LAUSR

Automated guided vehicles (AGVs) have become indispensable transportation tools in intelligent production workshops. The current AVG scheduling system has almost no processing capacity for temporary special cases and mostly depends on the path planning part to solve them, which can only reduce the cost waste caused to a certain extent. In this article, a dynamic AGV scheduling model is proposed, including an aperiodic departure method and a real-time task list update method. Compared with the static AGV scheduling model, the new model can reassign the AGVs for new tasks and special cases. A discrete invasive weed optimization (DIWO) algorithm with parameter adaptation and computing time adaptation is used to prove the effectiveness of the new model. The proposed model is verified by the cases from actual production workshops, which proves the effectiveness of the proposed dynamic AGV scheduling model for the special cases.