Stochastic Modeling of an Automated Guided Vehicle System With One Vehicle and a Closed-Loop Path

The use of automated guided vehicles (AGVs) in material-handling processes of manufacturing facilities and warehouses is becoming increasingly common. A critical drawback of an AGV is its prohibitively high cost. Cost considerations dictate an economic design of AGV systems. This paper presents an analytical model that uses a Markov chain approximation approach to evaluate the performance of the system with respect to costs and the risk associated with it. This model also allows the analytic optimization of the capacity of an AGV in a closed-loop multimachine stochastic system. We present numerical results with the Markov chain model which indicate that our model produces results comparable to a simulation model, but does so in a fraction of the computational time needed by the latter. This advantage of the analytical model becomes more pronounced in the context of optimization of the AGV´s capacity which without an analytical approach would require numerous simulation runs at each point in the capacity space.