Simultaneous planning method for number and allocation of AGVs in an AGV control system under uncertain transportation conditions

Nowadays, automated guided vehicle (AGV) systems are frequently employed in automated warehouses. Recently, a problem has emerged regarding the movement of AGVs under uncertain transportation conditions necessitated by the novel logistics required for connected industries and societies. In the present study, we attempt to develop a simultaneous planning method to determine the optimal number and dwell point of AGVs in an AGV transfer system, under uncertain transportation conditions, based on a genetic algorithm. We propose an algorithm that can determine the optimal number of AGVs as well as the dwell points for idle AGVs such that the mean response time is minimized and the amount of the work done by the AGVs is maximized, even when the transportation condition is uncertain. Moreover, we investigate the effectiveness of the proposed algorithm using numerical calculations and simulation experiments. The results show that the proposed algorithm performs better than previously used algorithms, in terms of the average matching time of products.