A NEURAL ALGORITHM FOR FINDING THE SHORTEST FLOW PATH FOR AN AUTOMATED GUIDED VEHICLE SYSTEM

The automated guided vehicle (AGV) system is emerging as the dominant technology to maximize the flexibility of material handling, while increasing the overall productivity of manufacturing operations. This paper presents a new way of finding the shortest flow path for an AGV system on a specific routing structure. An optimal solution of the system is determined by using an approach based on the Hopfield neural network with the simulated annealing (SA) procedure. In other words, the proposed approach reduces the total cost of an AGV delivery path from one workstation to another on the shop floor. By changing the temperature of the two-stage SA, a solution can be found that avoids potential collisions between AGVs. Both the flow path and the potential collision, which are major problems in AGV systems, may be solved simultaneously by the proposed neural network approach. Other advantages offered by the proposed method are its simplicity compared with operations research (OR) methods and a decreased number of needed AGVs. The performance of the approach is also investigated.