modeling of an advanced manufacturing system, considering agvs. thecurrent methods for modeling the reliability of a system involve determination of systemstate probabilities and transition states. since the failure of the machines and agvs couldbe considered in dierent states, a markovian model is proposed for reliability assessment.also, a neural network model is developed to point out the dierence in the accuracy of themarkovian model in comparison with the neural network. the optimization objectives inthe proposed model are maximizing the total reliability of machines in shops in the wholejobshop system and maximizing the total reliability of the agvs. the multi-objectivemathematical model is optimized using an analytic hierarchy process.